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Kannarkat GT, Zack R, Skrinak RT, Morley JF, Davila-Rivera R, Arezoumandan S, Dorfmann K, Luk K, Wolk DA, Weintraub D, Tropea TF, Lee EB, Xie SX, Chandrasekaran G, Lee VMY, Irwin D, Akhtar RS, Chen-Plotkin AS. α-Synuclein Conformations in Plasma Distinguish Parkinson's Disease from Dementia with Lewy Bodies. bioRxiv 2024:2024.05.07.593056. [PMID: 38765963 PMCID: PMC11100683 DOI: 10.1101/2024.05.07.593056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spread and aggregation of misfolded α-synuclein (aSyn) within the brain is the pathologic hallmark of Lewy body diseases (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). While evidence exists for multiple aSyn protein conformations, often termed "strains" for their distinct biological properties, it is unclear whether PD and DLB result from aSyn strain differences, and biomarkers that differentiate PD and DLB are lacking. Moreover, while pathological forms of aSyn have been detected outside the brain ( e.g., in skin, gut, blood), the functional significance of these peripheral aSyn species is unclear. Here, we developed assays using monoclonal antibodies selective for two different aSyn species generated in vitro - termed Strain A and Strain B - and used them to evaluate human brain tissue, cerebrospinal fluid (CSF), and plasma, through immunohistochemistry, enzyme-linked immunoassay, and immunoblotting. Surprisingly, we found that plasma aSyn species detected by these antibodies differentiated individuals with PD vs. DLB in a discovery cohort (UPenn, n=235, AUC 0.83) and a multi-site replication cohort (Parkinson's Disease Biomarker Program, or PDBP, n=200, AUC 0.72). aSyn plasma species detected by the Strain A antibody also predicted rate of cognitive decline in PD. We found no evidence for aSyn strains in CSF, and ability to template aSyn fibrillization differed for species isolated from plasma vs. brain, and in PD vs. DLB. Taken together, our findings suggest that aSyn conformational differences may impact clinical presentation and cortical spread of pathological aSyn. Moreover, the enrichment of these aSyn strains in plasma implicates a non-central nervous system source.
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Cousins KAQ, Phillips JS, Das SR, O'Brien K, Tropea TF, Chen-Plotkin A, Shaw LM, Nasrallah IM, Mechanic-Hamilton D, McMillan CT, Irwin DJ, Lee EB, Wolk DA. Pathologic and cognitive correlates of plasma biomarkers in neurodegenerative disease. Alzheimers Dement 2024. [PMID: 38644682 DOI: 10.1002/alz.13777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 04/23/2024]
Abstract
INTRODUCTION We investigate pathological correlates of plasma phosphorylated tau 181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) across a clinically diverse spectrum of neurodegenerative disease, including normal cognition (NormCog) and impaired cognition (ImpCog). METHODS Participants were NormCog (n = 132) and ImpCog (n = 461), with confirmed β-amyloid (Aβ+/-) status (cerebrospinal fluid, positron emission tomography, autopsy) and single molecule array plasma measurements. Logistic regression and receiver operating characteristic (ROC) area under the curve (AUC) tested how combining plasma analytes discriminated Aβ+ from Aβ-. Survival analyses tested time to clinical dementia rating (global CDR) progression. RESULTS Multivariable models (p-tau+GFAP+NfL) had the best performance to detect Aβ+ in NormCog (ROCAUC = 0.87) and ImpCog (ROCAUC = 0.87). Survival analyses demonstrated that higher NfL best predicted faster CDR progression for both Aβ+ (hazard ratio [HR] = 2.94; p = 8.1e-06) and Aβ- individuals (HR = 3.11; p = 2.6e-09). DISCUSSION Combining plasma biomarkers can optimize detection of Alzheimer's disease (AD) pathology across cognitively normal and clinically diverse neurodegenerative disease. HIGHLIGHTS Participants were clinically heterogeneous, with autopsy- or biomarker-confirmed Aβ. Combining plasma p-tau181, GFAP, and NfL improved diagnostic accuracy for Aβ status. Diagnosis by plasma biomarkers is more accurate in amnestic AD than nonamnestic AD. Plasma analytes show independent associations with tau PET and post mortem Aβ/tau. Plasma NfL predicted longitudinal cognitive decline in both Aβ+ and Aβ- individuals.
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Affiliation(s)
- Katheryn A Q Cousins
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jeffrey S Phillips
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sandhitsu R Das
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyra O'Brien
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Thomas F Tropea
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Alice Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Dawn Mechanic-Hamilton
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Corey T McMillan
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Katsumi Y, Howe IA, Eckbo R, Wong B, Quimby M, Hochberg D, McGinnis SM, Putcha D, Wolk DA, Touroutoglou A, Dickerson BC. Default mode network tau predicts future clinical decline in atypical early Alzheimer's disease. medRxiv 2024:2024.04.17.24305620. [PMID: 38699357 PMCID: PMC11065041 DOI: 10.1101/2024.04.17.24305620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
Identifying individuals with early stage Alzheimer's disease (AD) at greater risk of steeper clinical decline would allow professionals and loved ones to make better-informed medical, support, and life planning decisions. Despite accumulating evidence on the clinical prognostic value of tau PET in typical late-onset amnestic AD, its utility in predicting clinical decline in individuals with atypical forms of AD remains unclear. In this study, we examined the relationship between baseline tau PET signal and the rate of subsequent clinical decline in a sample of 48 A + /T + /N + patients with mild cognitive impairment or mild dementia due to AD with atypical clinical phenotypes (Posterior Cortical Atrophy, logopenic variant Primary Progressive Aphasia, and amnestic syndrome with multi-domain impairment and age of onset < 65 years). All patients underwent structural magnetic resonance imaging (MRI), tau ( 18 F-Flortaucipir) PET, and amyloid (either 18 F-Florbetaben or 11 C-Pittsburgh Compound B) PET scans at baseline. Each patient's longitudinal clinical decline was assessed by calculating the annualized change in the Clinical Dementia Rating Sum-of-Boxes (CDR-SB) scores from baseline to follow-up (mean time interval = 14.55 ± 3.97 months). Our sample of early atypical AD patients showed an increase in CDR-SB by 1.18 ± 1.25 points per year: t (47) = 6.56, p < .001, d = 0.95. These AD patients showed prominent baseline tau burden in posterior cortical regions including the major nodes of the default mode network, including the angular gyrus, posterior cingulate cortex/precuneus, and lateral temporal cortex. Greater baseline tau in the broader default mode network predicted faster clinical decline. Tau in the default mode network was the strongest predictor of clinical decline, outperforming baseline clinical impairment, tau in other functional networks, and the magnitude of cortical atrophy and amyloid burden in the default mode network. Overall, these findings point to the contribution of baseline tau burden within the default mode network of the cerebral cortex to predicting the magnitude of clinical decline in a sample of atypical early AD patients one year later. This simple measure based on a tau PET scan could aid the development of a personalized prognostic, monitoring, and treatment plan tailored to each individual patient, which would help clinicians not only predict the natural evolution of the disease but also estimate the effect of disease-modifying therapies on slowing subsequent clinical decline given the patient's tau burden while still early in the disease course.
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Ohm DT, Xie SX, Capp N, Arezoumandan S, Cousins KAQ, Rascovsky K, Wolk DA, Van Deerlin VM, Lee EB, McMillan CT, Irwin DJ. Cytoarchitectonic gradients of laminar degeneration in behavioral variant frontotemporal dementia. bioRxiv 2024:2024.04.05.588259. [PMID: 38644997 PMCID: PMC11030243 DOI: 10.1101/2024.04.05.588259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Behavioral variant frontotemporal dementia (bvFTD) is a clinical syndrome primarily caused by either tau (bvFTD-tau) or TDP-43 (bvFTD-TDP) proteinopathies. We previously found lower cortical layers and dorsolateral regions accumulate greater tau than TDP-43 pathology; however, patterns of laminar neurodegeneration across diverse cytoarchitecture in bvFTD is understudied. We hypothesized that bvFTD-tau and bvFTD-TDP have distinct laminar distributions of pyramidal neurodegeneration along cortical gradients, a topologic order of cytoarchitectonic subregions based on increasing pyramidal density and laminar differentiation. Here, we tested this hypothesis in a frontal cortical gradient consisting of five cytoarchitectonic types (i.e., periallocortex, agranular mesocortex, dysgranular mesocortex, eulaminate-I isocortex, eulaminate-II isocortex) spanning anterior cingulate, paracingulate, orbitofrontal, and mid-frontal gyri in bvFTD-tau (n=27), bvFTD-TDP (n=47), and healthy controls (HC; n=32). We immunostained all tissue for total neurons (NeuN; neuronal-nuclear protein) and pyramidal neurons (SMI32; non-phosphorylated neurofilament) and digitally quantified NeuN-immunoreactivity (ir) and SMI32-ir in supragranular II-III, infragranular V-VI, and all I-VI layers in each cytoarchitectonic type. We used linear mixed-effects models adjusted for demographic and biologic variables to compare SMI32-ir between groups and examine relationships with the cortical gradient, long-range pathways, and clinical symptoms. We found regional and laminar distributions of SMI32-ir expected for HC, validating our measures within the cortical gradient framework. While SMI32-ir loss was not related to the cortical gradient in bvFTD-TDP, SMI32-ir progressively decreased along the cortical gradient of bvFTD-tau and included greater SMI32-ir loss in supragranular eulaminate-II isocortex in bvFTD-tau vs bvFTD-TDP ( p =0.039). In a structural model for long-range laminar connectivity between infragranular mesocortex and supragranular isocortex, we found a larger laminar ratio of mesocortex-to-isocortex SMI32-ir in bvFTD-tau vs bvFTD-TDP ( p =0.019), suggesting select long-projecting pathways may contribute to isocortical-predominant degeneration in bvFTD-tau. In cytoarchitectonic types with the highest NeuN-ir, we found lower SMI32-ir in bvFTD-tau vs bvFTD-TDP ( p =0.047), suggesting pyramidal neurodegeneration may occur earlier in bvFTD-tau. Lastly, we found that reduced SMI32-ir related to behavioral severity and frontal-mediated letter fluency, not temporal-mediated confrontation naming, demonstrating the clinical relevance and specificity of frontal pyramidal neurodegeneration to bvFTD-related symptoms. Our data suggest loss of neurofilament-rich pyramidal neurons is a clinically relevant feature of bvFTD that selectively worsens along a frontal cortical gradient in bvFTD-tau, not bvFTD-TDP. Therefore, tau-mediated degeneration may preferentially involve pyramidal-rich layers that connect more distant cytoarchitectonic types. Moreover, the hierarchical arrangement of cytoarchitecture along cortical gradients may be an important neuroanatomical framework for identifying which types of cells and pathways are differentially involved between proteinopathies.
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Kim B, Yannatos I, Blam K, Wiebe D, Xie SX, McMillan CT, Mechanic‐Hamilton D, Wolk DA, Lee EB. Neighborhood disadvantage reduces cognitive reserve independent of neuropathologic change. Alzheimers Dement 2024; 20:2707-2718. [PMID: 38400524 PMCID: PMC11032541 DOI: 10.1002/alz.13736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/12/2024] [Accepted: 01/16/2024] [Indexed: 02/25/2024]
Abstract
INTRODUCTION Individuals in socioeconomically disadvantaged neighborhoods exhibit increased risk for impaired cognitive function. Whether this association relates to the major dementia-related neuropathologies is unknown. METHODS This cross-sectional study included 469 autopsy cases from 2011 to 2023. The relationships between neighborhood disadvantage measured by Area Deprivation Index (ADI) percentiles categorized into tertiles, cognition evaluated by the last Mini-Mental State Examination (MMSE) scores before death, and 10 dementia-associated proteinopathies and cerebrovascular disease were assessed using regression analyses. RESULTS Higher ADI was significantly associated with lower MMSE score. This was mitigated by increasing years of education. ADI was not associated with an increase in dementia-associated neuropathologic change. Moreover, the significant association between ADI and cognition remained even after controlling for changes in major dementia-associated proteinopathies or cerebrovascular disease. DISCUSSION Neighborhood disadvantage appears to be associated with decreased cognitive reserve. This association is modified by education but is independent of the major dementia-associated neuropathologies.
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Affiliation(s)
- Boram Kim
- Translational Neuropathology Research LaboratoryDepartment of Pathology and Laboratory MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Isabel Yannatos
- Penn Frontotemporal Degeneration CenterDepartment of NeurologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kaitlin Blam
- Translational Neuropathology Research LaboratoryDepartment of Pathology and Laboratory MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Douglas Wiebe
- Department of Emergency MedicineDepartment of EpidemiologyUniversity of MichiganAnn ArborMichiganUSA
| | - Sharon X. Xie
- Department of BiostatisticsEpidemiology and InformaticsPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration CenterDepartment of NeurologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Dawn Mechanic‐Hamilton
- Penn Memory CenterDepartment of NeurologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Penn Memory CenterDepartment of NeurologyPerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Edward B. Lee
- Translational Neuropathology Research LaboratoryDepartment of Pathology and Laboratory MedicinePerelman School of Medicine at the University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Phillips JS, Adluru N, Chung MK, Radhakrishnan H, Olm CA, Cook PA, Gee JC, Cousins KAQ, Arezoumandan S, Wolk DA, McMillan CT, Grossman M, Irwin DJ. Greater white matter degeneration and lower structural connectivity in non-amnestic vs. amnestic Alzheimer's disease. Front Neurosci 2024; 18:1353306. [PMID: 38567286 PMCID: PMC10986184 DOI: 10.3389/fnins.2024.1353306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Multimodal evidence indicates Alzheimer's disease (AD) is characterized by early white matter (WM) changes that precede overt cognitive impairment. WM changes have overwhelmingly been investigated in typical, amnestic mild cognitive impairment and AD; fewer studies have addressed WM change in atypical, non-amnestic syndromes. We hypothesized each non-amnestic AD syndrome would exhibit WM differences from amnestic and other non-amnestic syndromes. Materials and methods Participants included 45 cognitively normal (CN) individuals; 41 amnestic AD patients; and 67 patients with non-amnestic AD syndromes including logopenic-variant primary progressive aphasia (lvPPA, n = 32), posterior cortical atrophy (PCA, n = 17), behavioral variant AD (bvAD, n = 10), and corticobasal syndrome (CBS, n = 8). All had T1-weighted MRI and 30-direction diffusion-weighted imaging (DWI). We performed whole-brain deterministic tractography between 148 cortical and subcortical regions; connection strength was quantified by tractwise mean generalized fractional anisotropy. Regression models assessed effects of group and phenotype as well as associations with grey matter volume. Topological analyses assessed differences in persistent homology (numbers of graph components and cycles). Additionally, we tested associations of topological metrics with global cognition, disease duration, and DWI microstructural metrics. Results Both amnestic and non-amnestic patients exhibited lower WM connection strength than CN participants in corpus callosum, cingulum, and inferior and superior longitudinal fasciculi. Overall, non-amnestic patients had more WM disease than amnestic patients. LvPPA patients had left-lateralized WM degeneration; PCA patients had reductions in connections to bilateral posterior parietal, occipital, and temporal areas. Topological analysis showed the non-amnestic but not the amnestic group had more connected components than controls, indicating persistently lower connectivity. Longer disease duration and cognitive impairment were associated with more connected components and fewer cycles in individuals' brain graphs. Discussion We have previously reported syndromic differences in GM degeneration and tau accumulation between AD syndromes; here we find corresponding differences in WM tracts connecting syndrome-specific epicenters. Determining the reasons for selective WM degeneration in non-amnestic AD is a research priority that will require integration of knowledge from neuroimaging, biomarker, autopsy, and functional genetic studies. Furthermore, longitudinal studies to determine the chronology of WM vs. GM degeneration will be key to assessing evidence for WM-mediated tau spread.
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Affiliation(s)
- Jeffrey S. Phillips
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Moo K. Chung
- Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States
| | - Hamsanandini Radhakrishnan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Christopher A. Olm
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Philip A. Cook
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - James C. Gee
- Penn Image Computing and Science Laboratory, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Katheryn A. Q. Cousins
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Sanaz Arezoumandan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Memory Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Corey T. McMillan
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David J. Irwin
- Penn Frontotemporal Degeneration Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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Lyu X, Duong MT, Xie L, de Flores R, Richardson H, Hwang G, Wisse LEM, DiCalogero M, McMillan CT, Robinson JL, Xie SX, Lee EB, Irwin DJ, Dickerson BC, Davatzikos C, Nasrallah IM, Yushkevich PA, Wolk DA, Das SR. Tau-neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum. Alzheimers Dement 2024; 20:1586-1600. [PMID: 38050662 PMCID: PMC10984442 DOI: 10.1002/alz.13559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 10/23/2023] [Accepted: 10/24/2023] [Indexed: 12/06/2023]
Abstract
INTRODUCTION Variability in relationship of tau-based neurofibrillary tangles (T) and neurodegeneration (N) in Alzheimer's disease (AD) arises from non-specific nature of N, modulated by non-AD co-pathologies, age-related changes, and resilience factors. METHODS We used regional T-N residual patterns to partition 184 patients within the Alzheimer's continuum into data-driven groups. These were compared with groups from 159 non-AD (amyloid "negative") patients partitioned using cortical thickness, and groups in 98 patients with ante mortem MRI and post mortem tissue for measuring N and T, respectively. We applied the initial T-N residual model to classify 71 patients in an independent cohort into predefined groups. RESULTS AD groups displayed spatial T-N mismatch patterns resembling neurodegeneration patterns in non-AD groups, similarly associated with non-AD factors and diverging cognitive outcomes. In the autopsy cohort, limbic T-N mismatch correlated with TDP-43 co-pathology. DISCUSSION T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability in AD.
