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Billaud CHA, Yu J. The hippocampus as a structural and functional network epicentre for distant cortical thinning in neurocognitive aging. Neurobiol Aging 2024; 139:82-89. [PMID: 38657394 DOI: 10.1016/j.neurobiolaging.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 04/05/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024]
Abstract
Alterations in grey matter (GM) and white matter (WM) are associated with memory impairment across the neurocognitive aging spectrum and theorised to spread throughout brain networks. Functional and structural connectivity (FC,SC) may explain widespread atrophy. We tested the effect of SC and FC to the hippocampus on cortical thickness (CT) of connected areas. In 419 (223 F) participants (agemean=73 ± 8) from the Alzheimer's Disease Neuroimaging Initiative, cortical regions associated with memory (Rey Auditory Verbal Learning Test) were identified using Lasso regression. Two structural equation models (SEM), for SC and resting-state FC, were fitted including CT areas, and SC and FC to the left and right hippocampus (LHIP,RHIP). LHIP (β=-0.150,p=<.001) and RHIP (β=-0.139,p=<.001) SC predicted left temporopolar/rhinal CT; RHIP SC predicted right temporopolar/rhinal CT (β=-0.191,p=<.001). LHIP SC predicted right fusiform/parahippocampal (β=-0.104,p=.011) and intraparietal sulcus/superior parietal CT (β=0.101,p=.028). Increased RHIP FC predicted higher left inferior parietal CT (β=0.132,p=.042) while increased LHIP FC predicted lower right fusiform/parahippocampal CT (β=-0.97; p=.023). The hippocampi may be epicentres for cortical thinning through disrupted connectivity.
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Affiliation(s)
- Charly Hugo Alexandre Billaud
- Nanyang Technological University, Psychology, School of Social Sciences, 48 Nanyang Avenue, Singapore City 639798, Singapore.
| | - Junhong Yu
- Nanyang Technological University, Psychology, School of Social Sciences, 48 Nanyang Avenue, Singapore City 639798, Singapore
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Nabizadeh F, Pirahesh K, Aarabi MH, Wennberg A, Pini L. Behavioral and dysexecutive variant of Alzheimer's disease: Insights from structural and molecular imaging studies. Heliyon 2024; 10:e29420. [PMID: 38638964 PMCID: PMC11024599 DOI: 10.1016/j.heliyon.2024.e29420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 04/02/2024] [Accepted: 04/08/2024] [Indexed: 04/20/2024] Open
Abstract
Frontal variant Alzheimer's disease (AD) manifests with either behavioral or dysexecutive syndromes. Recent efforts to gain a deeper understanding of this phenotype have led to a re-conceptualization of frontal AD. Behavioral (bAD) and dysexecutive (dAD) phenotypes could be considered subtypes, as suggested by both clinical and neuroimaging studies. In this review, we focused on imaging studies to highlight specific brain patterns in these two uncommon clinical AD phenotypes. Although studies did not compare directly these two variants, a common epicenter located in the frontal cortex could be inferred. On the contrary, 18F-FDG-PET findings suggested differing metabolic patterns, with bAD showing specific involvement of frontal regions and dAD exhibiting widespread alterations. Structural MRI findings confirmed this pattern, suggesting that degeneration might involve neural circuits associated with behavioral control in bAD and attentional networks in dAD. Furthermore, molecular imaging has identified different neocortical tau distribution in bAD and dAD patients compared to typical AD patients, although the distribution is remarkably heterogeneous. In contrast, Aβ deposition patterns are less differentiated between these atypical variants and typical AD. Although preliminary, these findings underscore the complexity of AD frontal phenotypes and suggest that they represent distinct entities. Further research is essential to refine our understanding of the pathophysiological mechanisms in frontal AD.
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Affiliation(s)
- Fardin Nabizadeh
- School of Medicine, Iran University of Medical Sciences, Tehran, Iran
- Neuroscience Research Group (NRG), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Kasra Pirahesh
- School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | | | - Alexandra Wennberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Lorenzo Pini
- Padova Neuroscience Center, University of Padova, Italy
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3
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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] [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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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] [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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Gan S, Sun Y, Liu K, Jia X, Li X, Zhang M, Bai L. APOE ε4 allele status modulates the spatial patterns of progressive atrophy in the temporal lobes after mild traumatic brain injury. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12550. [PMID: 38371357 PMCID: PMC10870335 DOI: 10.1002/dad2.12550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 01/12/2024] [Accepted: 01/18/2024] [Indexed: 02/20/2024]
Abstract
INTRODUCTION We evaluated how the apolipoprotein E (APOE) ε4 allele modulated the spatial patterns of longitudinal atrophy in the Alzheimer's disease-vulnerable brain areas of patients with mild traumatic brain injury (mTBI) from the acute to chronic phase post injury. METHODS Fifty-nine adult patients with acute mTBI and 48 healthy controls with APOE ε4 allele testing underwent T1-weighted magnetic resonance imaging and neuropsychological assessments with 6 to 12 months of follow-up. Progressive brain volume loss was compared voxel-wise in the temporal lobes. RESULTS Patients with the APOE ε4 allele presented significant longitudinal atrophy in the left superior and middle temporal gyri, where the progressive gray matter volume loss predicted longitudinal impairment in language fluency, whereas mTBI APOE ε4 allele noncarriers showed mainly significant longitudinal atrophy in the medial temporal lobes, without significant neuropsychological relevance. DISCUSSION The atrophy progression observed in mTBI patients with the APOE ε4 allele may increase the possibility of developing a specific phenotype of Alzheimer's disease with language dysfunction. Highlights The apolipoprotein E (APOE) ε4 allele and mild traumatic brain injury (mTBI) are risk factors for Alzheimer's disease (AD) progression.It is unclear how the interaction of mTBI with the APOE ε4 allele impacts the progressive atrophy topography in AD-vulnerable brain regions.In this study, patients with the APOE ε4 allele showed progressive atrophy patterns similar to the early stage of logopenic variant of primary progressive aphasia (lvPPA) phenotype of AD. APOE ε4 allele carriers with mTBI history may be at the risk of developing a given AD phenotype with language dysfunction.