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Affiliation(s)
- Xueying Lyu
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Michael Tran Duong
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Long Xie
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Hayley Richardson
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Gyujoon Hwang
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Michael DiCalogero
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey T. McMillan
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John L. Robinson
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Edward B. Lee
- Departments of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Christos Davatzikos
- Departments of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul A. Yushkevich
- Departments of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu R. Das
- Departments of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Taghvaei M, Mechanic-Hamilton DJ, Sadaghiani S, Shakibajahromi B, Dolui S, Das S, Brown C, Tackett W, Khandelwal P, Cook P, Shinohara RT, Yushkevich P, Bassett DS, Wolk DA, Detre JA. Impact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants. Neurobiol Aging 2024; 135:79-90. [PMID: 38262221 PMCID: PMC10872454 DOI: 10.1016/j.neurobiolaging.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 10/19/2023] [Accepted: 10/22/2023] [Indexed: 01/25/2024]
Abstract
We used indirect brain mapping with virtual lesion tractography to test the hypothesis that the extent of white matter tract disconnection due to white matter hyperintensities (WMH) is associated with corresponding tract-specific cognitive performance decrements. To estimate tract disconnection, WMH masks were extracted from FLAIR MRI data of 481 cognitively intact participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and used as regions of avoidance for fiber tracking in diffusion MRI data from 50 healthy young participants from the Human Connectome Project. Estimated tract disconnection in the right inferior fronto-occipital fasciculus, right frontal aslant tract, and right superior longitudinal fasciculus mediated the effects of WMH volume on executive function. Estimated tract disconnection in the left uncinate fasciculus mediated the effects of WMH volume on memory and in the right frontal aslant tract on language. In a subset of ADNI control participants with amyloid data, positive status increased the probability of periventricular WMH and moderated the relationship between WMH burden and tract disconnection in executive function performance.
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Affiliation(s)
- Mohammad Taghvaei
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | - Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sandhitsu Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher Brown
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - William Tackett
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Pulkit Khandelwal
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Philip Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
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Arezoumandan S, Cousins KA, Ohm DT, Lowe M, Chen M, Gee J, Phillips JS, McMillan CT, Luk KC, Deik A, Spindler MA, Tropea TF, Weintraub D, Wolk DA, Grossman M, Lee V, Chen‐Plotkin AS, Lee EB, Irwin DJ. Tau maturation in the clinicopathological spectrum of Lewy body and Alzheimer's disease. Ann Clin Transl Neurol 2024; 11:673-685. [PMID: 38263854 PMCID: PMC10963284 DOI: 10.1002/acn3.51988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 01/25/2024] Open
Abstract
OBJECTIVE Alzheimer's disease neuropathologic change and alpha-synucleinopathy commonly co-exist and contribute to the clinical heterogeneity of dementia. Here, we examined tau epitopes marking various stages of tangle maturation to test the hypotheses that tau maturation is more strongly associated with beta-amyloid compared to alpha-synuclein, and within the context of mixed pathology, mature tau is linked to Alzheimer's disease clinical phenotype and negatively associated with Lewy body dementia. METHODS We used digital histology to measure percent area-occupied by pathology in cortical regions among individuals with pure Alzheimer's disease neuropathologic change, pure alpha-synucleinopathy, and a co-pathology group with both Alzheimer's and alpha-synuclein pathologic diagnoses. Multiple tau monoclonal antibodies were used to detect early (AT8, MC1) and mature (TauC3) epitopes of tangle progression. We used linear/logistic regression to compare groups and test the association between pathologies and clinical features. RESULTS There were lower levels of tau pathology (β = 1.86-2.96, p < 0.001) across all tau antibodies in the co-pathology group compared to the pure Alzheimer's pathology group. Among individuals with alpha-synucleinopathy, higher alpha-synuclein was associated with greater early tau (AT8 β = 1.37, p < 0.001; MC1 β = 1.2, p < 0.001) but not mature tau (TauC3 p = 0.18), whereas mature tau was associated with beta-amyloid (β = 0.21, p = 0.01). Finally, lower tau, particularly TauC3 pathology, was associated with lower frequency of both core clinical features and categorical clinical diagnosis of dementia with Lewy bodies. INTERPRETATION Mature tau may be more closely related to beta-amyloidosis than alpha-synucleinopathy, and pathophysiological processes of tangle maturation may influence the clinical features of dementia in mixed Lewy-Alzheimer's pathology.
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Affiliation(s)
- Sanaz Arezoumandan
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Daniel T. Ohm
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - MaKayla Lowe
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Min Chen
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - James Gee
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jeffrey S. Phillips
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Corey T. McMillan
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kelvin C. Luk
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andres Deik
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Thomas F. Tropea
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Daniel Weintraub
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Murray Grossman
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Virginia Lee
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - Edward B. Lee
- Department of Pathology and Laboratory MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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10
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Dolui S, Wang Z, Wolf RL, Nabavizadeh A, Xie L, Tosun D, Nasrallah IM, Wolk DA, Detre JA. Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps. J Magn Reson Imaging 2024. [PMID: 38400805 DOI: 10.1002/jmri.29308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality. PURPOSE To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps. STUDY TYPE Retrospective. POPULATION Data from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury. FIELD STRENGTH/SEQUENCE Pulsed or pseudocontinuous ASL acquired at 3 T using non-background suppressed 2D gradient-echo echoplanar imaging or background suppressed 3D spiral spin-echo readouts. ASSESSMENT The QEI was developed using N = 101 2D CBF maps rated as unacceptable, poor, average, or excellent by two neuroradiologists and validated by 1) leave-one-out cross validation, 2) assessing if CBF reproducibility in N = 53 cognitively normal adults correlates inversely with QEI, 3) if iterative discarding of low QEI data improves the Cohen's d effect size for CBF differences between preclinical AD (N = 27) and controls (N = 53), 4) comparing the QEI with manual ratings for N = 50 3D CBF maps, and 5) comparing the QEI with another automated quality metric. STATISTICAL TESTS Inter-rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant. RESULTS The correlation between QEI and manual ratings (R = 0.83, CI: 0.76-0.88) was similar (P = 0.56) to inter-rater correlation (R = 0.81, CI: 0.73-0.87) for the 2D data. CBF reproducibility correlated negatively (R = -0.74, CI: -0.84 to -0.59) with QEI. The effect size comparing patients and controls improved (R = 0.72, CI: 0.59-0.82) as low QEI data was discarded iteratively. The correlation between QEI and manual ratings (R = 0.86, CI: 0.77-0.92) of 3D ASL was similar (P = 0.09) to inter-rater correlation (R = 0.78, CI: 0.64-0.87). The QEI correlated (R = 0.87, CI: 0.77-0.92) significantly better with manual ratings than did an existing approach (R = 0.54, CI: 0.30-0.72). DATA CONCLUSION Automated QEI performed similarly to manual ratings and can provide scalable ASL quality control. EVIDENCE LEVEL 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Sudipto Dolui
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ze Wang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Ronald L Wolf
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ali Nabavizadeh
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA
| | - Ilya M Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John A Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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11
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Ma Y, Reyes-Dumeyer D, Piriz A, Recio P, Mejia DR, Medrano M, Lantigua RA, Vonsattel JPG, Tosto G, Teich AF, Ciener B, Leskinen S, Sivakumar S, DeTure M, Ranjan D, Dickson D, Murray M, Lee E, Wolk DA, Jin LW, Dugger BN, Hiniker A, Rissman RA, Mayeux R, Vardarajan BN. Multi-omics Characterization of Epigenetic and Genetic Risk of Alzheimer Disease in Autopsied Brains from two Ethnic Groups. medRxiv 2024:2024.02.12.24302533. [PMID: 38405911 PMCID: PMC10889011 DOI: 10.1101/2024.02.12.24302533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Background Both genetic variants and epigenetic features contribute to the risk of Alzheimer's disease (AD). We studied the AD association of CpG-related single nucleotide polymorphisms (CGS), which act as the hub of both the genetic and epigenetic effects, in Hispanics decedents and generalized the findings to Non-Hispanic Whites (NHW) decedents. Methods First, we derived the dosage of the CpG site-creating allele of multiple CGSes in each 1 KB window across the genome and we conducted a sliding window association test with clinical diagnosis of AD in 7,155 Hispanics (3,194 cases and 3,961 controls) using generalized linear mixed models with the adjustment of age, sex, population structure, genomic relationship matrix, and genotyping batches. Next, using methylation and bulk RNA-sequencing data from the dorsolateral pre-frontal cortex in 150 Hispanics brains, we tested the cis- and trans-effects of AD associated CGS on brain DNA methylation to mRNA expression. For the genes with significant cis- and trans-effects, we checked their enriched pathways. Results We identified six genetic loci in Hispanics with CGS dosage associated with AD at genome-wide significance levels: ADAM20 (Score=55.2, P= 4.06×10 -8 ), between VRTN (Score=-19.6, P= 1.47×10 -8 ) and SYNDIG1L (Score=-37.7, P= 2.25×10 -9 ), SPG7 (16q24.3) (Score=40.5, P= 2.23×10 -8 ), PVRL2 (Score=125.86, P= 1.64×10 -9 ), TOMM40 (Score=-18.58, P= 4.61×10 -8 ), and APOE (Score=75.12, P= 7.26×10 -26 ). CGSes in PVRL2 and APOE were also genome-wide significant in NHW. Except for ADAM20 , CGSes in all the other five loci were associated with Hispanic brain methylation levels (mQTLs) and CGSes in SPG7, PVRL2, and APOE were also mQTLs in NHW. Except for SYNDIG1L ( P =0.08), brain methylation levels in all the other five loci affected downstream RNA expression in the Hispanics ( P <0.05), and methylation at VRTN and TOMM40 were also associated with RNA expression in NHW. Gene expression in these six loci were also regulated by CpG sites in genes that were enriched in the neuron projection and synapse (FDR<0.05). Conclusions We identified six CpG associated genetic loci associated with AD in Hispanics, harboring both genetic and epigenetic risks. However, their downstream effects on mRNA expression maybe ethnic specific and different from NHW.
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12
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Phillips JS, Robinson JL, Cousins KAQ, Wolk DA, Lee EB, McMillan CT, Trojanowski JQ, Grossman M, Irwin DJ. Polypathologic Associations with Gray Matter Atrophy in Neurodegenerative Disease. J Neurosci 2024; 44:e0808232023. [PMID: 38050082 PMCID: PMC10860605 DOI: 10.1523/jneurosci.0808-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/01/2023] [Accepted: 10/26/2023] [Indexed: 12/06/2023] Open
Abstract
Mixed pathologies are common in neurodegenerative disease; however, antemortem imaging rarely captures copathologic effects on brain atrophy due to a lack of validated biomarkers for non-Alzheimer's pathologies. We leveraged a dataset comprising antemortem MRI and postmortem histopathology to assess polypathologic associations with atrophy in a clinically heterogeneous sample of 125 human dementia patients (41 female, 84 male) with T1-weighted MRI ≤ 5 years before death and postmortem ordinal ratings of amyloid-[Formula: see text], tau, TDP-43, and [Formula: see text]-synuclein. Regional volumes were related to pathology using linear mixed-effects models; approximately 25% of data were held out for testing. We contrasted a polypathologic model comprising independent factors for each proteinopathy with two alternatives: a model that attributed atrophy entirely to the protein(s) associated with the patient's primary diagnosis and a protein-agnostic model based on the sum of ordinal scores for all pathology types. Model fits were evaluated using log-likelihood and correlations between observed and fitted volume scores. Additionally, we performed exploratory analyses relating atrophy to gliosis, neuronal loss, and angiopathy. The polypathologic model provided superior fits in the training and testing datasets. Tau, TDP-43, and [Formula: see text]-synuclein burden were inversely associated with regional volumes, but amyloid-[Formula: see text] was not. Gliosis and neuronal loss explained residual variance in and mediated the effects of tau, TDP-43, and [Formula: see text]-synuclein on atrophy. Regional brain atrophy reflects not only the primary molecular pathology but also co-occurring proteinopathies; inflammatory immune responses may independently contribute to degeneration. Our findings underscore the importance of antemortem biomarkers for detecting mixed pathology.
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Affiliation(s)
- Jeffrey S Phillips
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - John L Robinson
- Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Katheryn A Q Cousins
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - David A Wolk
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Edward B Lee
- Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Corey T McMillan
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - John Q Trojanowski
- Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Murray Grossman
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - David J Irwin
- Departments of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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13
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Chapleau M, La Joie R, Yong K, Agosta F, Allen IE, Apostolova L, Best J, Boon BDC, Crutch S, Filippi M, Fumagalli GG, Galimberti D, Graff-Radford J, Grinberg LT, Irwin DJ, Josephs KA, Mendez MF, Mendez PC, Migliaccio R, Miller ZA, Montembeault M, Murray ME, Nemes S, Pelak V, Perani D, Phillips J, Pijnenburg Y, Rogalski E, Schott JM, Seeley W, Sullivan AC, Spina S, Tanner J, Walker J, Whitwell JL, Wolk DA, Ossenkoppele R, Rabinovici GD. Demographic, clinical, biomarker, and neuropathological correlates of posterior cortical atrophy: an international cohort study and individual participant data meta-analysis. Lancet Neurol 2024; 23:168-177. [PMID: 38267189 DOI: 10.1016/s1474-4422(23)00414-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 09/22/2023] [Accepted: 10/18/2023] [Indexed: 01/26/2024]
Abstract
BACKGROUND Posterior cortical atrophy is a rare syndrome characterised by early, prominent, and progressive impairment in visuoperceptual and visuospatial processing. The disorder has been associated with underlying neuropathological features of Alzheimer's disease, but large-scale biomarker and neuropathological studies are scarce. We aimed to describe demographic, clinical, biomarker, and neuropathological correlates of posterior cortical atrophy in a large international cohort. METHODS We searched PubMed between database inception and Aug 1, 2021, for all published research studies on posterior cortical atrophy and related terms. We identified research centres from these studies and requested deidentified, individual participant data (published and unpublished) that had been obtained at the first diagnostic visit from the corresponding authors of the studies or heads of the research centres. Inclusion criteria were a clinical diagnosis of posterior cortical atrophy as defined by the local centre and availability of Alzheimer's disease biomarkers (PET or CSF), or a diagnosis made at autopsy. Not all individuals with posterior cortical atrophy fulfilled consensus criteria, being diagnosed using centre-specific procedures or before development of consensus criteria. We obtained demographic, clinical, biofluid, neuroimaging, and neuropathological data. Mean values for continuous variables were combined using the inverse variance meta-analysis method; only research centres with more than one participant for a variable were included. Pooled proportions were calculated for binary variables using a restricted maximum likelihood model. Heterogeneity was quantified using I2. FINDINGS We identified 55 research centres from 1353 papers, with 29 centres responding to our request. An additional seven centres were recruited by advertising via the Alzheimer's Association. We obtained data for 1092 individuals who were evaluated at 36 research centres in 16 countries, the other sites having not responded to our initial invitation to participate to the study. Mean age at symptom onset was 59·4 years (95% CI 58·9-59·8; I2=77%), 60% (56-64; I2=35%) were women, and 80% (72-89; I2=98%) presented with posterior cortical atrophy pure syndrome. Amyloid β in CSF (536 participants from 28 centres) was positive in 81% (95% CI 75-87; I2=78%), whereas phosphorylated tau in CSF (503 participants from 29 centres) was positive in 65% (56-75; I2=87%). Amyloid-PET (299 participants from 24 centres) was positive in 94% (95% CI 90-97; I2=15%), whereas tau-PET (170 participants from 13 centres) was positive in 97% (93-100; I2=12%). At autopsy (145 participants from 13 centres), the most frequent neuropathological diagnosis was Alzheimer's disease (94%, 95% CI 90-97; I2=0%), with common co-pathologies of cerebral amyloid angiopathy (71%, 54-88; I2=89%), Lewy body disease (44%, 25-62; I2=77%), and cerebrovascular injury (42%, 24-60; I2=88%). INTERPRETATION These data indicate that posterior cortical atrophy typically presents as a pure, young-onset dementia syndrome that is highly specific for underlying Alzheimer's disease pathology. Further work is needed to understand what drives cognitive vulnerability and progression rates by investigating the contribution of sex, genetics, premorbid cognitive strengths and weaknesses, and brain network integrity. FUNDING None.
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Affiliation(s)
- Marianne Chapleau
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Keir Yong
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Federica Agosta
- Vita-Salute, San Raffaele University, Milan, Italy; Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Insitute, Milan, Italy
| | - Isabel Elaine Allen
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | | | - John Best
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Baayla D C Boon
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Sebastian Crutch
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK
| | - Massimo Filippi
- Vita-Salute, San Raffaele University, Milan, Italy; Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Insitute, Milan, Italy
| | | | - Daniela Galimberti
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy; Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | | | - Lea T Grinberg
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA; Department of Pathology, University of California San Francisco, San Francisco, CA, USA; Department of Pathology, University of Sao Paulo Medical School, Sao Paulo, Brazil
| | - David J Irwin
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Mario F Mendez
- David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Patricio Chrem Mendez
- Memory Center, Fundación para la Lucha contra las Enfermedades Neurológicas de la Infancia, Buenos Aires Argentina
| | - Raffaella Migliaccio
- Paris Brain Institute (ICM), FrontLab, Institut de la mémoire et de la maladie d'Alzheimer (IM2A), Department of Neurology, Pitié-Salpêtrière Hospital, Paris, France
| | - Zachary A Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Maxime Montembeault
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Sára Nemes
- Indiana University School of Medicine, Indianapolis, IN, USA
| | - Victoria Pelak
- Departments of Neurology and Ophthalmology, Divisions of Neuro-Ophthalmology and Behavioral Neurology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Daniela Perani
- Vita-Salute, San Raffaele University, Milan, Italy; Division of Neuroscience, IRCCS San Raffaele, San Raffaele University, Milan, Italy
| | - Jeffrey Phillips
- Penn Frontotemporal Degeneration Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Yolande Pijnenburg
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands
| | - Emily Rogalski
- Mesulam Center for Cognitive Neurology & Alzheimer's Disease, Northwestern University, Evanston, IL, USA
| | - Jonathan M Schott
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London, UK; Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands
| | - William Seeley
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA; Department of Pathology, University of California San Francisco, San Francisco, CA, USA
| | - A Campbell Sullivan
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Salvatore Spina
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Jeremy Tanner
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Jamie Walker
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | | | - David A Wolk
- Alzheimer's Disease Research Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, Netherlands; Amsterdam Neuroscience, Neurodegeneration, Amsterdam, Netherlands; Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA; Department of Radiology & Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
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Xie L, Das SR, Wisse LEM, Ittyerah R, de Flores R, Shaw LM, Yushkevich PA, Wolk DA. Correction: Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early Alzheimer's disease. Alzheimers Res Ther 2024; 16:11. [PMID: 38217025 PMCID: PMC10785540 DOI: 10.1186/s13195-023-01374-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2024]
Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA.
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - Robin de Flores
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6 Floor, Philadelphia, PA, 19104, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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15
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. Nat Commun 2024; 15:354. [PMID: 38191573 PMCID: PMC10774282 DOI: 10.1038/s41467-023-44271-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024] Open
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiong Chen
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, San Antonio, TX, USA
| | - Colin L Masters
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Paul Maruff
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, QLD, Australia
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel S Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AI2D), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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16
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Oxenford S, Ríos AS, Hollunder B, Neudorfer C, Boutet A, Elias GJB, Germann J, Loh A, Deeb W, Salvato B, Almeida L, Foote KD, Amaral R, Rosenberg PB, Tang-Wai DF, Wolk DA, Burke AD, Sabbagh MN, Salloway S, Chakravarty MM, Smith GS, Lyketsos CG, Okun MS, Anderson WS, Mari Z, Ponce FA, Lozano A, Neumann WJ, Al-Fatly B, Horn A. WarpDrive: Improving spatial normalization using manual refinements. Med Image Anal 2024; 91:103041. [PMID: 38007978 PMCID: PMC10842752 DOI: 10.1016/j.media.2023.103041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 11/08/2023] [Accepted: 11/17/2023] [Indexed: 11/28/2023]
Abstract
Spatial normalization-the process of mapping subject brain images to an average template brain-has evolved over the last 20+ years into a reliable method that facilitates the comparison of brain imaging results across patients, centers & modalities. While overall successful, sometimes, this automatic process yields suboptimal results, especially when dealing with brains with extensive neurodegeneration and atrophy patterns, or when high accuracy in specific regions is needed. Here we introduce WarpDrive, a novel tool for manual refinements of image alignment after automated registration. We show that the tool applied in a cohort of patients with Alzheimer's disease who underwent deep brain stimulation surgery helps create more accurate representations of the data as well as meaningful models to explain patient outcomes. The tool is built to handle any type of 3D imaging data, also allowing refinements in high-resolution imaging, including histology and multiple modalities to precisely aggregate multiple data sources together.
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Affiliation(s)
- Simón Oxenford
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Ana Sofía Ríos
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Barbara Hollunder
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Einstein Center for Neurosciences Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, United States; Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alexandre Boutet
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON M5T2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, ON M5T2S8, Canada; Joint Department of Medical Imaging, University of Toronto, Toronto, ON M5T1W7, Canada
| | - Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON M5T2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, ON M5T2S8, Canada
| | - Jurgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON M5T2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, ON M5T2S8, Canada
| | - Aaron Loh
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON M5T2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, ON M5T2S8, Canada
| | - Wissam Deeb
- UMass Chan Medical School, Department of Neurology, Worcester, MA 01655, United States; UMass Memorial Health, Department of Neurology, Worcester, MA 01655, United States
| | - Bryan Salvato
- University of Florida Health Jacksonville, Jacksonville, FL, United States
| | - Leonardo Almeida
- Department of Neurology, University of Minnesota, Twin Cities Campus, Minneapolis, MN, United States
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, United States
| | - Robert Amaral
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
| | - Paul B Rosenberg
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - David F Tang-Wai
- Krembil Research Institute, University of Toronto, Toronto, ON M5T2S8, Canada; Department of Medicine, Division of Neurology, University Health Network and University of Toronto, Toronto, ON M5T2S8, Canada
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Anna D Burke
- Barrow Neurological Institute, Phoenix, AZ, United States
| | | | - Stephen Salloway
- Department of Psychiatry and Human Behavior and Neurology, Alpert Medical School of Brown University, Providence, RI, United States; Memory & Aging Program, Butler Hospital, Providence, United States
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada; Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Gwenn S Smith
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
| | | | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, United States
| | | | - Zoltan Mari
- Johns Hopkins School of Medicine, Baltimore, MD, United States; Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | | | - Andres Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON M5T2S8, Canada; Krembil Research Institute, University of Toronto, Toronto, ON M5T2S8, Canada
| | - Wolf-Julian Neumann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Bassam Al-Fatly
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Andreas Horn
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité -Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany; Brain Modulation Lab, Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, United States; Center for Brain Circuit Therapeutics Department of Neurology Brigham & Women's Hospital, Harvard Medical School, Boston, MA, United States
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17
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Yang Z, Wen J, Erus G, Govindarajan ST, Melhem R, Mamourian E, Cui Y, Srinivasan D, Abdulkadir A, Parmpi P, Wittfeld K, Grabe HJ, Bülow R, Frenzel S, Tosun D, Bilgel M, An Y, Yi D, Marcus DS, LaMontagne P, Benzinger TL, Heckbert SR, Austin TR, Waldstein SR, Evans MK, Zonderman AB, Launer LJ, Sotiras A, Espeland MA, Masters CL, Maruff P, Fripp J, Toga A, O’Bryant S, Chakravarty MM, Villeneuve S, Johnson SC, Morris JC, Albert MS, Yaffe K, Völzke H, Ferrucci L, Bryan NR, Shinohara RT, Fan Y, Habes M, Lalousis PA, Koutsouleris N, Wolk DA, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures. medRxiv 2023:2023.12.29.23300642. [PMID: 38234857 PMCID: PMC10793523 DOI: 10.1101/2023.12.29.23300642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.
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Affiliation(s)
- Zhijian Yang
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sindhuja T. Govindarajan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Randa Melhem
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Mamourian
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuhan Cui
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Dhivya Srinivasan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ahmed Abdulkadir
- Laboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
| | - Paraskevi Parmpi
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katharina Wittfeld
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Hans J. Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
- German Center for Neurodegenerative Diseases (DZNE), Site Rostock/Greifswald, Germany
| | - Robin Bülow
- Institute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany
| | - Stefan Frenzel
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Dahyun Yi
- Institute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea
| | - Daniel S. Marcus
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Pamela LaMontagne
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas R. Austin
- Cardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shari R. Waldstein
- Department of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA
| | - Michele K. Evans
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B. Zonderman
- Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Lenore J. Launer
- Neuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA
| | - Aristeidis Sotiras
- Department of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA
| | - Mark A. Espeland
- Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Colin L. Masters
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Paul Maruff
- Florey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia
| | - Jurgen Fripp
- CSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Sid O’Bryant
- Institute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA
| | - Mallar M. Chakravarty
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada
| | - Sylvia Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA
| | - John C. Morris
- Knight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Marilyn S. Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kristine Yaffe
- Departments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
| | - Henry Völzke
- Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Luigi Ferrucci
- Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA
| | - Nick R. Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Russell T. Shinohara
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mohamad Habes
- Biggs Alzheimer’s Institute, University of Texas San Antonio Health Science Center, USA
| | - Paris Alexandros Lalousis
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Nikolaos Koutsouleris
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- Section for Precision Psychiatry, Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University Munich, Munich, Germany
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Haochang Shou
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ilya M. Nasrallah
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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18
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Ramanan VK, Armstrong MJ, Choudhury P, Coerver KA, Hamilton RH, Klein BC, Wolk DA, Wessels SR, Jones LK. Antiamyloid Monoclonal Antibody Therapy for Alzheimer Disease: Emerging Issues in Neurology. Neurology 2023; 101:842-852. [PMID: 37495380 PMCID: PMC10663011 DOI: 10.1212/wnl.0000000000207757] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023] Open
Abstract
With recent data demonstrating that lecanemab treatment can slow cognitive and functional decline in early symptomatic Alzheimer disease (AD), it is widely anticipated that this drug and potentially other monoclonal antibody infusions targeting β-amyloid protein will imminently be realistic options for some patients with AD. Given that these new antiamyloid monoclonal antibodies (mAbs) are associated with nontrivial risks and burdens of treatment that are radically different from current mainstays of AD management, effectively and equitably translating their use to real-world clinical care will require systematic and practice-specific modifications to existing workflows and infrastructure. In this Emerging Issues in Neurology article, we provide practical guidance for a wide audience of neurology clinicians on logistic adaptations and decision making around emerging antiamyloid mAbs. Specifically, we briefly summarize the rationale and available evidence supporting antiamyloid mAb use in AD to facilitate appropriate communication with patients and care partners on potential benefits. We also discuss pragmatic approaches to optimizing patient selection and treatment monitoring, with a particular focus on the value of incorporating shared decision making and multidisciplinary collaboration. In addition, we review some of the recognized limitations of current knowledge and highlight areas of future evolution to guide the development of sustainable and flexible models for treatment and follow-up. As the field enters a new era with disease-modifying treatment options for AD, it will be critical for neurology practices to prepare and continually innovate to ensure optimal outcomes for patients.
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Affiliation(s)
- Vijay K Ramanan
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - Melissa J Armstrong
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - Parichita Choudhury
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - Katherine A Coerver
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - Roy H Hamilton
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - Brad C Klein
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - David A Wolk
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - Scott R Wessels
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
| | - Lyell K Jones
- From the Department of Neurology (V.K.R., L.K.J.), Mayo Clinic, Rochester, MN; Department of Neurology (M.J.A.), University of Florida College of Medicine; Norman Fixel Institute for Neurologic Diseases (M.J.A.), University of Florida, Gainesville; Cleo Roberts Center (P.C.), Banner Sun Health Research Institute, Sun City, AZ; Rocky Mountain Neurology (K.C.), Lone Tree, CO; Department of Neurology (R.H.H., D.A.W.), Department of Physical Medicine and Rehabilitation (R.H.H.), and Department of Psychiatry (R.H.H.), University of Pennsylvania Perelman School of Medicine, Philadelphia; Abington Neurological Associates (B.C.K.), Ltd., Abington, PA; and American Academy of Neurology (S.R.W.), Minneapolis, MN
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19
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Hammers DB, Nemes S, Diedrich T, Eloyan A, Kirby K, Aisen P, Kramer J, Nudelman K, Foroud T, Rumbaugh M, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Weintraub S, Wingo TS, Wolk DA, Wong B, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Learning slopes in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S19-S28. [PMID: 37243937 PMCID: PMC10806757 DOI: 10.1002/alz.13159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/16/2023] [Accepted: 02/16/2023] [Indexed: 05/29/2023]
Abstract
OBJECTIVE Investigation of learning slopes in early-onset dementias has been limited. The current study aimed to highlight the sensitivity of learning slopes to discriminate disease severity in cognitively normal participants and those diagnosed with early-onset dementia with and without β-amyloid positivity METHOD: Data from 310 participants in the Longitudinal Early-Onset Alzheimer's Disease Study (aged 41 to 65) were used to calculate learning slope metrics. Learning slopes among diagnostic groups were compared, and the relationships of slopes with standard memory measures were determined RESULTS: Worse learning slopes were associated with more severe disease states, even after controlling for demographics, total learning, and cognitive severity. A particular metric-the learning ratio (LR)-outperformed other learning slope calculations across analyses CONCLUSIONS: Learning slopes appear to be sensitive to early-onset dementias, even when controlling for the effect of total learning and cognitive severity. The LR may be the learning measure of choice for such analyses. HIGHLIGHTS Learning is impaired in amyloid-positive EOAD, beyond cognitive severity scores alone. Amyloid-positive EOAD participants perform worse on learning slopes than amyloid-negative participants. Learning ratio appears to be the learning metric of choice for EOAD participants.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Sára Nemes
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Taylor Diedrich
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, California, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steve Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Sandra Weintraub
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Bonnie Wong
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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20
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Polsinelli AJ, Wonderlin RJ, Hammers DB, Pena Garcia A, Eloyan A, Taurone A, Thangarajah M, Beckett L, Gao S, Wang S, Kirby K, Logan PE, Aisen P, Dage JL, Foroud T, Griffin P, Iaccarino L, Kramer JH, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Soleimani-Meigooni DN, Rumbaugh M, Toga AW, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Womack K, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Baseline neuropsychiatric symptoms and psychotropic medication use midway through data collection of the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort. Alzheimers Dement 2023; 19 Suppl 9:S42-S48. [PMID: 37296082 PMCID: PMC10709525 DOI: 10.1002/alz.13344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 05/15/2023] [Accepted: 05/17/2023] [Indexed: 06/12/2023]
Abstract
INTRODUCTION We examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). METHODS Baseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70). RESULTS Affective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD. Tension and impulse control behaviors were more common in EOnonAD. A minority of participants were using psychotropic medications, and use was higher in EOnonAD. DISCUSSION Overall NPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants. Future research will investigate moderators and etiological drivers of NPS, and NPS differences in EOAD versus late-onset AD.
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Affiliation(s)
- Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Ryan J. Wonderlin
- Marian University College of Osteopathic Medicine, Indianapolis, Indiana, 46222, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Alex Pena Garcia
- Marian University College of Osteopathic Medicine, Indianapolis, Indiana, 46222, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, 02912, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, 95616, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Sophia Wang
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, 92121, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Joel H. Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, 98195, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, 90033, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55123, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, 33140, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, 10032, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55123, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, 77030, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Kyle Womack
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, 63110, USA
| | - Erik Musiek
- Department of Neurology, Washington University School of Medicine in St. Louis, St. Louis, MO, 63110, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Meghan Riddle
- Department of Psychiatry, Alpert Medical School, Brown University, Providence, Rhode Island, 02912, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, USA
| | - Steven Salloway
- Department of Psychiatry, Alpert Medical School, Brown University, Providence, Rhode Island, 02912, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, 94304, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., 20057, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, 30307, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, 94143, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, 92121, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, 46202, USA
| | - LEADS Consortium
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
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21
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Nemes S, Logan PE, Manchella MK, Mundada NS, Joie RL, Polsinelli AJ, Hammers DB, Koeppe RA, Foroud TM, Nudelman KN, Eloyan A, Iaccarino L, Dorsant-Ardón V, Taurone A, Maryanne Thangarajah, Dage JL, Aisen P, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Womack KB, Wolk DA, Rabinovici GD, Carrillo MC, Dickerson BC, Apostolova LG. Sex and APOE ε4 carrier effects on atrophy, amyloid PET, and tau PET burden in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S49-S63. [PMID: 37496307 PMCID: PMC10811272 DOI: 10.1002/alz.13403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/26/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION We used sex and apolipoprotein E ε4 (APOE ε4) carrier status as predictors of pathologic burden in early-onset Alzheimer's disease (EOAD). METHODS We included baseline data from 77 cognitively normal (CN), 230 EOAD, and 70 EO non-Alzheimer's disease (EOnonAD) participants from the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS). We stratified each diagnostic group by males and females, then further subdivided each sex by APOE ε4 carrier status and compared imaging biomarkers in each stratification. Voxel-wise multiple linear regressions yielded statistical brain maps of gray matter density, amyloid, and tau PET burden. RESULTS EOAD females had greater amyloid and tau PET burdens than males. EOAD female APOE ε4 non-carriers had greater amyloid PET burdens and greater gray matter atrophy than female ε4 carriers. EOnonAD female ε4 non-carriers also had greater gray matter atrophy than female ε4 carriers. DISCUSSION The effects of sex and APOE ε4 must be considered when studying these populations. HIGHLIGHTS Novel analysis examining the effects of biological sex and apolipoprotein E ε4 (APOE ε4) carrier status on neuroimaging biomarkers among early-onset Alzheimer's disease (EOAD), early-onset non-AD (EOnonAD), and cognitively normal (CN) participants. Female sex is associated with greater pathology burden in the EOAD cohort compared to male sex. The effect of APOE ε4 carrier status on pathology burden was the most impactful in females across all cohorts.
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Affiliation(s)
- Sára Nemes
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paige E. Logan
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Mohit K. Manchella
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Department of Chemistry, University of Southern Indiana, Evansville, Indiana, 47712, USA
| | - Nidhi S. Mundada
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Renaud La Joie
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Robert A. Koeppe
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI, 48105, USA
| | - Tatiana M. Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Kelly N. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Valérie Dorsant-Ardón
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, 02912, USA
| | - Jeffery L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, 92121, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California, San Francisco, California, 94158, USA
- Department of Pathology, University of California, San Francisco, San Francisco, CA, 94158, USA
| | - Clifford R. Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Joel Kramer
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA, 98195, USA
| | - Melissa E. Murray
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, 90033, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, 85315, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, 32224, USA
| | - Ranjan Duara
- Department of Neurology, Center for Mind/Brain Medicine, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, 02115, USA
- Wein Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, 33140, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, 10032, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, 559095, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, 77030, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, 90095, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, 21205, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, 60611, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, 02906, USA
| | - Sharon J. Sha
- Department of Neurology and Neurological Sciences, Stanford University, Palo Alto, CA, 94304, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown Universit, Washington, DC, 20007, USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, 63110, USA
| | - David A. Wolk
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA,19104, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California, San Francisco, California, 94158, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, 60603, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, 02114, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
- Indiana Alzheimer’s Disease Research Center, Indianapolis, Indiana, 46202 USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, 46202, USA
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22
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Hammers DB, Eloyan A, Taurone A, Thangarajah M, Beckett L, Gao S, Kirby K, Aisen P, Dage JL, Foroud T, Griffin P, Grinberg LT, Jack CR, Kramer J, Koeppe R, Kukull WA, Mundada NS, Joie RL, Soleimani-Meigooni DN, Iaccarino L, Murray ME, Nudelman K, Polsinelli AJ, Rumbaugh M, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Womack K, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Profiling baseline performance on the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort near the midpoint of data collection. Alzheimers Dement 2023; 19 Suppl 9:S8-S18. [PMID: 37256497 PMCID: PMC10806768 DOI: 10.1002/alz.13160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 04/12/2023] [Accepted: 04/13/2023] [Indexed: 06/01/2023]
Abstract
OBJECTIVE The Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) seeks to provide comprehensive understanding of early-onset Alzheimer's disease (EOAD; onset <65 years), with the current study profiling baseline clinical, cognitive, biomarker, and genetic characteristics of the cohort nearing the data-collection mid-point. METHODS Data from 371 LEADS participants were compared based on diagnostic group classification (cognitively normal [n = 89], amyloid-positive EOAD [n = 212], and amyloid-negative early-onset non-Alzheimer's disease [EOnonAD; n = 70]). RESULTS Cognitive performance was worse for EOAD than other groups, and EOAD participants were apolipoprotein E (APOE) ε4 homozygotes at higher rates. An amnestic presentation was common among impaired participants (81%), with several clinical phenotypes present. LEADS participants generally consented at high rates to optional trial procedures. CONCLUSIONS We present the most comprehensive baseline characterization of sporadic EOAD in the United States to date. EOAD presents with widespread cognitive impairment within and across clinical phenotypes, with differences in APOE ε4 allele carrier status appearing to be relevant. HIGHLIGHTS Findings represent the most comprehensive baseline characterization of sporadic early-onset Alzheimer's disease (EOAD) to date. Cognitive impairment was widespread for EOAD participants and more severe than other groups. EOAD participants were homozygous apolipoprotein E (APOE) ε4 carriers at higher rates than the EOnonAD group. Amnestic presentation predominated in EOAD and EOnonAD participants, but other clinical phenotypes were present.