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Affiliation(s)
- Shuoqiu Gan
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and TechnologyXi'an Jiaotong UniversityXi'anChina
- Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefeiChina
- Division of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina
- Department of Medical Imagingthe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Yingxiang Sun
- Department of Medical Imagingthe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Kejia Liu
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and TechnologyXi'an Jiaotong UniversityXi'anChina
| | - Xiaoyan Jia
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and TechnologyXi'an Jiaotong UniversityXi'anChina
| | - Xuan Li
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and TechnologyXi'an Jiaotong UniversityXi'anChina
| | - Ming Zhang
- Department of Medical Imagingthe First Affiliated Hospital of Xi'an Jiaotong UniversityXi'anChina
| | - Lijun Bai
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and TechnologyXi'an Jiaotong UniversityXi'anChina
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Ren X, Dong B, Luan Y, Wu Y, Huang Y. Alterations via inter-regional connective relationships in Alzheimer's disease. Front Hum Neurosci 2023; 17:1276994. [PMID: 38021241 PMCID: PMC10672243 DOI: 10.3389/fnhum.2023.1276994] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Disruptions in the inter-regional connective correlation within the brain are believed to contribute to memory impairment. To detect these corresponding correlation networks in Alzheimer's disease (AD), we conducted three types of inter-regional correlation analysis, including structural covariance, functional connectivity and group-level independent component analysis (group-ICA). The analyzed data were obtained from the Alzheimer's Disease Neuroimaging Initiative, comprising 52 cognitively normal (CN) participants without subjective memory concerns, 52 individuals with late mild cognitive impairment (LMCI) and 52 patients with AD. We firstly performed vertex-wise cortical thickness analysis to identify brain regions with cortical thinning in AD and LMCI patients using structural MRI data. These regions served as seeds to construct both structural covariance networks and functional connectivity networks for each subject. Additionally, group-ICA was performed on the functional data to identify intrinsic brain networks at the cohort level. Through a comparison of the structural covariance and functional connectivity networks with ICA networks, we identified several inter-regional correlation networks that consistently exhibited abnormal connectivity patterns among AD and LMCI patients. Our findings suggest that reduced inter-regional connectivity is predominantly observed within a subnetwork of the default mode network, which includes the posterior cingulate and precuneus regions, in both AD and LMCI patients. This disruption of connectivity between key nodes within the default mode network provides evidence supporting the hypothesis that impairments in brain networks may contribute to memory deficits in AD and LMCI.
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Affiliation(s)
- Xiaomei Ren
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Bowen Dong
- College of Electrical Engineering, Sichuan University, Chengdu, China
| | - Ying Luan
- Department of Radiology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Yunzhi Huang
- Institute for AI in Medicine, School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing, China
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Sintini I, Graff-Radford J, Schwarz CG, Machulda MM, Singh NA, Carlos AF, Senjem ML, Jack CR, Lowe VJ, Josephs KA, Whitwell JL. Longitudinal rates of atrophy and tau accumulation differ between the visual and language variants of atypical Alzheimer's disease. Alzheimers Dement 2023; 19:4396-4406. [PMID: 37485642 PMCID: PMC10592409 DOI: 10.1002/alz.13396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/19/2023] [Accepted: 06/19/2023] [Indexed: 07/25/2023]
Abstract
INTRODUCTION Atypical variants of Alzheimer's disease (AD) include the visual variant, known as posterior cortical atrophy (PCA), and the language variant, known as logopenic progressive aphasia (LPA). Clinically, rates of disease progression differ between them. METHODS We evaluated 34 PCA and 29 LPA participants. Structural magnetic resonance imaging and 18 F-flortaucipir positron emission tomography were performed at baseline and at 1-year follow-up. Rates of change in tau uptake and grey matter volumes were compared between PCA and LPA with linear mixed-effects models and voxel-based analyses. RESULTS PCA had faster rates of occipital atrophy. LPA had faster rates of left temporal atrophy and faster rates of tau accumulation in the parietal, right temporal, and occipital lobes. Age was negatively associated with rates of atrophy and tau accumulation. DISCUSSION Longitudinal patterns of neuroimaging abnormalities differed between PCA and LPA, although with divergent results for tau accumulation and atrophy. HIGHLIGHTS The language variant of Alzheimer's disease accumulates tau faster than the visual variant. Each variant shows faster rates of atrophy than the other in its signature regions. Age negatively influences rates of atrophy and tau accumulation in both variants.
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Affiliation(s)
- Irene Sintini
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
| | | | | | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester MN, USA, 55905
| | | | - Arenn F. Carlos
- Department of Neurology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Matthew L. Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
- Department of Information Technology, Mayo Clinic, Rochester, MN, USA, 55905
| | | | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA, 55905
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Brown A, Salo SK, Savage G. Frontal variant Alzheimer's disease: A systematic narrative synthesis. Cortex 2023; 166:121-153. [PMID: 37356113 DOI: 10.1016/j.cortex.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 01/03/2023] [Accepted: 05/22/2023] [Indexed: 06/27/2023]
Abstract
BACKGROUND Frontal variant Alzheimer's disease (fvAD) is considered a rare form of Alzheimer's disease (AD) which may be misdiagnosed as behavioural variant frontotemporal dementia (bvFTD). The literature has tended to conflate behavioural and executive dysfunction in fvAD cohorts and uses both AD diagnostic criteria and bvFTD diagnostic criteria to classify fvAD cohorts. The primary aim of this narrative synthesis was to summarise neuropsychological findings in fvAD cohorts in the context of established AD pathology. METHODS EMBASE, PsycINFO, PROQUEST and MEDLINE databases were searched for studies eligible for inclusion. Studies with both neuropsychological and biomarker evidence were included in the final narrative synthesis. RESULTS Ten studies were reviewed, including samples totalling 342 fvAD participants, 178 typical AD participants and 250 bvFTD participants. The review revealed areas worthy of further investigation that may aid differential diagnosis, including the degree of executive dysfunction in fvAD cohorts relative to bvFTD cohorts, the onset of behavioural and cognitive symptomatology, and similarities between fvAD and typical AD cognitive profiles. CONCLUSION There was insufficient neuropsychological evidence to clearly differentiate fvAD and bvFTD cognitive phenotypes, however, the review has highlighted distinctive features of the two disorders that may guide differential diagnosis in future research. Moreover, the review has highlighted issues involving disparate diagnostic criteria used to classify fvAD cohorts, contributing to variation in findings.
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Affiliation(s)
- Andrea Brown
- School of Psychological Sciences, Macquarie University, Sydney, Australia
| | - Sarah K Salo
- Department of Psychology, Swansea University, Swansea, UK; School of Psychology, University of Plymouth, Plymouth, UK
| | - Greg Savage
- School of Psychological Sciences, Macquarie University, Sydney, Australia.
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Donato L, Mordà D, Scimone C, Alibrandi S, D'Angelo R, Sidoti A. How Many Alzheimer-Perusini's Atypical Forms Do We Still Have to Discover? Biomedicines 2023; 11:2035. [PMID: 37509674 PMCID: PMC10377159 DOI: 10.3390/biomedicines11072035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Alzheimer-Perusini's (AD) disease represents the most spread dementia around the world and constitutes a serious problem for public health. It was first described by the two physicians from whom it took its name. Nowadays, we have extensively expanded our knowledge about this disease. Starting from a merely clinical and histopathologic description, we have now reached better molecular comprehension. For instance, we passed from an old conceptualization of the disease based on plaques and tangles to a more modern vision of mixed proteinopathy in a one-to-one relationship with an alteration of specific glial and neuronal phenotypes. However, no disease-modifying therapies are yet available. It is likely that the only way to find a few "magic bullets" is to deepen this aspect more and more until we are able to draw up specific molecular profiles for single AD cases. This review reports the most recent classifications of AD atypical variants in order to summarize all the clinical evidence using several discrimina (for example, post mortem neurofibrillary tangle density, cerebral atrophy, or FDG-PET studies). The better defined four atypical forms are posterior cortical atrophy (PCA), logopenic variant of primary progressive aphasia (LvPPA), behavioral/dysexecutive variant and AD with corticobasal degeneration (CBS). Moreover, we discuss the usefulness of such classifications before outlining the molecular-genetic aspects focusing on microglial activity or, more generally, immune system control of neuroinflammation and neurodegeneration.