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Affiliation(s)
- Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA
| | - Sujuan Gao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Lea T. Grinberg
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Angelina J. Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steven Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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23
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Eloyan A, Thangarajah M, An N, Borowski BJ, Reddy AL, Aisen P, Dage JL, Foroud T, Ghetti B, Griffin P, Hammers D, Iaccarino L, Jack CR, Kirby K, Kramer J, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez MF, Musiek E, Onyike CU, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Womack K, Beckett L, Gao S, Carrillo MC, Rabinovici G, Apostolova LG, Dickerson B, Vemuri P. White matter hyperintensities are higher among early-onset Alzheimer's disease participants than their cognitively normal and early-onset nonAD peers: Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S89-S97. [PMID: 37491599 PMCID: PMC10808262 DOI: 10.1002/alz.13402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION We compared white matter hyperintensities (WMHs) in early-onset Alzheimer's disease (EOAD) with cognitively normal (CN) and early-onset amyloid-negative cognitively impaired (EOnonAD) groups in the Longitudinal Early-Onset Alzheimer's Disease Study. METHODS We investigated the role of increased WMH in cognition and amyloid and tau burden. We compared WMH burden of 205 EOAD, 68 EOnonAD, and 89 CN participants in lobar regions using t-tests and analyses of covariance. Linear regression analyses were used to investigate the association between WMH and cognitive impairment and that between amyloid and tau burden. RESULTS EOAD showed greater WMHs compared with CN and EOnonAD participants across all regions with no significant differences between CN and EOnonAD groups. Greater WMHs were associated with worse cognition. Tau burden was positively associated with WMH burden in the EOAD group. DISCUSSION EOAD consistently showed higher WMH volumes. Overall, greater WMHs were associated with worse cognition and higher tau burden in EOAD. HIGHLIGHTS This study represents a comprehensive characterization of WMHs in sporadic EOAD. WMH volumes are associated with tau burden from positron emission tomography (PET) in EOAD, suggesting WMHs are correlated with increasing burden of AD. Greater WMH volumes are associated with worse performance on global cognitive tests. EOAD participants have higher WMH volumes compared with CN and early-onset amyloid-negative cognitively impaired (EOnonAD) groups across all brain regions.
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Affiliation(s)
- Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Na An
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA, 02903
| | - Bret J. Borowski
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Ashritha L. Reddy
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA, 92121
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Bernardino Ghetti
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Pathology & Laboratory Medicine Indiana University School of Medicine, Indianapolis, Indiana, USA, 02912
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA, 60603
| | - Dustin Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Clifford R. Jack
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA, 48109
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA, 98195
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA, 32224
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA, 90033
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA, 02114
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA, 85351
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA, 32224
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA, 33140
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA,10032
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA, 55905
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA, 77030
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA, 90095
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63108
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA, 21205
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA, 60611
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA, 02912
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA, 94304
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., USA, 20007
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA, 30322
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA, 19104
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63108
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA, 95616
| | - Sujuan Gao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA, 60603
| | - Gil Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA, 94143
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA, 46202
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA, 46202
| | - Brad Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA, 02114
| | - Prashanthi Vemuri
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA, 55905
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24
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Cho H, Mundada NS, Apostolova LG, Carrillo MC, Shankar R, Amuiri AN, Zeltzer E, Windon CC, Soleimani-Meigooni DN, Tanner JA, Heath CL, Lesman-Segev OH, Aisen P, Eloyan A, Lee HS, Hammers DB, Kirby K, Dage JL, Fagan A, Foroud T, Grinberg LT, Jack CR, Kramer J, Kukull WA, Murray ME, Nudelman K, Toga A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski EJ, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Koeppe R, Iaccarino L, Dickerson BC, La Joie R, Rabinovici GD. Amyloid and tau-PET in early-onset AD: Baseline data from the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Alzheimers Dement 2023; 19 Suppl 9:S98-S114. [PMID: 37690109 PMCID: PMC10807231 DOI: 10.1002/alz.13453] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/07/2023] [Accepted: 08/08/2023] [Indexed: 09/12/2023]
Abstract
INTRODUCTION We aimed to describe baseline amyloid-beta (Aβ) and tau-positron emission tomograrphy (PET) from Longitudinal Early-onset Alzheimer's Disease Study (LEADS), a prospective multi-site observational study of sporadic early-onset Alzheimer's disease (EOAD). METHODS We analyzed baseline [18F]Florbetaben (Aβ) and [18F]Flortaucipir (tau)-PET from cognitively impaired participants with a clinical diagnosis of mild cognitive impairment (MCI) or AD dementia aged < 65 years. Florbetaben scans were used to distinguish cognitively impaired participants with EOAD (Aβ+) from EOnonAD (Aβ-) based on the combination of visual read by expert reader and image quantification. RESULTS 243/321 (75.7%) of participants were assigned to the EOAD group based on amyloid-PET; 231 (95.1%) of them were tau-PET positive (A+T+). Tau-PET signal was elevated across cortical regions with a parietal-predominant pattern, and higher burden was observed in younger and female EOAD participants. DISCUSSION LEADS data emphasizes the importance of biomarkers to enhance diagnostic accuracy in EOAD. The advanced tau-PET binding at baseline might have implications for therapeutic strategies in patients with EOAD. HIGHLIGHTS 72% of patients with clinical EOAD were positive on both amyloid- and tau-PET. Amyloid-positive patients with EOAD had high tau-PET signal across cortical regions. In EOAD, tau-PET mediated the relationship between amyloid-PET and MMSE. Among EOAD patients, younger onset and female sex were associated with higher tau-PET.
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Affiliation(s)
- Hanna Cho
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
- Global Brain Health Institute, University of California, San Francisco, California, USA
| | - Nidhi S Mundada
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Ranjani Shankar
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Alinda N Amuiri
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Ehud Zeltzer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Charles C Windon
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - David N Soleimani-Meigooni
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Jeremy A Tanner
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Courtney Lawhn Heath
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Orit H Lesman-Segev
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Diagnostic Imaging, Sheba Medical Center, Tel HaShomer, Israel
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Rhode Island, USA
| | - Hye Sun Lee
- Biostatistics Collaboration Unit, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dustin B Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lea T Grinberg
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Pathology, University of California - San Francisco, San Francisco, California, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joel Kramer
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Emily J Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | | | - Thomas S Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Robert Koeppe
- Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Leonardo Iaccarino
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Renaud La Joie
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
| | - Gil D Rabinovici
- Memory and Aging Center, UCSF Weill Institute for Neurosciences, Department of Neurology, University of California, San Francisco, California, USA
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
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25
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Touroutoglou A, Katsumi Y, Brickhouse M, Zaitsev A, Eckbo R, Aisen P, Beckett L, Dage JL, Eloyan A, Foroud T, Ghetti B, Griffin P, Hammers D, Jack CR, Kramer JH, Iaccarino L, Joie RL, Mundada NS, Koeppe R, Kukull WA, Murray ME, Nudelman K, Polsinelli AJ, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Womack K, Carrillo MC, Rabinovici GD, Apostolova LG, Dickerson BC. The Sporadic Early-onset Alzheimer's Disease Signature Of Atrophy: Preliminary Findings From The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) Cohort. Alzheimers Dement 2023; 19 Suppl 9:S74-S88. [PMID: 37850549 PMCID: PMC10829523 DOI: 10.1002/alz.13466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/13/2023] [Accepted: 07/18/2023] [Indexed: 10/19/2023]
Abstract
INTRODUCTION Magnetic resonance imaging (MRI) research has advanced our understanding of neurodegeneration in sporadic early-onset Alzheimer's disease (EOAD) but studies include small samples, mostly amnestic EOAD, and have not focused on developing an MRI biomarker. METHODS We analyzed MRI scans to define the sporadic EOAD-signature atrophy in a small sample (n = 25) of Massachusetts General Hospital (MGH) EOAD patients, investigated its reproducibility in the large longitudinal early-onset Alzheimer's disease study (LEADS) sample (n = 211), and investigated the relationship of the magnitude of atrophy with cognitive impairment. RESULTS The EOAD-signature atrophy was replicated across the two cohorts, with prominent atrophy in the caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortices, and with relative sparing of the medial temporal lobe. The magnitude of EOAD-signature atrophy was associated with the severity of cognitive impairment. DISCUSSION The EOAD-signature atrophy is a reliable and clinically valid biomarker of AD-related neurodegeneration that could be used in clinical trials for EOAD. HIGHLIGHTS We developed an early-onset Alzheimer's disease (EOAD)-signature of atrophy based on magnetic resonance imaging (MRI) scans. EOAD signature was robustly reproducible across two independent patient cohorts. EOAD signature included prominent atrophy in parietal and posterior temporal cortex. The EOAD-signature atrophy was associated with the severity of cognitive impairment. EOAD signature is a reliable and clinically valid biomarker of neurodegeneration.
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Affiliation(s)
- Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yuta Katsumi
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Michael Brickhouse
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexander Zaitsev
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan Eckbo
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Paul Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Laurel Beckett
- Department of Public Health Sciences, University of California - Davis, Davis, California, USA
| | - Jeffrey L Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bernardino Ghetti
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Dustin Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joel H Kramer
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Renaud La Joie
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Angelina J Polsinelli
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic in Florida, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer's Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer's Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - R Scott Turner
- Department of Neurology, Georgetown University, Washington, D.C., USA
| | - Thomas S Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Maria C Carrillo
- Medical & Scientific Relations Division, Alzheimer's Association, Chicago, Illinois, USA
| | - Gil D Rabinovici
- Department of Neurology, University of California - San Francisco, San Francisco, California, USA
| | - Liana G Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
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Dage JL, Eloyan A, Thangarajah M, Hammers DB, Fagan AM, Gray JD, Schindler SE, Snoddy C, Nudelman KNH, Faber KM, Foroud T, Aisen P, Griffin P, Grinberg LT, Iaccarino L, Kirby K, Kramer J, Koeppe R, Kukull WA, Joie RL, Mundada NS, Murray ME, Rumbaugh M, Soleimani-Meigooni DN, Toga AW, Touroutoglou A, Vemuri P, Atri A, Beckett LA, Day GS, Graff-Radford NR, Duara R, Honig LS, Jones DT, Masdeu JC, Mendez MF, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Womack KB, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Cerebrospinal fluid biomarkers in the Longitudinal Early-onset Alzheimer's Disease Study. Alzheimers Dement 2023; 19 Suppl 9:S115-S125. [PMID: 37491668 PMCID: PMC10877673 DOI: 10.1002/alz.13399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 07/27/2023]
Abstract
INTRODUCTION One goal of the Longitudinal Early Onset Alzheimer's Disease Study (LEADS) is to define the fluid biomarker characteristics of early-onset Alzheimer's disease (EOAD). METHODS Cerebrospinal fluid (CSF) concentrations of Aβ1-40, Aβ1-42, total tau (tTau), pTau181, VILIP-1, SNAP-25, neurogranin (Ng), neurofilament light chain (NfL), and YKL-40 were measured by immunoassay in 165 LEADS participants. The associations of biomarker concentrations with diagnostic group and standard cognitive tests were evaluated. RESULTS Biomarkers were correlated with one another. Levels of CSF Aβ42/40, pTau181, tTau, SNAP-25, and Ng in EOAD differed significantly from cognitively normal and early-onset non-AD dementia; NfL, YKL-40, and VILIP-1 did not. Across groups, all biomarkers except SNAP-25 were correlated with cognition. Within the EOAD group, Aβ42/40, NfL, Ng, and SNAP-25 were correlated with at least one cognitive measure. DISCUSSION This study provides a comprehensive analysis of CSF biomarkers in sporadic EOAD that can inform EOAD clinical trial design.
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Affiliation(s)
- Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Anne M. Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Julia D. Gray
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Suzanne E. Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Casey Snoddy
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelly N. H. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Kelley M. Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Percy Griffin
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Lea T. Grinberg
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Nidhi S Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Laurel A. Beckett
- Department of Public Health Sciences, University of California-Davis, Davis, California, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | | | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario F. Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington, D.C., USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Kyle B. Womack
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, Indiana, USA
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Bushnell J, Hammers DB, Aisen P, Dage JL, Eloyan A, Foroud T, Grinberg LT, Iaccarino L, Jack CR, Kirby K, Kramer J, Koeppe R, Kukull WA, La Joie R, Mundada NS, Murray ME, Nudelman K, Rumbaugh M, Soleimani-Meigooni DN, Toga A, Touroutoglou A, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu J, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha S, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG, Clark DG. Influence of amyloid and diagnostic syndrome on non-traditional memory scores in early-onset Alzheimer's disease. Alzheimers Dement 2023; 19 Suppl 9:S29-S41. [PMID: 37653686 PMCID: PMC10855009 DOI: 10.1002/alz.13434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/27/2023] [Accepted: 06/28/2023] [Indexed: 09/02/2023]
Abstract
INTRODUCTION The Rey Auditory Verbal Learning Test (RAVLT) is a useful neuropsychological test for describing episodic memory impairment in dementia. However, there is limited research on its utility in early-onset Alzheimer's disease (EOAD). We assess the influence of amyloid and diagnostic syndrome on several memory scores in EOAD. METHODS We transcribed RAVLT recordings from 303 subjects in the Longitudinal Early-Onset Alzheimer's Disease Study. Subjects were grouped by amyloid status and syndrome. Primacy, recency, J-curve, duration, stopping time, and speed score were calculated and entered into linear mixed effects models as dependent variables. RESULTS Compared with amyloid negative subjects, positive subjects exhibited effects on raw score, primacy, recency, and stopping time. Inter-syndromic differences were noted with raw score, primacy, recency, J-curve, and stopping time. DISCUSSION RAVLT measures are sensitive to the effects of amyloid and syndrome in EOAD. Future work is needed to quantify the predictive value of these scores. HIGHLIGHTS RAVLT patterns characterize various presentations of EOAD and EOnonAD Amyloid impacts raw score, primacy, recency, and stopping time Timing-based scores add value over traditional count-based scores.
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Affiliation(s)
- Justin Bushnell
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, California, USA
| | - Jeffrey L. Dage
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, Rhode Island, USA
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lea T. Grinberg
- Department of Pathology, University of California – San Francisco, San Francisco, California, USA
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Leonardo Iaccarino
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kala Kirby
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Renaud La Joie
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Nidhi S. Mundada
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | | | - Kelly Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Arthur Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, California, USA
| | - Alexandra Touroutoglou
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, Florida, USA
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, Texas, USA
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Steven Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, Rhode Island, USA
| | - Sharon Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, California, USA
| | - Raymond S. Turner
- Department of Neurology, Georgetown University, Washington D.C., USA
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, Illinois, USA
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, California, USA
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - David G. Clark
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
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28
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Nudelman KNH, Jackson T, Rumbaugh M, Eloyan A, Abreu M, Dage JL, Snoddy C, Faber KM, Foroud T, Hammers DB, Taurone A, Thangarajah M, Aisen P, Beckett L, Kramer J, Koeppe R, Kukull WA, Murray ME, Toga AW, Vemuri P, Atri A, Day GS, Duara R, Graff-Radford NR, Honig LS, Jones DT, Masdeu JC, Mendez M, Musiek E, Onyike CU, Riddle M, Rogalski E, Salloway S, Sha SJ, Turner RS, Wingo TS, Wolk DA, Carrillo MC, Dickerson BC, Rabinovici GD, Apostolova LG. Pathogenic variants in the Longitudinal Early-onset Alzheimer's Disease Study cohort. Alzheimers Dement 2023; 19 Suppl 9:S64-S73. [PMID: 37801072 PMCID: PMC10783439 DOI: 10.1002/alz.13482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 10/07/2023]
Abstract
INTRODUCTION One goal of the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is to investigate the genetic etiology of early onset (40-64 years) cognitive impairment. Toward this goal, LEADS participants are screened for known pathogenic variants. METHODS LEADS amyloid-positive early-onset Alzheimer's disease (EOAD) or negative early-onset non-AD (EOnonAD) cases were whole exome sequenced (N = 299). Pathogenic variant frequency in APP, PSEN1, PSEN2, GRN, MAPT, and C9ORF72 was assessed for EOAD and EOnonAD. Gene burden testing was performed in cases compared to similar-age cognitively normal controls in the Parkinson's Progression Markers Initiative (PPMI) study. RESULTS Previously reported pathogenic variants in the six genes were identified in 1.35% of EOAD (3/223) and 6.58% of EOnonAD (5/76). No genes showed enrichment for carriers of rare functional variants in LEADS cases. DISCUSSION Results suggest that LEADS is enriched for novel genetic causative variants, as previously reported variants are not observed in most cases. HIGHLIGHTS Sequencing identified eight cognitively impaired pathogenic variant carriers. Pathogenic variants were identified in PSEN1, GRN, MAPT, and C9ORF72. Rare variants were not enriched in APP, PSEN1/2, GRN, and MAPT. The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is a key resource for early-onset Alzheimer's genetic research.