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Affiliation(s)
- Luigi Donato
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
- Department of Biomolecular Strategies, Genetics, Cutting-Edge Therapies, Euro-Mediterranean Institute of Science and Technology, Via Michele Miraglia, 98139 Palermo, Italy
| | - Domenico Mordà
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
- Department of Biomolecular Strategies, Genetics, Cutting-Edge Therapies, Euro-Mediterranean Institute of Science and Technology, Via Michele Miraglia, 98139 Palermo, Italy
| | - Concetta Scimone
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
- Department of Biomolecular Strategies, Genetics, Cutting-Edge Therapies, Euro-Mediterranean Institute of Science and Technology, Via Michele Miraglia, 98139 Palermo, Italy
| | - Simona Alibrandi
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, Viale Ferdinando Stagno D'Alcontres 31, 98166 Messina, Italy
| | - Rosalia D'Angelo
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
| | - Antonina Sidoti
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, Via Consolare Valeria 1, 98125 Messina, Italy
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Kawles A, Minogue G, Zouridakis A, Keszycki R, Gill N, Nassif C, Coventry C, Zhang H, Rogalski E, Flanagan ME, Castellani R, Bigio EH, Mesulam MM, Geula C, Gefen T. Differential vulnerability of the dentate gyrus to tauopathies in dementias. Acta Neuropathol Commun 2023; 11:1. [PMID: 36597124 PMCID: PMC9811688 DOI: 10.1186/s40478-022-01485-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/23/2022] [Indexed: 01/05/2023] Open
Abstract
The dentate gyrus (DG), a key hippocampal subregion in memory processing, generally resists phosphorylated tau accumulation in the amnestic dementia of the Alzheimer's type due to Alzheimer's disease (DAT-AD), but less is known about the susceptibility of the DG to other tauopathies. Here, we report stereologic densities of total DG neurons and tau inclusions in thirty-two brains of human participants with autopsy-confirmed tauopathies with distinct isoform profiles-3R Pick's disease (PiD, N = 8), 4R corticobasal degeneration (CBD, N = 8), 4R progressive supranuclear palsy (PSP, N = 8), and 3/4R AD (N = 8). All participants were diagnosed during life with primary progressive aphasia (PPA), an aphasic clinical dementia syndrome characterized by progressive deterioration of language abilities with spared non-language cognitive abilities in early stages, except for five patients with DAT-AD as a comparison group. 51% of total participants were female. All specimens were stained immunohistochemically with AT8 to visualize tau pathology, and PPA cases were stained for Nissl substance to visualize neurons. Unbiased stereological analysis was performed in granule and hilar DG cells, and inclusion-to-neuron ratios were calculated. In the PPA group, PiD had highest mean total (granule + hilar) densities of DG tau pathology (p < 0.001), followed by CBD, AD, then PSP. PPA-AD cases showed more inclusions in hilar cells compared to granule cells, while the opposite was true in PiD and CBD. Inclusion-to-neuron ratios revealed, on average, 33% of all DG neurons in PiD cases contained a tau inclusion, compared to ~ 7% in CBD, 2% in AD, and 0.4% in PSP. There was no significant difference between DAT-AD and PPA-AD pathologic tau burden, suggesting that differences in DG burden are not specific to clinical phenotype. We conclude that the DG is differentially vulnerable to pathologic tau accumulation, raising intriguing questions about the structural integrity and functional significance of hippocampal circuits in neurodegenerative dementias.
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Affiliation(s)
- Allegra Kawles
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Psychiatry & Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Grace Minogue
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
| | - Antonia Zouridakis
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
| | - Rachel Keszycki
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Psychiatry & Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Nathan Gill
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Caren Nassif
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
| | - Christina Coventry
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
| | - Hui Zhang
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Emily Rogalski
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Psychiatry & Behavioral Sciences, 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, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Rudolph Castellani
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Eileen H. Bigio
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - M. Marsel Mesulam
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Changiz Geula
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Cell and Developmental Biology, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Tamar Gefen
- Mesulam Center for Cognitive Neurology & Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, 300 E. Superior Street, Tarry Building, 8th Floor, Chicago, IL 60611 USA
- Department of Psychiatry & Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL USA
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Pelak VS, Mahmood A, Abe-Ridgway K. Perspectives and a Systematic Scoping Review on Longitudinal Profiles of Posterior Cortical Atrophy Syndrome. Curr Neurol Neurosci Rep 2022; 22:803-812. [PMID: 36242715 DOI: 10.1007/s11910-022-01238-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/16/2022] [Indexed: 01/27/2023]
Abstract
PURPOSE OF REVIEW To provide perspectives on the importance of understanding longitudinal profiles of posterior cortical atrophy (PCA) and report results of a scoping review to identify data and knowledge gaps related to PCA survival and longitudinal clinical and biomarker outcomes. RECENT FINDINGS Thirteen longitudinal studies were identified; all but two had fewer than 30 participants with PCA. Relatively few longitudinal data exist, particularly for survival. In PCA, posterior cortical dysfunction and atrophy progress at faster rates compared to non-posterior regions, potentially up to a decade after symptom onset. Unlike typical AD, PCA phenotype-defined cognitive dysfunction and atrophy remain relatively more severe compared to other regions throughout the PCA course. Select cognitive tests hold promise as PCA outcome measures and for staging. Further longitudinal investigations are critically needed to enable PCA inclusion in treatment trials and to provide appropriate care to patients and enhance our understanding of the pathophysiology of dementing diseases.
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Affiliation(s)
- Victoria S Pelak
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA. .,Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA.
| | - Asher Mahmood
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Kathryn Abe-Ridgway
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO, USA
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Fray S, Achouri-Rassas A, Belal S, Messaoud T. Missing apolipoprotein E ɛ4 allele associated with nonamnestic Alzheimer’s disease in a Tunisian population. J Genet 2022. [DOI: 10.1007/s12041-022-01384-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Whitwell JL, Martin PR, Graff-Radford J, Machulda MM, Sintini I, Buciuc M, Senjem ML, Schwarz CG, Botha H, Carrasquillo MM, Ertekin-Taner N, Lowe VJ, Jack CR, Josephs KA. Investigating Heterogeneity and Neuroanatomic Correlates of Longitudinal Clinical Decline in Atypical Alzheimer Disease. Neurology 2022; 98:e2436-e2445. [PMID: 35483899 PMCID: PMC9231842 DOI: 10.1212/wnl.0000000000200336] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 02/21/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES The aims of this work were to compare rates of longitudinal change in neurologic and neuropsychological test performance between the logopenic progressive aphasia (LPA) and posterior cortical atrophy (PCA) variants of atypical Alzheimer disease (AD) and to use unbiased principal component analysis to assess heterogeneity in patterns of change and relationships to demographics and concurrent brain atrophy. METHODS Patients with PCA or LPA who were positive for amyloid and tau AD biomarkers and had undergone serial neurologic and neuropsychological assessments and structural MRI were identified. Rates of change in 13 clinical measures were compared between groups in a case-control design, and principal component analysis was used to assess patterns of clinical change unbiased by clinical phenotype. Components were correlated with rates of regional brain atrophy with tensor-based morphometry. RESULTS Twenty-eight patients with PCA and 27 patients with LPA were identified. Those with LPA showed worse baseline performance and faster rates of decline in naming, repetition, and working memory, as well as faster rates of decline in verbal episodic memory, compared to those with PCA. Conversely, patients with PCA showed worse baseline performance in tests of visuospatial and perceptual function and on the Clinical Dementia Rating Scale and faster rates of decline in visuoperceptual function compared to those with LPA. Principal component analysis showed that patterns of clinical decline were highly heterogeneous across the cohort, with 10 principal components required to explain >90% of the variance. The first principal component reflected overall severity, with higher scores in LPA than PCA reflecting faster decline in LPA, and was related to left temporoparietal atrophy. The second and third principal components were not related to clinical phenotype but showed some relationship to regional atrophy. No relationships were identified between the principal components and age, sex, disease duration, amyloid PET findings, or apolipoprotein genotype. DISCUSSION Longitudinal patterns of clinical decline differ between LPA and PCA but are heterogeneous and related to different patterns of topographic spread. PCA is associated with a more slowly progressive course than LPA.