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Affiliation(s)
- Kelly N. H. Nudelman
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
| | - Trever Jackson
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Malia Rumbaugh
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Ani Eloyan
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Marco Abreu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Jeffrey L. Dage
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Casey Snoddy
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Kelley M. Faber
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - Tatiana Foroud
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
| | - Dustin B. Hammers
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
| | - DIAN/DIAN-TU Clinical/Genetics Committee
- Washington University School of Medicine in St. Louis, MO, USA, 63110
- Icahn School of Medicine at Mount Sinai, New York, NY, USA, 10029
- Mayo Clinic College of Medicine, Jacksonville, FL, USA, 32224
| | - Alexander Taurone
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Maryanne Thangarajah
- Department of Biostatistics, Center for Statistical Sciences, Brown University, Providence, RI, USA, 02912
| | - Paul Aisen
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, 92121
| | - Laurel Beckett
- Department of Public Health Sciences, University of California – Davis, Davis, California, USA, 95616
| | - Joel Kramer
- Department of Neurology, University of California – San Francisco, San Francisco, CA, USA, 94143
| | - Robert Koeppe
- Department of Radiology, University of Michigan, Ann Arbor, MI, USA, 48109
| | - Walter A. Kukull
- Department of Epidemiology, University of Washington, Seattle, WA, USA, 98195
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA, 32224
| | - Arthur W. Toga
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, Los Angeles, CA, USA, 90033
| | | | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, AZ, USA, 85315
| | - Gregory S. Day
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA, 32224
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center, Miami, FL, USA, 33140
| | | | - Lawrence S. Honig
- Taub Institute and Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA, 10032
| | - David T. Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
- Department of Neurology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Joseph C. Masdeu
- Nantz National Alzheimer Center, Houston Methodist and Weill Cornell Medicine, Houston, TX, USA, 77030
| | - Mario Mendez
- Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA, 90095
| | - Erik Musiek
- Department of Neurology, Washington University in St. Louis, St. Louis, Missouri, USA, 63110
| | - Chiadi U. Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA, 21295
| | - Meghan Riddle
- Department of Neurology, Alpert Medical School, Brown University, Providence, RI Island, USA, 02912
| | - Emily Rogalski
- Department of Psychiatry and Behavioral Sciences, Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA , 60611
| | - Stephen Salloway
- Department of Neurology, Alpert Medical School, Brown University, Providence, RI Island, USA, 02912
| | - Sharon J. Sha
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA, 94304
| | - R. Scott Turner
- Department of Neurology, Georgetown University, DC, USA, 20057
| | - Thomas S. Wingo
- Department of Neurology and Human Genetics, Emory University School of Medicine, Atlanta, GA, USA, 30307
| | - David A. Wolk
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, 19104
| | - Maria C. Carrillo
- Medical & Scientific Relations Division, Alzheimer’s Association, Chicago, IL, USA, 60603
| | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, 02114
| | - Gil D. Rabinovici
- Department of Neurology, University of California – San Francisco, San Francisco, CA, USA, 94143
| | - Liana G. Apostolova
- Indiana Alzheimer’s Disease Research Center, Indianapolis, IN, USA, 46202
- Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA, 46202
- Alzheimer’s Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, 92121
- Department of Radiology and Imaging Sciences, Center for Neuroimaging, Indiana University School of Medicine Indianapolis, Indianapolis, IN, USA, 46202
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29
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Richards KC, Lozano AJ, Morris J, Moelter ST, Ji W, Vallabhaneni V, Wang Y, Chi L, Davis EM, Cheng C, Aguilar V, Khan S, Sankhavaram M, Hanlon AL, Wolk DA, Gooneratne N. Predictors of Adherence to Continuous Positive Airway Pressure in Older Adults With Apnea and Amnestic Mild Cognitive Impairment. J Gerontol A Biol Sci Med Sci 2023; 78:1861-1870. [PMID: 37021413 PMCID: PMC11007392 DOI: 10.1093/gerona/glad099] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Indexed: 04/07/2023] Open
Abstract
BACKGROUND Almost 60% of adults with amnestic mild cognitive impairment (aMCI) have obstructive sleep apnea (OSA). Treatment with continuous positive airway pressure (CPAP) may delay cognitive decline, but CPAP adherence is often suboptimal. In this study, we report predictors of CPAP adherence in older adults with aMCI who have increased odds of progressing to dementia, particularly due to Alzheimer's disease. METHODS The data are from Memories 2, "Changing the Trajectory of Mild Cognitive Impairment with CPAP Treatment of Obstructive Sleep Apnea." Participants had moderate to severe OSA, were CPAP naïve, and received a telehealth CPAP adherence intervention. Linear and logistic regression models examined predictors. RESULTS The 174 participants (mean age 67.08 years, 80 female, 38 Black persons) had a mean apnea-hypopnea index of 34.78, and 73.6% were adherent, defined as an average of ≥4 hours of CPAP use per night. Only 18 (47.4%) Black persons were CPAP adherent. In linear models, White race, moderate OSA, and participation in the tailored CPAP adherence intervention were significantly associated with higher CPAP use at 3 months. In logistic models, White persons had 9.94 times the odds of adhering to CPAP compared to Black persons. Age, sex, ethnicity, education, body mass index, nighttime sleep duration, daytime sleepiness, and cognitive status were not significant predictors. CONCLUSIONS Older patients with aMCI have high CPAP adherence, suggesting that age and cognitive impairment should not be a barrier to prescribing CPAP. Research is needed to improve adherence in Black patients, perhaps through culturally tailored interventions.
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Affiliation(s)
- Kathy C Richards
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Alicia J Lozano
- Department of Statistics, Center for Biostatistics and Health Data Science, Virginia Tech, Roanoke, Virginia, USA
| | - Jennifer Morris
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stephen T Moelter
- Department of Psychology, Saint Joseph’s University, Philadelphia, Pennsylvania, USA
| | - Wenyan Ji
- Department of Statistics, Center for Biostatistics and Health Data Science, Virginia Tech, Roanoke, Virginia, USA
| | | | - Yanyan Wang
- National Clinical Research Center for Geriatrics & Nursing Key Laboratory of Sichuan Province, West China Hospital & West China School of Medicine, Sichuan University, Chengdu, China
| | - Luqi Chi
- Department of Neurology, Washington University, St. Louis, Missouri, USA
- Department of Sleep Medicine, Washington University, St. Louis, Missouri, USA
| | - Eric M Davis
- Division of Pulmonary and Critical Care, Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Cindy Cheng
- Department of Family and Community Medicine, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Vanessa Aguilar
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Sneha Khan
- Department of Osteopathic Medicine, Arkansas State University, Jonesboro, Arkansas, USA
| | - Mira Sankhavaram
- School of Nursing, University of Texas at Austin, Austin, Texas, USA
| | - Alexandra L Hanlon
- Department of Statistics, Center for Biostatistics and Health Data Science, Virginia Tech, Roanoke, Virginia, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nalaka Gooneratne
- Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Hrybouski S, Das SR, Xie L, Wisse LEM, Kelley M, Lane J, Sherin M, DiCalogero M, Nasrallah I, Detre J, Yushkevich PA, Wolk DA. Aging and Alzheimer's disease have dissociable effects on local and regional medial temporal lobe connectivity. Brain Commun 2023; 5:fcad245. [PMID: 37767219 PMCID: PMC10521906 DOI: 10.1093/braincomms/fcad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 08/06/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighbourhood-the Anterior-Temporal and Posterior-Medial brain networks-in normal agers, individuals with preclinical Alzheimer's disease and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbours in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (i) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (ii) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and (iii) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging versus Alzheimer's disease.
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Affiliation(s)
- Stanislau Hrybouski
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Diagnostic Radiology, Lund University, 221 00 Lund, Sweden
| | - Melissa Kelley
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jacqueline Lane
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Monica Sherin
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael DiCalogero
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ilya Nasrallah
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Detre
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Alzheimer’s Disease Research Center, University of Pennsylvania, Philadelphia, PA 19104, USA
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31
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Nelson PT, Schneider JA, Jicha GA, Duong MT, Wolk DA. When Alzheimer's is LATE: Why Does it Matter? Ann Neurol 2023; 94:211-222. [PMID: 37245084 PMCID: PMC10516307 DOI: 10.1002/ana.26711] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/07/2023] [Accepted: 05/18/2023] [Indexed: 05/29/2023]
Abstract
Recent therapeutic advances provide heightened motivation for accurate diagnosis of the underlying biologic causes of dementia. This review focuses on the importance of clinical recognition of limbic-predominant age-related TDP-43 encephalopathy (LATE). LATE affects approximately one-quarter of older adults and produces an amnestic syndrome that is commonly mistaken for Alzheimer's disease (AD). Although AD and LATE often co-occur in the same patients, these diseases differ in the protein aggregates driving neuropathology (Aβ amyloid/tau vs TDP-43). This review discusses signs and symptoms, relevant diagnostic testing, and potential treatment implications for LATE that may be helpful for physicians, patients, and families. ANN NEUROL 2023;94:211-222.
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Affiliation(s)
| | | | | | | | - David A. Wolk
- University of Pennsylvania Alzheimer’s Disease Research Center
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32
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Young AL, Vogel JW, Robinson JL, McMillan CT, Ossenkoppele R, Wolk DA, Irwin DJ, Elman L, Grossman M, Lee VMY, Lee EB, Hansson O. Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies. Brain 2023; 146:2975-2988. [PMID: 37150879 PMCID: PMC10317181 DOI: 10.1093/brain/awad145] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/27/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
TAR DNA-binding protein-43 (TDP-43) accumulation is the primary pathology underlying several neurodegenerative diseases. Charting the progression and heterogeneity of TDP-43 accumulation is necessary to better characterize TDP-43 proteinopathies, but current TDP-43 staging systems are heuristic and assume each syndrome is homogeneous. Here, we use data-driven disease progression modelling to derive a fine-grained empirical staging system for the classification and differentiation of frontotemporal lobar degeneration due to TDP-43 (FTLD-TDP, n = 126), amyotrophic lateral sclerosis (ALS, n = 141) and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) with and without Alzheimer's disease (n = 304). The data-driven staging of ALS and FTLD-TDP complement and extend previously described human-defined staging schema for ALS and behavioural variant frontotemporal dementia. In LATE-NC individuals, progression along data-driven stages was positively associated with age, but negatively associated with age in individuals with FTLD-TDP. Using only regional TDP-43 severity, our data driven model distinguished individuals diagnosed with ALS, FTLD-TDP or LATE-NC with a cross-validated accuracy of 85.9%, with misclassifications associated with mixed pathological diagnosis, age and genetic mutations. Adding age and SuStaIn stage to this model increased accuracy to 92.3%. Our model differentiates LATE-NC from FTLD-TDP, though some overlap was observed between late-stage LATE-NC and early-stage FTLD-TDP. We further tested for the presence of subtypes with distinct regional TDP-43 progression patterns within each diagnostic group, identifying two distinct cortical-predominant and brainstem-predominant subtypes within FTLD-TDP and a further two subcortical-predominant and corticolimbic-predominant subtypes within ALS. The FTLD-TDP subtypes exhibited differing proportions of TDP-43 type, while there was a trend for age differing between ALS subtypes. Interestingly, a negative relationship between age and SuStaIn stage was seen in the brainstem/subcortical-predominant subtype of each proteinopathy. No subtypes were observed for the LATE-NC group, despite aggregating individuals with and without Alzheimer's disease and a larger sample size for this group. Overall, we provide an empirical pathological TDP-43 staging system for ALS, FTLD-TDP and LATE-NC, which yielded accurate classification. We further demonstrate that there is substantial heterogeneity amongst ALS and FTLD-TDP progression patterns that warrants further investigation in larger cross-cohort studies.
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Affiliation(s)
- Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1V 6LJ, UK
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, SE-222 42 Lund, Sweden
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
| | - John L Robinson
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - David A Wolk
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - David J Irwin
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lauren Elman
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Virginia M Y Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
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Taghvaei M, Cook P, Sadaghiani S, Shakibajahromi B, Tackett W, Dolui S, De D, Brown C, Khandelwal P, Yushkevich P, Das S, Wolk DA, Detre JA. Young versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography. Hum Brain Mapp 2023; 44:3943-3953. [PMID: 37148501 PMCID: PMC10258527 DOI: 10.1002/hbm.26326] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/12/2023] [Accepted: 04/17/2023] [Indexed: 05/08/2023] Open
Abstract
White matter hyperintensity (WMH) lesions on T2 fluid-attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) and changes in adjacent normal-appearing white matter can disrupt computerized tract reconstruction and result in inaccurate measures of structural brain connectivity. The virtual lesion approach provides an alternative strategy for estimating structural connectivity changes due to WMH. To assess the impact of using young versus older subject diffusion MRI data for virtual lesion tractography, we leveraged recently available diffusion MRI data from the Human Connectome Project (HCP) Lifespan database. Neuroimaging data from 50 healthy young (39.2 ± 1.6 years) and 46 healthy older (74.2 ± 2.5 years) subjects were obtained from the publicly available HCP-Aging database. Three WMH masks with low, moderate, and high lesion burdens were extracted from the WMH lesion frequency map of locally acquired FLAIR MRI data. Deterministic tractography was conducted to extract streamlines in 21 WM bundles with and without the WMH masks as regions of avoidance in both young and older cohorts. For intact tractography without virtual lesion masks, 7 out of 21 WM pathways showed a significantly lower number of streamlines in older subjects compared to young subjects. A decrease in streamline count with higher native lesion burden was found in corpus callosum, corticostriatal tract, and fornix pathways. Comparable percentages of affected streamlines were obtained in young and older groups with virtual lesion tractography using the three WMH lesion masks of increasing severity. We conclude that using normative diffusion MRI data from young subjects for virtual lesion tractography of WMH is, in most cases, preferable to using age-matched normative data.
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Affiliation(s)
- Mohammad Taghvaei
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip Cook
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Shokufeh Sadaghiani
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | | | - William Tackett
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sudipto Dolui
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Debarun De
- Department of Computer EngineeringUniversity of IllinoisUrbanaIllinoisUSA
| | - Christopher Brown
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Pulkit Khandelwal
- Department of BioengineeringUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Paul Yushkevich
- Department of RadiologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandhitsu Das
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John A. Detre
- Department of NeurologyUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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34
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Wolk DA, Rabinovici GD, Dickerson BC. A Step Forward in the Fight Against Dementia-Are We There Yet? JAMA Neurol 2023; 80:429-430. [PMID: 36912845 PMCID: PMC10979367 DOI: 10.1001/jamaneurol.2023.0123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2023]
Abstract
This Viewpoint reports on the results of the Clarity AD trial, a phase 3 randomized clinical trial of lecanemab for patients with early Alzheimer disease, in which lecanemab’s clinical efficacy was demonstrated using well-established outcome measures.
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Affiliation(s)
- David A. Wolk
- Penn Alzheimer’s Disease Research Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA
| | - Gil D. Rabinovici
- Penn Alzheimer’s Disease Research Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia PA
| | - Bradford C. Dickerson
- Frontotemporal Disorders Unit and Alzheimer’s Disease Research Center, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston MA
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35
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Valentino RR, Scotton WJ, Roemer SF, Lashley T, Heckman MG, Shoai M, Martinez-Carrasco A, Tamvaka N, Walton RL, Baker MC, Macpherson HL, Real R, Soto-Beasley AI, Mok K, Revesz T, Warner TT, Jaunmuktane Z, Boeve BF, Christopher EA, DeTure M, Duara R, Graff-Radford NR, Josephs KA, Knopman DS, Koga S, Murray ME, Lyons KE, Pahwa R, Parisi JE, Petersen RC, Whitwell J, Grinberg LT, Miller B, Schlereth A, Seeley WW, Spina S, Grossman M, Irwin DJ, Lee EB, Suh E, Trojanowski JQ, Van Deerlin VM, Wolk DA, Connors TR, Dooley PM, Frosch MP, Oakley DH, Aldecoa I, Balasa M, Gelpi E, Borrego-Écija S, de Eugenio Huélamo RM, Gascon-Bayarri J, Sánchez-Valle R, Sanz-Cartagena P, Piñol-Ripoll G, Molina-Porcel L, Bigio EH, Flanagan ME, Gefen T, Rogalski EJ, Weintraub S, Redding-Ochoa J, Chang K, Troncoso JC, Prokop S, Newell KL, Ghetti B, Jones M, Richardson A, Robinson AC, Roncaroli F, Snowden J, Allinson K, Green O, Rowe JB, Singh P, Beach TG, Serrano GE, Flowers XE, Goldman JE, Heaps AC, Leskinen SP, Teich AF, Black SE, Keith JL, Masellis M, Bodi I, King A, Sarraj SA, Troakes C, Halliday GM, Hodges JR, Kril JJ, Kwok JB, Piguet O, Gearing M, Arzberger T, Roeber S, Attems J, Morris CM, Thomas AJ, Evers BM, White CL, Mechawar N, Sieben AA, Cras PP, De Vil BB, De Deyn PPP, Duyckaerts C, Le Ber I, Seihean D, Turbant-Leclere S, MacKenzie IR, McLean C, Cykowski MD, Ervin JF, Wang SHJ, Graff C, Nennesmo I, Nagra RM, Riehl J, Kovacs GG, Giaccone G, Nacmias B, Neumann M, Ang LC, Finger EC, Blauwendraat C, Nalls MA, Singleton AB, Vitale D, Cunha C, Carvalho A, Wszolek ZK, Morris HR, Rademakers R, Hardy JA, Dickson DW, Rohrer JD, Ross OA. Creating the Pick's disease International Consortium: Association study of MAPT H2 haplotype with risk of Pick's disease. medRxiv 2023:2023.04.17.23288471. [PMID: 37163045 PMCID: PMC10168402 DOI: 10.1101/2023.04.17.23288471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Pick's disease (PiD) is a rare and predominantly sporadic form of frontotemporal dementia that is classified as a primary tauopathy. PiD is pathologically defined by argyrophilic inclusion Pick bodies and ballooned neurons in the frontal and temporal brain lobes. PiD is characterised by the presence of Pick bodies which are formed from aggregated, hyperphosphorylated, 3-repeat tau proteins, encoded by the MAPT gene. The MAPT H2 haplotype has consistently been associated with a decreased disease risk of the 4-repeat tauopathies of progressive supranuclear palsy and corticobasal degeneration, however its role in susceptibility to PiD is unclear. The primary aim of this study was to evaluate the association between MAPT H2 and risk of PiD. Methods We established the Pick's disease International Consortium (PIC) and collected 338 (60.7% male) pathologically confirmed PiD brains from 39 sites worldwide. 1,312 neurologically healthy clinical controls were recruited from Mayo Clinic Jacksonville, FL (N=881) or Rochester, MN (N=431). For the primary analysis, subjects were directly genotyped for MAPT H1-H2 haplotype-defining variant rs8070723. In secondary analysis, we genotyped and constructed the six-variant MAPT H1 subhaplotypes (rs1467967, rs242557, rs3785883, rs2471738, rs8070723, and rs7521). Findings Our primary analysis found that the MAPT H2 haplotype was associated with increased risk of PiD (OR: 1.35, 95% CI: 1.12-1.64 P=0.002). In secondary analysis involving H1 subhaplotypes, a protective association with PiD was observed for the H1f haplotype (0.0% vs. 1.2%, P=0.049), with a similar trend noted for H1b (OR: 0.76, 95% CI: 0.58-1.00, P=0.051). The 4-repeat tauopathy risk haplotype MAPT H1c was not associated with PiD susceptibility (OR: 0.93, 95% CI: 0.70-1.25, P=0.65). Interpretation The PIC represents the first opportunity to perform relatively large-scale studies to enhance our understanding of the pathobiology of PiD. This study demonstrates that in contrast to its protective role in 4R tauopathies, the MAPT H2 haplotype is associated with an increased risk of PiD. This finding is critical in directing isoform-related therapeutics for tauopathies.
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Affiliation(s)
| | - William J Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
| | - Shanu F Roemer
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Tammaryn Lashley
- Queen Square Brain Bank for Neurological Disorders, University College London, Queen Square Institute of Neurology London, UK
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
| | - Michael G Heckman
- Division of Clinical Trials and Biostatistics, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Maryam Shoai
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
| | - Alejandro Martinez-Carrasco
- Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Nicole Tamvaka
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Ronald L Walton
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Matthew C Baker
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Hannah L Macpherson
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
| | - Raquel Real
- Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | | | - Kin Mok
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
| | - Tamas Revesz
- Queen Square Brain Bank for Neurological Disorders, University College London, Queen Square Institute of Neurology London, UK
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
| | - Thomas T Warner
- Queen Square Brain Bank for Neurological Disorders, University College London, Queen Square Institute of Neurology London, UK
- Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Zane Jaunmuktane
- Queen Square Brain Bank for Neurological Disorders, University College London, Queen Square Institute of Neurology London, UK
- Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Michael DeTure
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Ranjan Duara
- Wien Center for Alzheimer’s Disease and Memory Disorders, Mount Sinai Medical Center Miami Beach, FL
| | | | - Keith A Josephs
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Shunsuke Koga
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Kelly E Lyons
- University of Kansas Medical Center, Parkinson’s Disease & Movement Disorder Division, Kansas City, KS. 66160
| | - Rajesh Pahwa
- University of Kansas Medical Center, Parkinson’s Disease & Movement Disorder Division, Kansas City, KS. 66160
| | - Joseph E Parisi
- Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | - Lea T Grinberg
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Athena Schlereth
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - William W Seeley
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Salvatore Spina
- Department of Neurology, Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Murray Grossman
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David J Irwin
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - EunRan Suh
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Vivianna M Van Deerlin
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theresa R Connors
- Neuropathology Service, C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Patrick M Dooley
- Neuropathology Service, C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Matthew P Frosch
- Neuropathology Service, C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Derek H Oakley
- Neuropathology Service, C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA
| | - Iban Aldecoa
- Pathology, BDC, Hospital Clinic de Barcelona, Barcelona, Spain
- University of Barcelona, Barcelona, Spain
- Neurological Tissue Bank, Biobanc-Hospital Clínic-FRCB-IDIBAPS, Barcelona, Spain
| | - Mircea Balasa
- Alzheimer’s Disease and other Cognitive Disorders Unit, Neurology Department, Hospital Clinic, Barcelona, Spain
- Barcelona Clinical Research Foundation-August Pi i Sunyer Biomedical Research Institute (FRCB-IDIBAPS), Barcelona, Spain
| | - Ellen Gelpi
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Sergi Borrego-Écija
- University of Barcelona, Barcelona, Spain
- Alzheimer’s Disease and other Cognitive Disorders Unit, Neurology Department, Hospital Clinic, Barcelona, Spain
- Barcelona Clinical Research Foundation-August Pi i Sunyer Biomedical Research Institute (FRCB-IDIBAPS), Barcelona, Spain
| | | | - Jordi Gascon-Bayarri
- Servei de Neurologia, Hospital Universitari de Bellvitge. Institut d’Investigació Biomèdica de Bellvitge (Idibell). L’Hospitalet de Llobregat, Spain
| | - Raquel Sánchez-Valle
- University of Barcelona, Barcelona, Spain
- Alzheimer’s Disease and other Cognitive Disorders Unit, Neurology Department, Hospital Clinic, Barcelona, Spain
- Barcelona Clinical Research Foundation-August Pi i Sunyer Biomedical Research Institute (FRCB-IDIBAPS), Barcelona, Spain
| | | | - Gerard Piñol-Ripoll
- Unitat Trastorns Cognitius (Cognitive Disorders Unit), Clinical Neuroscience Research, IRBLleida, Santa Maria University Hospital, Lleida, Spain
| | - Laura Molina-Porcel
- Neurological Tissue Bank, Biobanc-Hospital Clínic-FRCB-IDIBAPS, Barcelona, Spain
- Alzheimer’s Disease and other Cognitive Disorders Unit, Neurology Department, Hospital Clinic, Barcelona, Spain
- Barcelona Clinical Research Foundation-August Pi i Sunyer Biomedical Research Institute (FRCB-IDIBAPS), Barcelona, Spain
| | - Eileen H Bigio
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Margaret E Flanagan
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Tamar Gefen
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Emily J Rogalski
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sandra Weintraub
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Koping Chang
- Johns Hopkins School of Medicine, Baltimore, MD, USA
| | | | - Stefan Prokop
- Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Kathy L Newell
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana 46202, USA
| | - Matthew Jones
- Cerebral Function Unit, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, UK
- Division of Neuroscience, School of Biological Sciences, University of Manchester, UK
| | - Anna Richardson
- Cerebral Function Unit, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, UK
- Division of Neuroscience, School of Biological Sciences, University of Manchester, UK
| | - Andrew C Robinson
- Division of Neuroscience, Faculty of Biology, Medicine and Health, School of Biological Sciences, The University of Manchester, Salford Royal Hospital, Salford, M6 8HD, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre (MAHSC), Manchester, UK
| | - Federico Roncaroli
- Division of Neuroscience, Faculty of Biology, Medicine and Health, School of Biological Sciences, The University of Manchester, Salford Royal Hospital, Salford, M6 8HD, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre (MAHSC), Manchester, UK
| | - Julie Snowden
- Cerebral Function Unit, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, UK
- Division of Neuroscience, School of Biological Sciences, University of Manchester, UK
| | - Kieren Allinson
- Histopathology Box 235 Cambridge University Hospital NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ
| | - Oliver Green
- Histopathology Box 235 Cambridge University Hospital NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ
| | - James B Rowe
- Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - Poonam Singh
- Histopathology Box 235 Cambridge University Hospital NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ
| | - Thomas G Beach
- Civin Laboratory of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Geidy E Serrano
- Civin Laboratory of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ 85351, USA
| | - Xena E Flowers
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
| | - James E Goldman
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA
| | - Allison C Heaps
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
| | - Sandra P Leskinen
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
| | - Andrew F Teich
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, NY, USA
- Department of Pathology and Cell Biology, Columbia University, New York, NY, USA
| | - Sandra E Black
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre and University of Toronto, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute
| | - Julia L Keith
- Laboratory Medicine and Molecular Diagnostics, Sunnybrook Health Sciences Centre, and Laboratory Medicine and Pathobiology, University of Toronto
| | - Mario Masellis
- Department of Medicine, Division of Neurology, Sunnybrook Health Sciences Centre and University of Toronto, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute
| | - Istvan Bodi
- Clinical Neuropathology Department, King’s College Hospital NHS Foundation Trust, London, UK
- London Neurodegenerative Diseases Brain Bank, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Andrew King
- Clinical Neuropathology Department, King’s College Hospital NHS Foundation Trust, London, UK
- London Neurodegenerative Diseases Brain Bank, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Safa-Al Sarraj
- Clinical Neuropathology Department, King’s College Hospital NHS Foundation Trust, London, UK
- London Neurodegenerative Diseases Brain Bank, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Claire Troakes
- London Neurodegenerative Diseases Brain Bank, Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Glenda M Halliday
- University of Sydney Brain and Mind Centre and Faculty of Medicine and Health School of Medical Sciences
| | - John R Hodges
- University of Sydney Brain and Mind Centre and Faculty of Medicine and Health School of Medical Sciences
| | - Jillian J Kril
- University of Sydney Faculty of Medicine and Health School of Medical Sciences
| | - John B Kwok
- University of Sydney Brain and Mind Centre and Faculty of Medicine and Health School of Medical Sciences
| | - Olivier Piguet
- University of Sydney Brain and Mind Centre and Faculty of Science School of Psychology
| | - Marla Gearing
- Dept. of Pathology and Laboratory Medicine, Dept. of Neurology, and Goizueta Alzheimer’s Disease Center Brain Bank; Emory University School of Medicine, Atlanta, GA USA
| | - Thomas Arzberger
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilians-University Munich, Germany
| | - Sigrun Roeber
- Center for Neuropathology and Prion Research, Ludwig-Maximilians-University Munich, Germany
| | - Johannes Attems
- Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Christopher M Morris
- Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Alan J Thomas
- Newcastle Brain Tissue Resource, Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK
| | - Bret M. Evers
- University of Texas Southwestern Medical Center, Dallas, TX 75390
| | - Charles L White
- University of Texas Southwestern Medical Center, Dallas, TX 75390
| | | | - Anne A Sieben
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Pathology, Antwerp University Hospital, Antwerp, Belgium
- Department of Neurology, Ghent University Hospital, Ghent University, Belgium
| | - Patrick P Cras
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Neurology, Antwerp University Hospital - UZA, Antwerp, Belgium
| | - Bart B De Vil
- Laboratory of Neurology, Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
- IBB-NeuroBiobank BB190113, Born Bunge Institute, Antwerp, Belgium
- Department of Neurology, Antwerp University Hospital - UZA, Antwerp, Belgium
| | - Peter Paul P.P. De Deyn
- Laboratory of Neurochemistry and Behavior, Experimental Neurobiology Unit, University of Antwerp, Universiteitsplein 1, 2610 Antwerpen, Belgium
| | - Charles Duyckaerts
- Laboratoire de Neuropathologie Escourolle, Hôpital de la Salpêtrière, AP-HP, & Alzheimer Prion Team, ICM, 47 Bd de l’Hôpital, 75651 CEDEX 13 Paris, France
| | - Isabelle Le Ber
- Inserm U1127, CNRS UMR 7225, Sorbonne Université, Paris Brain Institute (ICM), Hôpital Pitié-Salpêtrière, Paris, France
- Centre de référence des démences rares ou précoces, Hôpital Pitié-Salpêtrière, Paris, France
| | - Danielle Seihean
- Laboratoire de Neuropathologie Escourolle, Hôpital de la Salpêtrière, AP-HP, & ICM, 47 Bd de l’Hôpital, 75651 CEDEX 13 Paris, France
| | - Sabrina Turbant-Leclere
- Inserm U1127, CNRS UMR 7225, Sorbonne Université, Paris Brain Institute (ICM) Hôpital Pitié-Salpêtrière, Paris, France
| | - Ian R MacKenzie
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada V6T 2B5
| | - Catriona McLean
- Department of Anatomical Pathology Alfred Heath, Melbourne, Victoria, 3004, Australia
- Victorian Brain Bank, The Florey Institute of Neuroscience of Mental Health, Parkville, Victoria, 3052, Australia
| | - Matthew D Cykowski
- Department of Pathology and Genomic Medicine, Houston Methodist Research Institute and Weill Cornell Medicine, Houston, TX
| | - John F Ervin
- Department of Neurology, Duke University Medical Center, Durham, USA
| | - Shih-Hsiu J Wang
- Department of Neurology, Duke University Medical Center, Durham, USA
| | - Caroline Graff
- Division for Neurogeriatrics, Centre for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
- Unit for Hereditary Dementias, Karolinska University Hospital Solna, Stockholm, Sweden
| | - Inger Nennesmo
- Dept of laboratory Medicine Huddinge Karolinska Institutet, Stockholm Sweden
- Dept of Pathology, Karolinska University Hospital Solna, Stockholm, Sweden
| | - Rashed M Nagra
- Human Brain and Spinal Fluid Resource Center, Brentwood Biomedical Research Institute, Los Angeles, CA, United States
| | | | - Gabor G Kovacs
- Tanz Centre for Research in Neurodegenerative Disease (CRND) and Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Laboratory Medicine Program and Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | | | - Benedetta Nacmias
- Department of Neuroscience, Psychology, Drug Research and Child Health University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Manuela Neumann
- Molecular Neuropathology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
- Department of Neuropathology, University Hospital of Tübingen, Tübingen, Germany
| | - Lee-Cyn Ang
- Department of Pathology and Laboratory Medicine, London Health Sciences Centre, London, ON, Canada
- Schulich School of Medicine and Dentistry, Western University, London. ON, Canada
| | - Elizabeth C Finger
- Department of Clinical Neurological Sciences, Western University, London, ON, Canada
- Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Cornelis Blauwendraat
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Mike A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Washington, DC, USA
| | - Andrew B Singleton
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Dan Vitale
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Center for Alzheimer’s and Related Dementias, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International LLC, Washington, DC, USA
| | - Cristina Cunha
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Agostinho Carvalho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
- ICVS/3B’s - PT Government Associate Laboratory, Braga/Guimarães, Portugal
| | | | - Huw R Morris
- Department of Clinical and Movement Neurosciences, University College London, Queen Square Institute of Neurology, London, UK
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
- VIBUAntwerp Center for Molecular Neurology, University of Antwerp, Antwerp 2610, Belgium
| | - John A Hardy
- Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Reta Lila Weston Institute, University College London, Queen Square Institute of Neurology, London, UK
- Institute for Advanced Study, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, UK
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
- Department of Clinical Genomics, Mayo Clinic, Jacksonville, FL 32224, USA
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Xie L, Das SR, Wisse LEM, Ittyerah R, de Flores R, Shaw LM, Yushkevich PA, Wolk DA. Baseline structural MRI and plasma biomarkers predict longitudinal structural atrophy and cognitive decline in early Alzheimer's disease. Alzheimers Res Ther 2023; 15:79. [PMID: 37041649 PMCID: PMC10088234 DOI: 10.1186/s13195-023-01210-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 03/13/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND Crucial to the success of clinical trials targeting early Alzheimer's disease (AD) is recruiting participants who are more likely to progress over the course of the trials. We hypothesize that a combination of plasma and structural MRI biomarkers, which are less costly and non-invasive, is predictive of longitudinal progression measured by atrophy and cognitive decline in early AD, providing a practical alternative to PET or cerebrospinal fluid biomarkers. METHODS Longitudinal T1-weighted MRI, cognitive (memory-related test scores and clinical dementia rating scale), and plasma measurements of 245 cognitively normal (CN) and 361 mild cognitive impairment (MCI) patients from ADNI were included. Subjects were further divided into β-amyloid positive/negative (Aβ+/Aβ-)] subgroups. Baseline plasma (p-tau181 and neurofilament light chain) and MRI-based structural medial temporal lobe subregional measurements and their association with longitudinal measures of atrophy and cognitive decline were tested using stepwise linear mixed effect modeling in CN and MCI, as well as separately in the Aβ+/Aβ- subgroups. Receiver operating characteristic (ROC) analyses were performed to investigate the discriminative power of each model in separating fast and slow progressors (first and last terciles) of each longitudinal measurement. RESULTS A total of 245 CN (35.0% Aβ+) and 361 MCI (53.2% Aβ+) participants were included. In the CN and MCI groups, both baseline plasma and structural MRI biomarkers were included in most models. These relationships were maintained when limited to the Aβ+ and Aβ- subgroups, including Aβ- CN (normal aging). ROC analyses demonstrated reliable discriminative power in identifying fast from slow progressors in MCI [area under the curve (AUC): 0.78-0.93] and more modestly in CN (0.65-0.73). CONCLUSIONS The present data support the notion that plasma and MRI biomarkers, which are relatively easy to obtain, provide a prediction for the rate of future cognitive and neurodegenerative progression that may be particularly useful in clinical trial stratification and prognosis. Additionally, the effect in Aβ- CN indicates the potential use of these biomarkers in predicting a normal age-related decline.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA.
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Ranjit Ittyerah
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - Robin de Flores
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, 3700 Hamilton Walk, Suite D600, Richards Building 6th floor, Philadelphia, PA, 19104, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
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Dong M, Xie L, Das SR, Wang J, Wisse LEM, deFlores R, Wolk DA, Yushkevich PA. Regional Deep Atrophy: a Self-Supervised Learning Method to Automatically Identify Regions Associated With Alzheimer's Disease Progression From Longitudinal MRI. ArXiv 2023:arXiv:2304.04673v1. [PMID: 37090239 PMCID: PMC10120742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Longitudinal assessment of brain atrophy, particularly in the hippocampus, is a well-studied biomarker for neurodegenerative diseases, such as Alzheimer's disease (AD). In clinical trials, estimation of brain progressive rates can be applied to track therapeutic efficacy of disease modifying treatments. However, most state-of-the-art measurements calculate changes directly by segmentation and/or deformable registration of MRI images, and may misreport head motion or MRI artifacts as neurodegeneration, impacting their accuracy. In our previous study, we developed a deep learning method DeepAtrophy that uses a convolutional neural network to quantify differences between longitudinal MRI scan pairs that are associated with time. DeepAtrophy has high accuracy in inferring temporal information from longitudinal MRI scans, such as temporal order or relative inter-scan interval. DeepAtrophy also provides an overall atrophy score that was shown to perform well as a potential biomarker of disease progression and treatment efficacy. However, DeepAtrophy is not interpretable, and it is unclear what changes in the MRI contribute to progression measurements. In this paper, we propose Regional Deep Atrophy (RDA), which combines the temporal inference approach from DeepAtrophy with a deformable registration neural network and attention mechanism that highlights regions in the MRI image where longitudinal changes are contributing to temporal inference. RDA has similar prediction accuracy as DeepAtrophy, but its additional interpretability makes it more acceptable for use in clinical settings, and may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.
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Affiliation(s)
- Mengjin Dong
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Sandhitsu R Das
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Laura E M Wisse
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Robin deFlores
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Institut National de la Santé et de la Recherche Médicale (INSERM), Caen, France
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
- Penn Memory Center, University of Pennsylvania, Philadelphia, Pennsylvania, United States
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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Zhou Z, Li H, Srinivasan D, Abdulkadir A, Nasrallah IM, Wen J, Doshi J, Erus G, Mamourian E, Bryan NR, Wolk DA, Beason-Held L, Resnick SM, Satterthwaite TD, Davatzikos C, Shou H, Fan Y. Multiscale functional connectivity patterns of the aging brain learned from harmonized rsfMRI data of the multi-cohort iSTAGING study. Neuroimage 2023; 269:119911. [PMID: 36731813 PMCID: PMC9992322 DOI: 10.1016/j.neuroimage.2023.119911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/06/2023] [Accepted: 01/28/2023] [Indexed: 02/03/2023] Open
Abstract
To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4186 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and the data harmonization in the functional connectivity measures' tangent space worked better than in their original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.