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Affiliation(s)
- Jennifer L Whitwell
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL.
| | - Peter R Martin
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Jonathan Graff-Radford
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Mary M Machulda
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Irene Sintini
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Marina Buciuc
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Matthew L Senjem
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Christopher G Schwarz
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Hugo Botha
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Minerva M Carrasquillo
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Nilufer Ertekin-Taner
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Val J Lowe
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Clifford R Jack
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
| | - Keith A Josephs
- From the Departments of Radiology (J.W., I.S., M.L.S., C.G.S., V.J.L., C.R.J.), Quantitative Health Sciences (P.R.M.), Neurology (J.G.-R., M.B., H.B., K.A.J.), Psychiatry and Psychology (M.M.), and Information Technology (M.L.S.), Mayo Clinic, Rochester, MN; and Department of Neuroscience (M.M.C., N.E.-T.), Mayo Clinic, Jacksonville, FL
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Clinical Screening for Posterior Cortical Atrophy. Cogn Behav Neurol 2022; 35:104-109. [PMID: 35639011 DOI: 10.1097/wnn.0000000000000297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 08/04/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Posterior cortical atrophy (PCA) is a progressive neurologic syndrome that presents with complex visual deficits. Although PCA is most commonly a form of Alzheimer disease (AD), its early diagnosis is usually delayed due to a lack of understanding for how best to clinically screen for the syndrome. OBJECTIVE To identify neurobehavioral screening tasks for PCA-beyond simple visual constructions-that can be administered in clinic or at bedside. METHOD We compared the performance of 12 individuals who met neuroimaging-supported consensus criteria for PCA with that of 12 matched individuals with typical AD (tAD) and 24 healthy controls (HC) on clinic/bedside tasks measuring (a) complex figure copying, (b) Balint syndrome, (c) visual object agnosia, (d) color identification, (e) figure-ground discrimination, (f) global-local processing, (g) dressing apraxia, (h) ideomotor apraxia, and (i) Gerstmann syndrome. RESULTS All of the individuals with PCA were impaired on the figure-ground discrimination task compared with half of the tAD group and no HC. Approximately half of the PCA group had Balint syndrome, dressing apraxia, and ideomotor apraxia compared with none in the tAD group. Difficulty copying a complex figure, global-local processing impairment, and Gerstmann syndrome did not distinguish between the two dementia groups. CONCLUSION The figure-ground discrimination task can be used successfully as an overall screening measure for PCA, followed by specific tasks for Balint syndrome and dressing and limb apraxia. Findings reinforce PCA as a predominant occipitoparietal disorder with dorsal visual stream involvement and parietal signs with spatiomotor impairments.
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Wei X, Du X, Xie Y, Suo X, He X, Ding H, Zhang Y, Ji Y, Chai C, Liang M, Yu C, Liu Y, Qin W. Mapping cerebral atrophic trajectory from amnestic mild cognitive impairment to Alzheimer's disease. Cereb Cortex 2022; 33:1310-1327. [PMID: 35368064 PMCID: PMC9930625 DOI: 10.1093/cercor/bhac137] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/13/2022] [Accepted: 03/13/2022] [Indexed: 11/14/2022] Open
Abstract
Alzheimer's disease (AD) patients suffer progressive cerebral atrophy before dementia onset. However, the region-specific atrophic processes and the influences of age and apolipoprotein E (APOE) on atrophic trajectory are still unclear. By mapping the region-specific nonlinear atrophic trajectory of whole cerebrum from amnestic mild cognitive impairment (aMCI) to AD based on longitudinal structural magnetic resonance imaging data from Alzheimer's disease Neuroimaging Initiative (ADNI) database, we unraveled a quadratic accelerated atrophic trajectory of 68 cerebral regions from aMCI to AD, especially in the superior temporal pole, caudate, and hippocampus. Besides, interaction analyses demonstrated that APOE ε4 carriers had faster atrophic rates than noncarriers in 8 regions, including the caudate, hippocampus, insula, etc.; younger patients progressed faster than older patients in 32 regions, especially for the superior temporal pole, hippocampus, and superior temporal gyrus; and 15 regions demonstrated complex interaction among age, APOE, and disease progression, including the caudate, hippocampus, etc. (P < 0.05/68, Bonferroni correction). Finally, Cox proportional hazards regression model based on the identified region-specific biomarkers could effectively predict the time to AD conversion within 10 years. In summary, cerebral atrophic trajectory mapping could help a comprehensive understanding of AD development and offer potential biomarkers for predicting AD conversion.