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Affiliation(s)
- Zhen Zhou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Hongming Li
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dhivya Srinivasan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Junhao Wen
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jimit Doshi
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Guray Erus
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Elizabeth Mamourian
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Nick R Bryan
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Diagnostic Medicine, University of Texas at Austin, Austin, TX, 78705, USA
| | - David A Wolk
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, 20892, USA
| | - Theodore D Satterthwaite
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Psychiatry, Brain Behavior Laboratory and Penn-CHOP Lifespan Brain Institute, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Haochang Shou
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn Statistic in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yong Fan
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Cousins KAQ, Irwin DJ, Chen-Plotkin A, Shaw LM, Arezoumandan S, Lee EB, Wolk DA, Weintraub D, Spindler M, Deik A, Grossman M, Tropea TF. Plasma GFAP associates with secondary Alzheimer's pathology in Lewy body disease. Ann Clin Transl Neurol 2023; 10:802-813. [PMID: 37000892 DOI: 10.1002/acn3.51768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/13/2023] [Accepted: 03/14/2023] [Indexed: 04/03/2023] Open
Abstract
OBJECTIVE Within Lewy body spectrum disorders (LBSD) with α-synuclein pathology (αSyn), concomitant Alzheimer's disease (AD) pathology is common and is predictive of clinical outcomes, including cognitive impairment and decline. Plasma phosphorylated tau 181 (p-tau181 ) is sensitive to AD neuropathologic change (ADNC) in clinical AD, and plasma glial fibrillary acidic protein (GFAP) is associated with the presence of β-amyloid plaques. While these plasma biomarkers are well tested in clinical and pathological AD, their diagnostic and prognostic performance for concomitant AD in LBSD is unknown. METHODS In autopsy-confirmed αSyn-positive LBSD, we tested how plasma p-tau181 and GFAP differed across αSyn with concomitant ADNC (αSyn+AD; n = 19) and αSyn without AD (αSyn; n = 30). Severity of burden was scored on a semiquantitative scale for several pathologies (e.g., β-amyloid and tau), and scores were averaged across sampled brainstem, limbic, and neocortical regions. RESULTS Linear models showed that plasma GFAP was significantly higher in αSyn+AD compared to αSyn (β = 0.31, 95% CI = 0.065-0.56, and P = 0.015), after covarying for age at plasma, plasma-to-death interval, and sex; plasma p-tau181 was not (P = 0.37). Next, linear models tested associations of AD pathological features with both plasma analytes, covarying for plasma-to-death, age at plasma, and sex. GFAP was significantly associated with brain β-amyloid (β = 15, 95% CI = 6.1-25, and P = 0.0018) and tau burden (β = 12, 95% CI = 2.5-22, and P = 0.015); plasma p-tau181 was not associated with either (both P > 0.34). INTERPRETATION Findings indicate that plasma GFAP may be sensitive to concomitant AD pathology in LBSD, especially accumulation of β-amyloid plaques.
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Affiliation(s)
- Katheryn A Q Cousins
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - David J Irwin
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Alice Chen-Plotkin
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sanaz Arezoumandan
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Wolk
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Daniel Weintraub
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Meredith Spindler
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Andres Deik
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Murray Grossman
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Thomas F Tropea
- Department of Neurology, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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Insausti R, Insausti AM, Muñoz López M, Medina Lorenzo I, Arroyo-Jiménez MDM, Marcos Rabal MP, de la Rosa-Prieto C, Delgado-González JC, Montón Etxeberria J, Cebada-Sánchez S, Raspeño-García JF, Iñiguez de Onzoño MM, Molina Romero FJ, Benavides-Piccione R, Tapia-González S, Wisse LEM, Ravikumar S, Wolk DA, DeFelipe J, Yushkevich P, Artacho-Pérula E. Ex vivo, in situ perfusion protocol for human brain fixation compatible with microscopy, MRI techniques, and anatomical studies. Front Neuroanat 2023; 17:1149674. [PMID: 37034833 PMCID: PMC10076536 DOI: 10.3389/fnana.2023.1149674] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 02/28/2023] [Indexed: 04/11/2023] Open
Abstract
We present a method for human brain fixation based on simultaneous perfusion of 4% paraformaldehyde through carotids after a flush with saline. The left carotid cannula is used to perfuse the body with 10% formalin, to allow further use of the body for anatomical research or teaching. The aim of our method is to develop a vascular fixation protocol for the human brain, by adapting protocols that are commonly used in experimental animal studies. We show that a variety of histological procedures can be carried out (cyto- and myeloarchitectonics, histochemistry, immunohistochemistry, intracellular cell injection, and electron microscopy). In addition, ex vivo, ex situ high-resolution MRI (9.4T) can be obtained in the same specimens. This procedure resulted in similar morphological features to those obtained by intravascular perfusion in experimental animals, provided that the postmortem interval was under 10 h for several of the techniques used and under 4 h in the case of intracellular injections and electron microscopy. The use of intravascular fixation of the brain inside the skull provides a fixed whole human brain, perfectly fitted to the skull, with negligible deformation compared to conventional techniques. Given this characteristic of ex vivo, in situ fixation, this procedure can probably be considered the most suitable one available for ex vivo MRI scans of the brain. We describe the compatibility of the method proposed for intravascular fixation of the human brain and fixation of the donor's body for anatomical purposes. Thus, body donor programs can provide human brain tissue, while the remainder of the body can also be fixed for anatomical studies. Therefore, this method of human brain fixation through the carotid system optimizes the procurement of human brain tissue, allowing a greater understanding of human neurological diseases, while benefiting anatomy departments by making the remainder of the body available for teaching purposes.
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Affiliation(s)
- Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Ana María Insausti
- Department of Health, School of Medicine, Public University of Navarra, Pamplona, Spain
| | - Mónica Muñoz López
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Isidro Medina Lorenzo
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Maria del Mar Arroyo-Jiménez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - María Pilar Marcos Rabal
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Carlos de la Rosa-Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - José Carlos Delgado-González
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Javier Montón Etxeberria
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Sandra Cebada-Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Juan Francisco Raspeño-García
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - María Mercedes Iñiguez de Onzoño
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Francisco Javier Molina Romero
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
| | - Ruth Benavides-Piccione
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | - Silvia Tapia-González
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | | | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, and Instituto Cajal, CSIC, Madrid, Spain
| | - Paul Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, Medical Sciences Department, School of Medicine and CRIB, University of Castilla La Mancha, Albacete, Spain
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Rashid T, Li K, Toledo JB, Nasrallah I, Pajewski NM, Dolui S, Detre J, Wolk DA, Liu H, Heckbert SR, Bryan RN, Williamson J, Davatzikos C, Seshadri S, Launer LJ, Habes M. Association of Intensive vs Standard Blood Pressure Control With Regional Changes in Cerebral Small Vessel Disease Biomarkers: Post Hoc Secondary Analysis of the SPRINT MIND Randomized Clinical Trial. JAMA Netw Open 2023; 6:e231055. [PMID: 36857053 PMCID: PMC9978954 DOI: 10.1001/jamanetworkopen.2023.1055] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023] Open
Abstract
IMPORTANCE Little is known about the associations of strict blood pressure (BP) control with microstructural changes in small vessel disease markers. OBJECTIVE To investigate the regional associations of intensive vs standard BP control with small vessel disease biomarkers, such as white matter lesions (WMLs), fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF). DESIGN, SETTING, AND PARTICIPANTS The Systolic Blood Pressure Intervention Trial (SPRINT) is a multicenter randomized clinical trial that compared intensive systolic BP (SBP) control (SBP target <120 mm Hg) vs standard control (SBP target <140 mm Hg) among participants aged 50 years or older with hypertension and without diabetes or a history of stroke. The study began randomization on November 8, 2010, and stopped July 1, 2016, with a follow-up duration of approximately 4 years. A total of 670 and 458 participants completed brain magnetic resonance imaging at baseline and follow-up, respectively, and comprise the cohort for this post hoc analysis. Statistical analyses for this post hoc analysis were performed between August 2020 and October 2022. INTERVENTIONS At baseline, 355 participants received intensive SBP treatment and 315 participants received standard SBP treatment. MAIN OUTCOMES AND MEASURES The main outcomes were regional changes in WMLs, FA, MD (in white matter regions of interest), and CBF (in gray matter regions of interest). RESULTS At baseline, 355 participants (mean [SD] age, 67.7 [8.0] years; 200 men [56.3%]) received intensive BP treatment and 315 participants (mean [SD] age, 67.0 [8.4] years; 199 men [63.2%]) received standard BP treatment. Intensive treatment was associated with smaller mean increases in WML volume compared with standard treatment (644.5 mm3 vs 1258.1 mm3). The smaller mean increases were observed specifically in the deep white matter regions of the left anterior corona radiata (intensive treatment, 30.3 mm3 [95% CI, 16.0-44.5 mm3]; standard treatment, 80.5 mm3 [95% CI, 53.8-107.2 mm3]), left tapetum (intensive treatment, 11.8 mm3 [95% CI, 4.4-19.2 mm3]; standard treatment, 27.2 mm3 [95% CI, 19.4-35.0 mm3]), left superior fronto-occipital fasciculus (intensive treatment, 3.2 mm3 [95% CI, 0.7-5.8 mm3]; standard treatment, 9.4 mm3 [95% CI, 5.5-13.4 mm3]), left posterior corona radiata (intensive treatment, 26.0 mm3 [95% CI, 12.9-39.1 mm3]; standard treatment, 52.3 mm3 [95% CI, 34.8-69.8 mm3]), left splenium of the corpus callosum (intensive treatment, 45.4 mm3 [95% CI, 25.1-65.7 mm3]; standard treatment, 83.0 mm3 [95% CI, 58.7-107.2 mm3]), left posterior thalamic radiation (intensive treatment, 53.0 mm3 [95% CI, 29.8-76.2 mm3]; standard treatment, 106.9 mm3 [95% CI, 73.4-140.3 mm3]), and right posterior thalamic radiation (intensive treatment, 49.5 mm3 [95% CI, 24.3-74.7 mm3]; standard treatment, 102.6 mm3 [95% CI, 71.0-134.2 mm3]). CONCLUSIONS AND RELEVANCE This study suggests that intensive BP treatment, compared with standard treatment, was associated with a slower increase of WMLs, improved diffusion tensor imaging, and FA and CBF changes in several brain regions that represent vulnerable areas that may benefit from more strict BP control. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT01206062.
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Affiliation(s)
- Tanweer Rashid
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - Karl Li
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - Jon B. Toledo
- Department of Neurology, University of Florida, Gainesville
- Department of Neurology, Houston Methodist Hospital, Houston, Texas
| | - Ilya Nasrallah
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Nicholas M. Pajewski
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Sudipto Dolui
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
| | - John Detre
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia
| | - Hangfan Liu
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | | | - R. Nick Bryan
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
| | - Jeff Williamson
- Section of Gerontology and Geriatric Medicine, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
| | - Christos Davatzikos
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia
| | - Sudha Seshadri
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
| | - Lenore J. Launer
- Intramural Research Program, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Bethesda, Maryland
| | - Mohamad Habes
- Neuroimage Analytics Laboratory and the Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center San Antonio, San Antonio
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia
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42
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Lyu X, Duong MT, Xie L, de Flores R, Richardson H, Hwang G, Wisse LEM, DiCalogero M, McMillan CT, Robinson JL, Xie SX, Grossman M, Lee EB, Irwin DJ, Dickerson BC, Davatzikos C, Nasrallah IM, Yushkevich PA, Wolk DA, Das SR. Tau-Neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in Alzheimer's continuum. medRxiv 2023:2023.02.12.23285594. [PMID: 36824762 PMCID: PMC9949174 DOI: 10.1101/2023.02.12.23285594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Variability in the relationship of tau-based neurofibrillary tangles (T) and degree of neurodegeneration (N) in Alzheimer's Disease (AD) is likely attributable to the non-specific nature of N, which is also modulated by such factors as other co-pathologies, age-related changes, and developmental differences. We studied this variability by partitioning patients within the Alzheimer's continuum into data-driven groups based on their regional T-N dissociation, which reflects the residuals after the effect of tau pathology is "removed". We found six groups displaying distinct spatial T-N mismatch and thickness patterns despite similar tau burden. Their T-N patterns resembled the neurodegeneration patterns of non-AD groups partitioned on the basis of z-scores of cortical thickness alone and were similarly associated with surrogates of non-AD factors. In an additional sample of individuals with antemortem imaging and autopsy, T-N mismatch was associated with TDP-43 co-pathology. Finally, T-N mismatch training was then applied to a separate cohort to determine the ability to classify individual patients within these groups. These findings suggest that T-N mismatch may provide a personalized approach for determining non-AD factors associated with resilience/vulnerability to Alzheimer's disease.
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Young AL, Vogel JW, Robinson JL, McMillan CT, Ossenkoppele R, Wolk DA, Irwin DJ, Elman L, Grossman M, Lee VMY, Lee EB, Hansson O. Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies. medRxiv 2023:2023.01.31.23285242. [PMID: 36778217 PMCID: PMC9915837 DOI: 10.1101/2023.01.31.23285242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
TAR DNA-binding protein-43 (TDP-43) accumulation is the primary pathology underlying several neurodegenerative diseases. Charting the progression and heterogeneity of TDP-43 accumulation is necessary to better characterise TDP-43 proteinopathies, but current TDP-43 staging systems are heuristic and assume each syndrome is homogeneous. Here, we use data-driven disease progression modelling to derive a fine-grained empirical staging system for the classification and differentiation of frontotemporal lobar degeneration due to TDP-43 (FTLD-TDP, n=126), amyotrophic lateral sclerosis (ALS, n=141) and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) with and without Alzheimer’s disease (n=304). The data-driven staging of ALS and FTLD-TDP complement and extend previously described human-defined staging schema for ALS and behavioural variant frontotemporal dementia. In LATE-NC individuals, progression along data-driven stages was positively associated with age, but negatively associated with age in individuals with FTLD-TDP. Using only regional TDP-43 severity, our data driven model distinguished individuals diagnosed with ALS, FTLD-TDP or LATE-NC with a cross-validated accuracy of 85.9%, with misclassifications associated with mixed pathological diagnosis, age and genetic mutations. Adding age and SuStaIn stage to this model increased accuracy to 92.3%. Our model differentiates LATE-NC from FTLD-TDP, though some overlap was observed between late-stage LATE-NC and early-stage FTLD-TDP. We further tested for the presence of subtypes with distinct regional TDP-43 progression patterns within each diagnostic group, identifying two distinct cortical-predominant and brainstem-predominant subtypes within FTLD-TDP and a further two subcortical-predominant and corticolimbic-predominant subtypes within ALS. The FTLD-TDP subtypes exhibited differing proportions of TDP-43 type, while there was a trend for age differing between ALS subtypes. Interestingly, a negative relationship between age and SuStaIn stage was seen in the brainstem/subcortical-predominant subtype of each proteinopathy. No subtypes were observed for the LATE-NC group, despite aggregating AD+ and AD-individuals and a larger sample size for this group. Overall, we provide an empirical pathological TDP-43 staging system for ALS, FTLD-TDP and LATE-NC, which yielded accurate classification. We further demonstrate that there is substantial heterogeneity amongst ALS and FTLD-TDP progression patterns that warrants further investigation in larger cross-cohort studies.
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Affiliation(s)
- Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - John L Robinson
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Corey T McMillan
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - David A Wolk
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - David J Irwin
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Elman
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Murray Grossman
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Virginia M-Y Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Edward B Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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Yang Z, Wen J, Abdulkadir A, Cui Y, Erus G, Mamourian E, Melhem R, Srinivasan D, Govindarajan ST, Chen J, Habes M, Masters CL, Maruff P, Fripp J, Ferrucci L, Albert MS, Johnson SC, Morris JC, LaMontagne P, Marcus DS, Benzinger TLS, Wolk DA, Shen L, Bao J, Resnick SM, Shou H, Nasrallah IM, Davatzikos C. Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering. ArXiv 2023:arXiv:2301.10772v1. [PMID: 36748000 PMCID: PMC9900969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.
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Hrybouski S, Das SR, Xie L, Wisse LEM, Kelley M, Lane J, Sherin M, DiCalogero M, Nasrallah I, Detre JA, Yushkevich PA, Wolk DA. Aging and Alzheimer's Disease Have Dissociable Effects on Medial Temporal Lobe Connectivity. medRxiv 2023:2023.01.18.23284749. [PMID: 36711782 PMCID: PMC9882834 DOI: 10.1101/2023.01.18.23284749] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Functional disruption of the medial temporal lobe-dependent networks is thought to underlie episodic memory deficits in aging and Alzheimer's disease. Previous studies revealed that the anterior medial temporal lobe is more vulnerable to pathological and neurodegenerative processes in Alzheimer's disease. In contrast, cognitive and structural imaging literature indicates posterior, as opposed to anterior, medial temporal lobe vulnerability in normal aging. However, the extent to which Alzheimer's and aging-related pathological processes relate to functional disruption of the medial temporal lobe-dependent brain networks is poorly understood. To address this knowledge gap, we examined functional connectivity alterations in the medial temporal lobe and its immediate functional neighborhood - the Anterior-Temporal and Posterior-Medial brain networks - in normal agers, individuals with preclinical Alzheimer's disease, and patients with Mild Cognitive Impairment or mild dementia due to Alzheimer's disease. In the Anterior-Temporal network and in the perirhinal cortex, in particular, we observed an inverted 'U-shaped' relationship between functional connectivity and Alzheimer's stage. According to our results, the preclinical phase of Alzheimer's disease is characterized by increased functional connectivity between the perirhinal cortex and other regions of the medial temporal lobe, as well as between the anterior medial temporal lobe and its one-hop neighbors in the Anterior-Temporal system. This effect is no longer present in symptomatic Alzheimer's disease. Instead, patients with symptomatic Alzheimer's disease displayed reduced hippocampal connectivity within the medial temporal lobe as well as hypoconnectivity within the Posterior-Medial system. For normal aging, our results led to three main conclusions: (1) intra-network connectivity of both the Anterior-Temporal and Posterior-Medial networks declines with age; (2) the anterior and posterior segments of the medial temporal lobe become increasingly decoupled from each other with advancing age; and, (3) the posterior subregions of the medial temporal lobe, especially the parahippocampal cortex, are more vulnerable to age-associated loss of function than their anterior counterparts. Together, the current results highlight evolving medial temporal lobe dysfunction in Alzheimer's disease and indicate different neurobiological mechanisms of the medial temporal lobe network disruption in aging vs. Alzheimer's disease.