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Affiliation(s)
| | | | | | | | - Xiaoxi He
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Hao Ding
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Yu Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Yi Ji
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Chao Chai
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Meng Liang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China,Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Tianjin 300052, China,School of Medical Imaging, Tianjin Medical University, Tianjin 300070, China
| | - Yong Liu
- Corresponding author: Wen Qin, Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road No 154, Heping District, Tianjin 300052, China. ; Yong Liu, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Wen Qin
- Corresponding author: Wen Qin, Department of Radiology, and Tianjin Key Lab of Functional Imaging, Tianjin Medical University General Hospital, Anshan Road No 154, Heping District, Tianjin 300052, China. ; Yong Liu, School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
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Zang C, Liu H, Ju C, Yuan F, Ning J, Shang M, Bao X, Yu Y, Yao X, Zhang D. Gardenia jasminoides J. Ellis extract alleviated white matter damage through promoting the differentiation of oligodendrocyte precursor cells via suppressing neuroinflammation. Food Funct 2022; 13:2131-2141. [PMID: 35112688 DOI: 10.1039/d1fo02127c] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Increasing evidence has highlighted the role of white matter damage in the pathology of Alzheimer's disease (AD). Previous research has shown that a mixture of crocin analogues (GJ-4), Gardenia jasminoides J. Ellis extract, improved cognition in several AD mouse models, but the mechanism remains unclear. The aim of the present study was to investigate the effects and underlying mechanisms of GJ-4 on white matter damage. Proteomic analysis and western blotting results suggested that the level of myelin-related proteins, including myelin basic protein (MBP), myelin associated glycoprotein (MAG) and myelin associated oligodendrocyte basic protein (MOBP), was significantly decreased in the brain of PrP-hAβPPswe/PS1ΔE9 (APP/PS1) transgenic mice, and GJ-4 treatment increased the expressions of these proteins. This result revealed that GJ-4 could ameliorate myelin injury, suggesting that this might be a possible mechanism of GJ-4 on cognition. To validate the effects of GJ-4 on myelin, a metabolite of GJ-4, crocetin, which can pass through the blood-brain barrier, was applied in in vitro experiments. A mechanistic study revealed that crocetin significantly promoted the differentiation of primary cultured oligodendrocyte precursor cells to oligodendrocytes through up-regulation of nuclear Ki67 and transcription factor 2 (Olig2). Oligodendrocytes, the myelin-forming cells, have been reported to be lifelong partners of neurons. Therefore, to investigate the effects of crocetin on myelin and neurons, lysophosphatidylcholine (LPC)-treated primary mixed midbrain neuronal/glial culture was used. Immunofluorescence results indicated that crocetin treatment protected neurons and suppressed microglial activation against LPC-induced injury. To further discern the effects of GJ-4 on white matter injury and neuroinflammation, an LPC-induced mouse model was developed. GJ-4 administration increased oligodendrocyte proliferation, differentiation, and myelin repair. The mechanistic study indicated that GJ-4 improved white matter injury through the regulation of neuroinflammatory dysfunction. These data indicated that GJ-4 effectively repaired white matter damage in the LPC-treated mice. Thus, the present study supported GJ-4 as a potential therapeutic agent for AD and white matter related diseases.
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Affiliation(s)
- Caixia Zang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Hui Liu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Cheng Ju
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Fangyu Yuan
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Jingwen Ning
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Meiyu Shang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Xiuqi Bao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Yang Yu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Xinsheng Yao
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
| | - Dan Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Department of Pharmacology, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, 1 Xian Nong Tan Street, Beijing 100050, P. R. China.
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Singleton EH, Pijnenburg YAL, Gami-Patel P, Boon BDC, Bouwman F, Papma JM, Seelaar H, Scheltens P, Grinberg LT, Spina S, Nana AL, Rabinovici GD, Seeley WW, Ossenkoppele R, Dijkstra AA. The behavioral variant of Alzheimer's disease does not show a selective loss of Von Economo and phylogenetically related neurons in the anterior cingulate cortex. Alzheimers Res Ther 2022; 14:11. [PMID: 35057846 PMCID: PMC8772094 DOI: 10.1186/s13195-021-00947-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Accepted: 12/13/2021] [Indexed: 12/13/2022]
Abstract
BACKGROUND The neurobiological origins of the early and predominant behavioral changes seen in the behavioral variant of Alzheimer's disease (bvAD) remain unclear. A selective loss of Von Economo neurons (VENs) and phylogenetically related neurons have been observed in behavioral variant frontotemporal dementia (bvFTD) and several psychiatric diseases. Here, we assessed whether these specific neuronal populations show a selective loss in bvAD. METHODS VENs and GABA receptor subunit theta (GABRQ)-immunoreactive pyramidal neurons of the anterior cingulate cortex (ACC) were quantified in post-mortem tissue of patients with bvAD (n = 9) and compared to typical AD (tAD, n = 6), bvFTD due to frontotemporal lobar degeneration based on TDP-43 pathology (FTLD, n = 18) and controls (n = 13) using ANCOVAs adjusted for age and Bonferroni corrected. In addition, ratios of VENs and GABRQ-immunoreactive (GABRQ-ir) pyramidal neurons over all Layer 5 neurons were compared between groups to correct for overall Layer 5 neuronal loss. RESULTS The number of VENs or GABRQ-ir neurons did not differ significantly between bvAD (VENs: 26.0 ± 15.3, GABRQ-ir pyramidal: 260.4 ± 87.1) and tAD (VENs: 32.0 ± 18.1, p = 1.00, GABRQ-ir pyramidal: 349.8 ± 109.6, p = 0.38) and controls (VENs: 33.5 ± 20.3, p = 1.00, GABRQ-ir pyramidal: 339.4 ± 95.9, p = 0.37). Compared to bvFTD, patients with bvAD showed significantly more GABRQ-ir pyramidal neurons (bvFTD: 140.5 ± 82.658, p = 0.01) and no significant differences in number of VENs (bvFTD: 10.9 ± 13.8, p = 0.13). Results were similar when assessing the number of VENs and GABRQ-ir relative to all neurons of Layer 5. DISCUSSION VENs and phylogenetically related neurons did not show a selective loss in the ACC in patients with bvAD. Our results suggest that, unlike in bvFTD, the clinical presentation in bvAD may not be related to the loss of VENs and related neurons in the ACC.
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Affiliation(s)
- E. H. Singleton
- grid.509540.d0000 0004 6880 3010Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Y. A. L. Pijnenburg
- grid.509540.d0000 0004 6880 3010Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - P. Gami-Patel
- grid.509540.d0000 0004 6880 3010Department of Pathology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - B. D. C. Boon
- grid.509540.d0000 0004 6880 3010Department of Pathology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - F. Bouwman
- grid.509540.d0000 0004 6880 3010Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - J. M. Papma
- grid.5645.2000000040459992XNeurology, Erasmus University Medical Center, Rotterdam, the Netherlands ,grid.5645.2000000040459992XRadiology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - H. Seelaar
- grid.5645.2000000040459992XNeurology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - P. Scheltens
- grid.509540.d0000 0004 6880 3010Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - L. T. Grinberg
- grid.266102.10000 0001 2297 6811Departments of Pathology, University of California San Francisco, San Francisco, USA ,grid.266102.10000 0001 2297 6811Departments of Neurology, University of California San Francisco, San Francisco, USA
| | - S. Spina
- grid.266102.10000 0001 2297 6811Departments of Pathology, University of California San Francisco, San Francisco, USA
| | - A. L. Nana
- grid.266102.10000 0001 2297 6811Departments of Pathology, University of California San Francisco, San Francisco, USA
| | - G. D. Rabinovici
- grid.266102.10000 0001 2297 6811Departments of Neurology, University of California San Francisco, San Francisco, USA ,grid.266102.10000 0001 2297 6811Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, USA
| | - W. W. Seeley
- grid.266102.10000 0001 2297 6811Departments of Pathology, University of California San Francisco, San Francisco, USA ,grid.266102.10000 0001 2297 6811Departments of Neurology, University of California San Francisco, San Francisco, USA
| | - R. Ossenkoppele
- grid.509540.d0000 0004 6880 3010Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands ,grid.4514.40000 0001 0930 2361Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - A. A. Dijkstra
- grid.509540.d0000 0004 6880 3010Department of Pathology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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18
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Ossenkoppele R, Singleton EH, Groot C, Dijkstra AA, Eikelboom WS, Seeley WW, Miller B, Laforce RJ, Scheltens P, Papma JM, Rabinovici GD, Pijnenburg YAL. Research Criteria for the Behavioral Variant of Alzheimer Disease: A Systematic Review and Meta-analysis. JAMA Neurol 2021; 79:48-60. [PMID: 34870696 PMCID: PMC8649917 DOI: 10.1001/jamaneurol.2021.4417] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Importance The behavioral variant of Alzheimer disease (bvAD) is characterized by early and predominant behavioral deficits caused by AD pathology. This AD phenotype is insufficiently understood and lacks standardized clinical criteria, limiting reliability and reproducibility of diagnosis and scientific reporting. Objective To perform a systematic review and meta-analysis of the bvAD literature and use the outcomes to propose research criteria for this syndrome. Data Sources A systematic literature search in PubMed/MEDLINE and Web of Science databases (from inception through April 7, 2021) was performed in duplicate. Study Selection Studies reporting on behavioral, neuropsychological, or neuroimaging features in bvAD and, when available, providing comparisons with typical amnestic-predominant AD (tAD) or behavioral variant frontotemporal dementia (bvFTD). Data Extraction and Synthesis This analysis involved random-effects meta-analyses on group-level study results of clinical data and systematic review of the neuroimaging literature. The study was performed following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. Main Outcomes and Measures Behavioral symptoms (neuropsychiatric symptoms and bvFTD core clinical criteria), cognitive function (global cognition, episodic memory, and executive functioning), and neuroimaging features (structural magnetic resonance imaging, [18F]fluorodeoxyglucose-positron emission tomography, perfusion single-photon emission computed tomography, amyloid positron emission tomography, and tau positron emission tomography). Results The search led to the assessment of 83 studies, including 13 suitable for meta-analysis. Data were collected for 591 patients with bvAD. There was moderate to substantial heterogeneity and moderate risk of bias across studies. Cases with bvAD showed more severe behavioral symptoms than tAD (standardized mean difference [SMD], 1.16 [95% CI, 0.74-1.59]; P < .001) and a trend toward less severe behavioral symptoms compared with bvFTD (SMD, -0.22 [95% CI, -0.47 to 0.04]; P = .10). Meta-analyses of cognitive data indicated worse executive performance in bvAD vs tAD (SMD, -1.03 [95% CI, -1.74 to -0.32]; P = .008) but not compared with bvFTD (SMD, -0.61 [95% CI, -1.75 to 0.53]; P = .29). Cases with bvAD showed a nonsignificant difference of worse memory performance compared with bvFTD (SMD, -1.31 [95% CI, -2.75 to 0.14]; P = .08) but did not differ from tAD (SMD, 0.43 [95% CI, -0.46 to 1.33]; P = .34). The neuroimaging literature revealed 2 distinct bvAD neuroimaging phenotypes: an AD-like pattern with relative frontal sparing and a relatively more bvFTD-like pattern characterized by additional anterior involvement, with the AD-like pattern being more prevalent. Conclusions and Relevance These data indicate that bvAD is clinically most similar to bvFTD, while it shares most pathophysiological features with tAD. Based on these insights, we propose research criteria for bvAD aimed at improving the consistency and reliability of future research and aiding the clinical assessment of this AD phenotype.
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Affiliation(s)
- Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands.,Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - Ellen H Singleton
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Colin Groot
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Anke A Dijkstra
- Department of Pathology, Amsterdam Neuroscience, Amsterdam University Medical Centre, Location VUMC, Amsterdam, the Netherlands
| | - Willem S Eikelboom
- Department of Neurology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco
| | - Bruce Miller
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco
| | - Robert Jr Laforce
- Clinique Interdisciplinaire de Mémoire, Centre Hospitalier Universitaire de Québec, Québec, Canada
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Janne M Papma
- Department of Neurology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco.,Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco.,Weill Institute for Neurosciences, University of California, San Francisco, San Francisco.,Associate Editor, JAMA Neurology
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
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19
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Whitwell JL, Tosakulwong N, Weigand SD, Graff-Radford J, Ertekin-Taner N, Machulda MM, Duffy JR, Schwarz CG, Senjem ML, Jack CR, Lowe VJ, Josephs KA. Relationship of APOE, age at onset, amyloid and clinical phenotype in Alzheimer disease. Neurobiol Aging 2021; 108:90-98. [PMID: 34551374 DOI: 10.1016/j.neurobiolaging.2021.08.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 08/13/2021] [Accepted: 08/18/2021] [Indexed: 11/26/2022]
Abstract
The apolipoprotein E (APOE) ε4 allele is the most well-established risk factor for Alzheimer's disease (AD), although its relationship to age at onset and clinical phenotype is unclear. We aimed to assess relationships between APOE genotype and age at onset, amyloid-beta (Aβ) deposition and typical versus atypical clinical presentations in AD. Frequency of APOE ε4 carriers by age at onset was assessed in 447 AD patients, 138 atypical AD patients recruited by the Neurodegenerative Research Group at Mayo Clinic, and 309 with typical AD from ADNI. APOE ε4 frequency increased with age at onset in atypical AD but showed a bell-shaped curve in typical AD where highest frequencies were observed between 65 and 70 years. Typical AD showed higher APOE ε4 frequencies than atypical AD only between the ages of 57 and 69 years. Global Aβ standard uptake value ratios did not differ according to APOE e4 status in either group. APOE genotype varies by both age at onset and clinical phenotype in AD, highlighting the heterogeneous nature of AD.
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Affiliation(s)
| | | | - Stephen D Weigand
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | | | | | - Mary M Machulda
- Department of Psychology and Psychiatry, Mayo Clinic, Rochester, MN, USA
| | - Joseph R Duffy
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA; Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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20
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Phillips JS, Nitchie FJ, Da Re F, Olm CA, Cook PA, McMillan CT, Irwin DJ, Gee JC, Dubroff JG, Grossman M, Nasrallah IM. Rates of longitudinal change in 18 F-flortaucipir PET vary by brain region, cognitive impairment, and age in atypical Alzheimer's disease. Alzheimers Dement 2021; 18:1235-1247. [PMID: 34515411 PMCID: PMC9292954 DOI: 10.1002/alz.12456] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 06/24/2021] [Accepted: 07/30/2021] [Indexed: 01/12/2023]
Abstract
Introduction Longitudinal positron emission tomography (PET) studies of tau accumulation in Alzheimer's disease (AD) have noted reduced increases or frank decreases in tau signal. We investigated how such reductions related to analytical confounds and disease progression markers in atypical AD. Methods We assessed regional and interindividual variation in longitudinal change on 18F‐flortaucipir PET imaging in 24 amyloid beta (Aβ)+ patients with atypical, early‐onset amnestic or non‐amnestic AD plus 62 Aβ– and 132 Aβ+ Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Results In atypical AD, 18F‐flortaucipir uptake slowed or declined over time in areas with high baseline signal and older, more impaired individuals. ADNI participants had reduced longitudinal change in early Braak stage regions relative to late‐stage areas. Discussion Results suggested radioligand uptake plateaus or declines in advanced neurodegeneration. Further research should investigate whether results generalize to other radioligands and whether they relate to changes of the radioligand binding site structure or accessibility.