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Xie L, Wisse LEM, Wang J, Ravikumar S, Khandelwal P, Glenn T, Luther A, Lim S, Wolk DA, Yushkevich PA. Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation. Med Image Anal 2023; 83:102683. [PMID: 36379194 PMCID: PMC10009820 DOI: 10.1016/j.media.2022.102683] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 10/18/2022] [Accepted: 11/03/2022] [Indexed: 11/07/2022]
Abstract
Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN in many applications, tends to generalize well to unseen datasets with different characteristics from the training dataset. Several groups have attempted to integrate the power of DCNN to learn complex data representations and the robustness of MAS to changes in image characteristics. However, these studies primarily focused on replacing individual components of MAS with DCNN models and reported marginal improvements in accuracy. In this study we describe and evaluate a 3D end-to-end hybrid MAS and DCNN segmentation pipeline, called Deep Label Fusion (DLF). The DLF pipeline consists of two main components with learnable weights, including a weighted voting subnet that mimics the MAS algorithm and a fine-tuning subnet that corrects residual segmentation errors to improve final segmentation accuracy. We evaluate DLF on five datasets that represent a diversity of anatomical structures (medial temporal lobe subregions and lumbar vertebrae) and imaging modalities (multi-modality, multi-field-strength MRI and Computational Tomography). These experiments show that DLF achieves comparable segmentation accuracy to nnU-Net (Isensee et al., 2020), the state-of-the-art DCNN pipeline, when evaluated on a dataset with similar characteristics to the training datasets, while outperforming nnU-Net on tasks that involve generalization to datasets with different characteristics (different MRI field strength or different patient population). DLF is also shown to consistently improve upon conventional MAS methods. In addition, a modality augmentation strategy tailored for multimodal imaging is proposed and demonstrated to be beneficial in improving the segmentation accuracy of learning-based methods, including DLF and DCNN, in missing data scenarios in test time as well as increasing the interpretability of the contribution of each individual modality.
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Affiliation(s)
- Long Xie
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA.
| | - Laura E M Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Jiancong Wang
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sadhana Ravikumar
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Pulkit Khandelwal
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Trevor Glenn
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Anica Luther
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Sydney Lim
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - David A Wolk
- Penn Memory Center, University of Pennsylvania, Philadelphia, USA; Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Paul A Yushkevich
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Ko YA, Billheimer JT, Lyssenko NN, Kueider-Paisley A, Wolk DA, Arnold SE, Leung YY, Shaw LM, Trojanowski JQ, Kaddurah-Daouk RF, Kling MA, Rader DJ. ApoJ/Clusterin concentrations are determinants of cerebrospinal fluid cholesterol efflux capacity and reduced levels are associated with Alzheimer's disease. Alzheimers Res Ther 2022; 14:194. [PMID: 36572909 PMCID: PMC9791777 DOI: 10.1186/s13195-022-01119-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 11/06/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) shares risk factors with cardiovascular disease (CVD) and dysregulated cholesterol metabolism is a mechanism common to both diseases. Cholesterol efflux capacity (CEC) is an ex vivo metric of plasma high-density lipoprotein (HDL) function and inversely predicts incident CVD independently of other risk factors. Cholesterol pools in the central nervous system (CNS) are largely separate from those in blood, and CNS cholesterol excess may promote neurodegeneration. CEC of cerebrospinal fluid (CSF) may be a useful measure of CNS cholesterol trafficking. We hypothesized that subjects with AD and mild cognitive impairment (MCI) would have reduced CSF CEC compared with Cognitively Normal (CN) and that CSF apolipoproteins apoA-I, apoJ, and apoE might have associations with CSF CEC. METHODS We retrieved CSF and same-day ethylenediaminetetraacetic acid (EDTA) plasma from 108 subjects (40 AD; 18 MCI; and 50 CN) from the Center for Neurodegenerative Disease Research biobank at the Perelman School of Medicine, University of Pennsylvania. For CSF CEC assays, we used N9 mouse microglial cells and SH-SY5Y human neuroblastoma cells, and the corresponding plasma assay used J774 cells. Cells were labeled with [3H]-cholesterol for 24 h, had ABCA1 expression upregulated for 6 h, were exposed to 33 μl of CSF, and then were incubated for 2.5 h. CEC was quantified as percent [3H]-cholesterol counts in medium of total counts medium+cells, normalized to a pool sample. ApoA-I, ApoJ, ApoE, and cholesterol were also measured in CSF. RESULTS We found that CSF CEC was significantly lower in MCI compared with controls and was poorly correlated with plasma CEC. CSF levels of ApoJ/Clusterin were also significantly lower in MCI and were significantly associated with CSF CEC. While CSF ApoA-I was also associated with CSF CEC, CSF ApoE had no association with CSF CEC. CSF CEC is significantly and positively associated with CSF Aβ. Taken together, ApoJ/Clusterin may be an important determinant of CSF CEC, which in turn could mitigate risk of MCI and AD risk by promoting cellular efflux of cholesterol or other lipids. In contrast, CSF ApoE does not appear to play a role in determining CSF CEC.
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Affiliation(s)
- Yi-An Ko
- grid.25879.310000 0004 1936 8972Division of Translational Medicine and Human Research, Perelman School of Medicine, University of Pennsylvania, 11-125 Smilow Center for Translational Research, 3400 Civic Center Blvd, Philadelphia, PA 19104-5158 USA
| | - Jeffrey T. Billheimer
- grid.25879.310000 0004 1936 8972Division of Translational Medicine and Human Research, Perelman School of Medicine, University of Pennsylvania, 11-125 Smilow Center for Translational Research, 3400 Civic Center Blvd, Philadelphia, PA 19104-5158 USA
| | - Nicholas N. Lyssenko
- grid.264727.20000 0001 2248 3398Alzheimer’s Center at Temple, Department of Neural Sciences, Lewis Katz School of Medicine at Temple University, Philadelphia, PA 19140 USA
| | - Alexandra Kueider-Paisley
- grid.26009.3d0000 0004 1936 7961Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708 USA
| | - David A. Wolk
- grid.25879.310000 0004 1936 8972Department of Neurology, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Steven E. Arnold
- grid.38142.3c000000041936754XDepartment of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Yuk Yee Leung
- grid.25879.310000 0004 1936 8972Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Leslie M. Shaw
- grid.25879.310000 0004 1936 8972Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - John Q. Trojanowski
- grid.25879.310000 0004 1936 8972Division of Translational Medicine and Human Research, Perelman School of Medicine, University of Pennsylvania, 11-125 Smilow Center for Translational Research, 3400 Civic Center Blvd, Philadelphia, PA 19104-5158 USA
| | - Rima F. Kaddurah-Daouk
- grid.26009.3d0000 0004 1936 7961Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708 USA ,grid.26009.3d0000 0004 1936 7961Duke Institute for Brain Sciences, Duke University, Durham, NC 27708 USA ,grid.26009.3d0000 0004 1936 7961Department of Medicine, Duke University, Durham, NC 27708 USA
| | - Mitchel A. Kling
- grid.262671.60000 0000 8828 4546Department of Geriatrics and Gerontology, New Jersey Institute for Successful Aging, Rowan University School of Osteopathic Medicine, 42 E. Laurel Rd., Suite 1800, Stratford, NJ 08084 USA ,grid.25879.310000 0004 1936 8972Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania USA
| | - Daniel J. Rader
- grid.25879.310000 0004 1936 8972Division of Translational Medicine and Human Research, Perelman School of Medicine, University of Pennsylvania, 11-125 Smilow Center for Translational Research, 3400 Civic Center Blvd, Philadelphia, PA 19104-5158 USA
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Ríos AS, Oxenford S, Neudorfer C, Butenko K, Li N, Rajamani N, Boutet A, Elias GJB, Germann J, Loh A, Deeb W, Wang F, Setsompop K, Salvato B, Almeida LBD, Foote KD, Amaral R, Rosenberg PB, Tang-Wai DF, Wolk DA, Burke AD, Salloway S, Sabbagh MN, Chakravarty MM, Smith GS, Lyketsos CG, Okun MS, Anderson WS, Mari Z, Ponce FA, Lozano AM, Horn A. Optimal deep brain stimulation sites and networks for stimulation of the fornix in Alzheimer's disease. Nat Commun 2022; 13:7707. [PMID: 36517479 PMCID: PMC9751139 DOI: 10.1038/s41467-022-34510-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 10/27/2022] [Indexed: 12/15/2022] Open
Abstract
Deep brain stimulation (DBS) to the fornix is an investigational treatment for patients with mild Alzheimer's Disease. Outcomes from randomized clinical trials have shown that cognitive function improved in some patients but deteriorated in others. This could be explained by variance in electrode placement leading to differential engagement of neural circuits. To investigate this, we performed a post-hoc analysis on a multi-center cohort of 46 patients with DBS to the fornix (NCT00658125, NCT01608061). Using normative structural and functional connectivity data, we found that stimulation of the circuit of Papez and stria terminalis robustly associated with cognitive improvement (R = 0.53, p < 0.001). On a local level, the optimal stimulation site resided at the direct interface between these structures (R = 0.48, p < 0.001). Finally, modulating specific distributed brain networks related to memory accounted for optimal outcomes (R = 0.48, p < 0.001). Findings were robust to multiple cross-validation designs and may define an optimal network target that could refine DBS surgery and programming.
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Grants
- P30 AG066507 NIA NIH HHS
- P30 AG072979 NIA NIH HHS
- R01 MH130666 NIMH NIH HHS
- Deutsche Forschungsgemeinschaft (German Research Foundation)
- Received grants and personal fees from Medtronic and Boston Scientific, grants from Abbott/St. Jude, and Functional Neuromodulation outside the submitted work.
- Received grants from Functional Neuromodulation during conduct of this study, grants and personal fees from Avid/Lily, and Merck, personal fees from Jannsen, GE Healthcare, Biogen and Neuronix outside the submitted work.
- Receives personal fees from Elsai, Lilly, Roche Novartis and Biogen outside the submitted work.
- Received personal fees from Allergan, Biogen, Roche-Genentech, Cortexyme, Bracket, Sanofi, and other type of support from Brain Health Inc and uMethod Health outside of the submitted work.
- Received grants from Functional Neuromodulation Inc. during conduct of this study, from Avanir and Eli Lily and NFL Benefits Office outside of the submitted work.
- Received grants from NIH, Tourette Association of America Grant, Parkinson’s Alliance, Smallwood Foundation, and personal fees from Parkinson’s Foundation Medical Director, Books4Patients, American Academy of Neurology, Peerview, WebMD/Medscape, Mededicus, Movement Disorders Society, Taylor and Francis, Demos, Robert Rose and non-financial support from Medtronic outside of the submitted work.
- Received grants from Medtronic and Functional Neuromodulation during conduct of this study, personal fees from Medtronic, St. Jude, Boston Scientific, and Functional Neuromodulation outside of submitted work
- Deutsches Zentrum für Luft- und Raumfahrt (German Centre for Air and Space Travel)
- National Institutes of Health (R01 13478451, 1R01NS127892-01 & 2R01 MH113929) New Venture Fund (FFOR Seed Grant).
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Affiliation(s)
- Ana Sofía Ríos
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Simón Oxenford
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Clemens Neudorfer
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Konstantin Butenko
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ningfei Li
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nanditha Rajamani
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Alexandre Boutet
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, M5T1W7, Canada
| | - Gavin J B Elias
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Jurgen Germann
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Aaron Loh
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Wissam Deeb
- UMass Chan Medical School, Department of Neurology, Worcester, MA, 01655, USA
- UMass Memorial Health, Department of Neurology, Worcester, MA, 01655, USA
| | - Fuyixue Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
| | - Kawin Setsompop
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard-MIT Health Sciences and Technology, MIT, Cambridge, MA, USA
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Bryan Salvato
- University of Florida Health Jacksonville, Jacksonville, FL, USA
| | - Leonardo Brito de Almeida
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Kelly D Foote
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | - Robert Amaral
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
| | - Paul B Rosenberg
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David F Tang-Wai
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
- Department of Medicine, Division of Neurology, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Stephen Salloway
- Department of Psychiatry and Human Behavior and Neurology, Alpert Medical School of Brown University, Providence, RI, USA
- Memory & Aging Program, Butler Hospital, Providence, USA
| | | | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Research Centre, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Gwenn S Smith
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Constantine G Lyketsos
- Department of Psychiatry and Behavioral Sciences and Richman Family Precision Medicine Center of Excellence, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Michael S Okun
- Norman Fixel Institute for Neurological Diseases, Departments of Neurology and Neurosurgery, University of Florida, Gainesville, FL, USA
| | | | - Zoltan Mari
- Johns Hopkins School of Medicine, Baltimore, MD, USA
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | | | - Andres M Lozano
- Division of Neurosurgery, Department of Surgery, University Health Network and University of Toronto, Toronto, ON, M5T2S8, Canada
- Krembil Research Institute, University of Toronto, Toronto, ON, M5T2S8, Canada
| | - Andreas Horn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
- Departments of Neurology and Neurosurgery, Massachusetts General Hospital, Boston, MA, USA.
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Tropea TF, Waligorska T, Xie SX, Nasrallah IM, Cousins KAQ, Trojanowski JQ, Grossman M, Irwin DJ, Weintraub D, Lee EB, Wolk DA, Chen‐Plotkin AS, Shaw LM. Plasma phosphorylated tau181 predicts cognitive and functional decline. Ann Clin Transl Neurol 2022; 10:18-31. [PMID: 36518085 PMCID: PMC9852389 DOI: 10.1002/acn3.51695] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 12/23/2022] Open
Abstract
OBJECTIVE To determine if plasma tau phosphorylated at threonine 181 (p-tau181) distinguishes pathology-confirmed Alzheimer's disease (AD) from normal cognition (NC) adults, to test if p-tau181 predicts cognitive and functional decline, and to validate findings in an external cohort. METHODS Thirty-one neuropathology-confirmed AD cases, participants with clinical diagnoses of mild cognitive impairment (MCI, N = 91) or AD dementia (N = 64), and NC (N = 241) had plasma collected at study entry. The clinical diagnosis groups had annual cognitive (Mini-Mental State Examination, MMSE) and functional (Clinical Dementia Rating Scale, CDR) measures. NC (N = 70), MCI (N = 75), and AD dementia (N = 50) cases from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were used as a validation cohort. Plasma p-tau181 was measured using the Quanterix SiMoA HD-X platform. RESULTS Plasma p-tau181 differentiated pathology-confirmed AD from NC with negative amyloid PET scans with an AUC of 0.93. A cut point of 3.44 pg/mL (maximum Youden Index) had a sensitivity of 0.77, specificity of 0.96. p-Tau181 values above the cut point were associated with the faster rate of decline in MMSE in AD dementia and MCI and a shorter time to a clinically significant functional decline in all groups. In a subset of MCI cases from ADNI, p-tau181 values above the cut point associated with faster rate of decline in MMSE, and a shorter time to a clinically significant functional decline and conversion to dementia. INTERPRETATION Plasma p-tau181 differentiates AD pathology cases from NC with high accuracy. Higher levels of plasma p-tau181 are associated with faster cognitive and functional decline.
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Affiliation(s)
- Thomas F. Tropea
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Teresa Waligorska
- Department of Pathology and Laboratory MedicinePerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sharon X. Xie
- Department of Biostatistics, Epidemiology, and InformaticsPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Ilya M. Nasrallah
- Department of RadiologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Katheryn A. Q. Cousins
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory MedicinePerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Murray Grossman
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David J. Irwin
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Daniel Weintraub
- Department of PsychiatryPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA,Parkinson's Disease Research, Education and Clinical Center (PADRECC), Philadelphia Veterans Affairs Medical CenterPhiladelphiaPennsylvaniaUSA
| | - Edward B. Lee
- Department of Pathology and Laboratory MedicinePerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David A. Wolk
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Alice S. Chen‐Plotkin
- Department of NeurologyPerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory MedicinePerelman School of Medicine, University of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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50
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Sadaghiani S, Trotman W, Lim SA, Chung E, Ittyerah R, Ravikumar S, Khandelwal P, Prabhakaran K, Lavery ML, Ohm DT, Gabrielyan M, Das SR, Schuck T, Capp N, Peterson CS, Migdal E, Artacho-Pérula E, Jiménez MDMA, Rabal MDPM, Sánchez SC, Prieto CDLR, Parada MC, Insausti R, Robinson JL, McMillan C, Grossman M, Lee EB, Detre JA, Xie SX, Trojanowski JQ, Tisdall MD, Wisse LEM, Irwin DJ, Wolk DA, Yushkevich PA. Associations of phosphorylated tau pathology with whole-hemisphere ex vivo morphometry in 7 tesla MRI. Alzheimers Dement 2022. [PMID: 36464907 DOI: 10.1002/alz.12884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/29/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION Neurodegenerative disorders are associated with different pathologies that often co-occur but cannot be measured specifically with in vivo methods. METHODS Thirty-three brain hemispheres from donors with an Alzheimer's disease (AD) spectrum diagnosis underwent T2-weighted magnetic resonance imaging (MRI). Gray matter thickness was paired with histopathology from the closest anatomic region in the contralateral hemisphere. RESULTS Partial Spearman correlation of phosphorylated tau and cortical thickness with TAR DNA-binding protein 43 (TDP-43) and α-synuclein scores, age, sex, and postmortem interval as covariates showed significant relationships in entorhinal and primary visual cortices, temporal pole, and insular and posterior cingulate gyri. Linear models including Braak stages, TDP-43 and α-synuclein scores, age, sex, and postmortem interval showed significant correlation between Braak stage and thickness in the parahippocampal gyrus, entorhinal cortex, and Broadman area 35. CONCLUSION We demonstrated an association of measures of AD pathology with tissue loss in several AD regions despite a limited range of pathology in these cases. HIGHLIGHTS Neurodegenerative disorders are associated with co-occurring pathologies that cannot be measured specifically with in vivo methods. Identification of the topographic patterns of these pathologies in structural magnetic resonance imaging (MRI) may provide probabilistic biomarkers. We demonstrated the correlation of the specific patterns of tissue loss from ex vivo brain MRI with underlying pathologies detected in postmortem brain hemispheres in patients with Alzheimer's disease (AD) spectrum disorders. The results provide insight into the interpretation of in vivo structural MRI studies in patients with AD spectrum disorders.
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Affiliation(s)
- Shokufeh Sadaghiani
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Winifred Trotman
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sydney A Lim
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Eunice Chung
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Ranjit Ittyerah
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sadhana Ravikumar
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Pulkit Khandelwal
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Karthik Prabhakaran
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Madigan L Lavery
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Daniel T Ohm
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Marianna Gabrielyan
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sandhitsu R Das
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Theresa Schuck
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Noah Capp
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Claire S Peterson
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Elyse Migdal
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Emilio Artacho-Pérula
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | | | | | - Sandra Cebada Sánchez
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Carlos de la Rosa Prieto
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Marta Córcoles Parada
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, Neuromax CSIC Associated Unit, University of Castilla-La Mancha, Albacete, Spain
| | - John L Robinson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John A Detre
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sharon X Xie
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - M Dylan Tisdall
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Laura E M Wisse
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Paul A Yushkevich
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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