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Affiliation(s)
| | | | - Fulvio Da Re
- University of Milan-Bicocca Faculty of Medicine and Surgery, Universita degli Studi di Milano-Bicocca Dipartimento di Medicina e Chirurgia, Milan, Italy
| | | | - Philip A Cook
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | - David J Irwin
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - James C Gee
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
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21
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Zhang L, Wang L, Gao J, Risacher SL, Yan J, Li G, Liu T, Zhu D. Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment. Med Image Anal 2021; 72:102082. [PMID: 34004495 DOI: 10.1016/j.media.2021.102082] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 03/20/2021] [Accepted: 04/13/2021] [Indexed: 01/22/2023]
Abstract
Multimodal fusion of different types of neural image data provides an irreplaceable opportunity to take advantages of complementary cross-modal information that may only partially be contained in single modality. To jointly analyze multimodal data, deep neural networks can be especially useful because many studies have suggested that deep learning strategy is very efficient to reveal complex and non-linear relations buried in the data. However, most deep models, e.g., convolutional neural network and its numerous extensions, can only operate on regular Euclidean data like voxels in 3D MRI. The interrelated and hidden structures that beyond the grid neighbors, such as brain connectivity, may be overlooked. Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied. In this work, we developed a graph-based deep neural network to simultaneously model brain structure and function in Mild Cognitive Impairment (MCI): the topology of the graph is initialized using structural network (from diffusion MRI) and iteratively updated by incorporating functional information (from functional MRI) to maximize the capability of differentiating MCI patients from elderly normal controls. This resulted in a new connectome by exploring "deep relations" between brain structure and function in MCI patients and we named it as Deep Brain Connectome. Though deep brain connectome is learned individually, it shows consistent patterns of alteration comparing to structural network at group level. With deep brain connectome, our developed deep model can achieve 92.7% classification accuracy on ADNI dataset.
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Affiliation(s)
- Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Li Wang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA; Department of Mathematics, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Jean Gao
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Jingwen Yan
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN 46202 USA
| | - Gang Li
- Biomedical Research Imaging Center and Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7160, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA.
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22
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Mendez MF, Khattab YI, Yerstein O. Impaired visual search in posterior cortical atrophy vs. typical Alzheimer's disease. J Neurol Sci 2021; 428:117574. [PMID: 34271285 DOI: 10.1016/j.jns.2021.117574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 07/05/2021] [Accepted: 07/08/2021] [Indexed: 10/20/2022]
Abstract
BACKGROUND Posterior cortical atrophy (PCA) is a neurocognitive disorder characterized by difficulty localizing in space. Recognizing PCA is important because it is usually missed early in its course and may result from a number of neurological disorders other than Alzheimer's disease (AD). OBJECTIVE This study aimed to clarify whether impaired visual search tasks of spatial localization distinguished patients with PCA from those with other more typical dementias as well as from healthy control (HC) subjects. METHODS Twelve patients meeting neuroimaging-supported Consensus Criteria for PCA, 12 comparably advanced patients with amnestic-predominant typical AD (tAD), and 24 HC participants were compared on tests of untimed and timed visual search, spatial neglect, mental rotation, environmental orientation, visuospatial construction, and face recognition. RESULTS Only abnormalities in untimed and timed visual search and environmental orientation distinguished the PCA patients from both the tAD group and the HC group without also distinguishing the tAD patients from HC's. The PCA patients also had a tendency to greater difficulty scanning left hemispace compared to HC's. Visuospatial constructions, although worse in PCA, and face recognition were impaired in both dementia groups. CONCLUSIONS These findings support the concept of PCA as a disorder of spatial processing and localization, indicating that visual search tasks are particularly sensitive and specific for detecting PCA and distinguishing it from more typical dementia syndromes.
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Affiliation(s)
- Mario F Mendez
- Departments of Neurology, David Geffen School of Medicine, University of California Los Angeles (UCLA), USA; Psychiatry and Behavioral Sciences, David Geffen School of Medicine, University of California Los Angeles (UCLA), USA; Neurology Service, Neurobehavior Unit, V.A. Greater Los Angeles Healthcare System, USA.
| | - Youssef I Khattab
- Departments of Neurology, David Geffen School of Medicine, University of California Los Angeles (UCLA), USA
| | - Oleg Yerstein
- Department of Neurology, Lahey Hospital and Medical Center, USA.
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23
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Graff-Radford J, Yong KXX, Apostolova LG, Bouwman FH, Carrillo M, Dickerson BC, Rabinovici GD, Schott JM, Jones DT, Murray ME. New insights into atypical Alzheimer's disease in the era of biomarkers. Lancet Neurol 2021; 20:222-234. [PMID: 33609479 PMCID: PMC8056394 DOI: 10.1016/s1474-4422(20)30440-3] [Citation(s) in RCA: 198] [Impact Index Per Article: 66.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 12/12/2022]
Abstract
Most patients with Alzheimer's disease present with amnestic problems; however, a substantial proportion, over-represented in young-onset cases, have atypical phenotypes including predominant visual, language, executive, behavioural, or motor dysfunction. In the past, these individuals often received a late diagnosis; however, availability of CSF and PET biomarkers of Alzheimer's disease pathologies and incorporation of atypical forms of Alzheimer's disease into new diagnostic criteria increasingly allows them to be more confidently diagnosed early in their illness. This early diagnosis in turn allows patients to be offered tailored information, appropriate care and support, and individualised treatment plans. These advances will provide improved access to clinical trials, which often exclude atypical phenotypes. Research into atypical Alzheimer's disease has revealed previously unrecognised neuropathological heterogeneity across the Alzheimer's disease spectrum. Neuroimaging, genetic, biomarker, and basic science studies are providing key insights into the factors that might drive selective vulnerability of differing brain networks, with potential mechanistic implications for understanding typical late-onset Alzheimer's disease.
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Affiliation(s)
| | - Keir X. X. Yong
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Liana G. Apostolova
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Femke H. Bouwman
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam University Medical Center
| | | | - Bradford C. Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Gil D. Rabinovici
- Departments of Neurology, Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Jonathan M. Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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24
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Yerstein O, Parand L, Liang LJ, Isaac A, Mendez MF. Benson's Disease or Posterior Cortical Atrophy, Revisited. J Alzheimers Dis 2021; 82:493-502. [PMID: 34057092 PMCID: PMC8316293 DOI: 10.3233/jad-210368] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND D. Frank Benson and colleagues first described the clinical and neuropathological features of posterior cortical atrophy (PCA) from patients in the UCLA Neurobehavior Program. OBJECTIVE We reviewed the Program's subsequent clinical experience with PCA, and its potential for clarifying this relatively rare syndrome in comparison to the accumulated literature on PCA. METHODS Using the original criteria derived from this clinic, 65 patients with neuroimaging-supported PCA were diagnosed between 1995 and 2020. RESULTS On presentation, most had visual localization complaints and related visuospatial symptoms, but nearly half had memory complaints followed by symptoms of depression. Neurobehavioral testing showed predominant difficulty with visuospatial constructions, Gerstmann's syndrome, and Balint's syndrome, but also impaired memory and naming. On retrospective application of the current Consensus Criteria for PCA, 59 (91%) met PCA criteria with a modification allowing for "significantly greater visuospatial over memory and naming deficits." There were 37 deaths (56.9%) with the median overall survival of 10.3 years (95% CI: 9.6-13.6 years), consistent with a slow neurodegenerative disorder in most patients. CONCLUSION Together, these findings recommend modifying the PCA criteria for "relatively spared" memory, language, and behavior to include secondary memory and naming difficulty and depression, with increased emphasis on the presence of Gerstmann's and Balint's syndromes.
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Affiliation(s)
- Oleg Yerstein
- Department of Neurology, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Leila Parand
- Department of Neurology, Behavioral Sciences, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
- Neurology Service, Neurobehavior Unit, V.A. Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Li-Jung Liang
- Department of Medicine, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Adrienne Isaac
- Department of Linguistics, Georgetown University, Washington, DC, USA
| | - Mario F. Mendez
- Department of Neurology, Behavioral Sciences, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
- Neurology Service, Neurobehavior Unit, V.A. Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Department of Psychiatry and Behavioral Sciences, David Geffen School of Medicine, University of California Los Angeles (UCLA), Los Angeles, CA, USA
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25
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Cousins KAQ, Irwin DJ, Wolk DA, Lee EB, Shaw LMJ, Trojanowski JQ, Da Re F, Gibbons GS, Grossman M, Phillips JS. ATN status in amnestic and non-amnestic Alzheimer's disease and frontotemporal lobar degeneration. Brain 2020; 143:2295-2311. [PMID: 32666090 DOI: 10.1093/brain/awaa165] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/27/2020] [Accepted: 03/27/2020] [Indexed: 12/13/2022] Open
Abstract
Under the ATN framework, CSF analytes provide evidence of the presence or absence of Alzheimer's disease pathological hallmarks: amyloid plaques (A), phosphorylated tau (T), and accompanying neurodegeneration (N). Still, differences in CSF levels across amnestic and non-amnestic variants or due to co-occurring pathologies might lead to misdiagnoses. We assess the diagnostic accuracy of CSF markers for amyloid, tau, and neurodegeneration in an autopsy cohort of 118 Alzheimer's disease patients (98 amnestic; 20 non-amnestic) and 64 frontotemporal lobar degeneration patients (five amnestic; 59 non-amnestic). We calculated between-group differences in CSF concentrations of amyloid-β1-42 peptide, tau protein phosphorylated at threonine 181, total tau, and the ratio of phosphorylated tau to amyloid-β1-42. Results show that non-amnestic Alzheimer's disease patients were less likely to be correctly classified under the ATN framework using independent, published biomarker cut-offs for positivity. Amyloid-β1-42 did not differ between amnestic and non-amnestic Alzheimer's disease, and receiver operating characteristic curve analyses indicated that amyloid-β1-42 was equally effective in discriminating both groups from frontotemporal lobar degeneration. However, CSF concentrations of phosphorylated tau, total tau, and the ratio of phosphorylated tau to amyloid-β1-42 were significantly lower in non-amnestic compared to amnestic Alzheimer's disease patients. Receiver operating characteristic curve analyses for these markers showed reduced area under the curve when discriminating non-amnestic Alzheimer's disease from frontotemporal lobar degeneration, compared to discrimination of amnestic Alzheimer's disease from frontotemporal lobar degeneration. In addition, the ATN framework was relatively insensitive to frontotemporal lobar degeneration, and these patients were likely to be classified as having normal biomarkers or biomarkers suggestive of primary Alzheimer's disease pathology. We conclude that amyloid-β1-42 maintains high sensitivity to A status, although with lower specificity, and this single biomarker provides better sensitivity to non-amnestic Alzheimer's disease than either the ATN framework or the phosphorylated-tau/amyloid-β1-42 ratio. In contrast, T and N status biomarkers differed between amnestic and non-amnestic Alzheimer's disease; standard cut-offs for phosphorylated tau and total tau may thus result in misclassifications for non-amnestic Alzheimer's disease patients. Consideration of clinical syndrome may help improve the accuracy of ATN designations for identifying true non-amnestic Alzheimer's disease.
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Affiliation(s)
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - David A Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA
| | - Leslie M J Shaw
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA
| | - Fulvio Da Re
- School of Medicine and Surgery, Milan Center for Neuroscience, University of Milano-Bicocca, Milan, Italy
| | - Garrett S Gibbons
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, USA
| | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
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26
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Sintini I, Graff-Radford J, Senjem ML, Schwarz CG, Machulda MM, Martin PR, Jones DT, Boeve BF, Knopman DS, Kantarci K, Petersen RC, Jack CR, Lowe VJ, Josephs KA, Whitwell JL. Longitudinal neuroimaging biomarkers differ across Alzheimer's disease phenotypes. Brain 2020; 143:2281-2294. [PMID: 32572464 DOI: 10.1093/brain/awaa155] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 03/11/2020] [Accepted: 03/27/2020] [Indexed: 11/12/2022] Open
Abstract
Alzheimer's disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer's disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer's disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer's disease (18 with typical amnestic Alzheimer's disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer's disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer's disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer's disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer's disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer's disease.
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Affiliation(s)
- Irene Sintini
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Matthew L Senjem
- Department of Radiology, Mayo Clinic, Rochester, MN, USA.,Department of Information Technology, Mayo Clinic, Rochester, MN, USA
| | | | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester MN, USA
| | - Peter R Martin
- Department of Health Science Research, Mayo Clinic, Rochester MN, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
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27
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Ramanan S, Roquet D, Goldberg ZL, Hodges JR, Piguet O, Irish M, Lambon Ralph MA. Establishing two principal dimensions of cognitive variation in logopenic progressive aphasia. Brain Commun 2020; 2:fcaa125. [PMID: 33376980 PMCID: PMC7750924 DOI: 10.1093/braincomms/fcaa125] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 06/25/2020] [Accepted: 07/13/2020] [Indexed: 12/28/2022] Open
Abstract
Logopenic progressive aphasia is a neurodegenerative syndrome characterized by sentence repetition and naming difficulties arising from left-lateralized temporoparietal atrophy. Clinical descriptions of logopenic progressive aphasia largely concentrate on profiling language deficits, however, accumulating evidence points to the presence of cognitive deficits even on tasks with minimal language demands. Although non-linguistic cognitive deficits in logopenic progressive aphasia are thought to scale with disease severity, patients at discrete stages of language dysfunction display overlapping cognitive profiles, suggesting individual-level variation in cognitive performance, independent of primary language dysfunction. To address this issue, we used principal component analysis to decompose the individual-level variation in cognitive performance in 43 well-characterized logopenic progressive aphasia patients who underwent multi-domain neuropsychological assessments and structural neuroimaging. The principal component analysis solution revealed the presence of two, statistically independent factors, providing stable and clinically intuitive explanations for the majority of variance in cognitive performance in the syndrome. Factor 1 reflected 'speech production and verbal memory' deficits which typify logopenic progressive aphasia. Systematic variations were also confirmed on a second, orthogonal factor mainly comprising visuospatial and executive processes. Adopting a case-comparison approach, we further demonstrate that pairs of patients with comparable Factor 1 scores, regardless of their severity, diverge considerably on visuo-executive test performance, underscoring the inter-individual variability in cognitive profiles in comparably 'logopenic' patients. Whole-brain voxel-based morphometry analyses revealed that speech production and verbal memory factor scores correlated with left middle frontal gyrus, while visuospatial and executive factor scores were associated with grey matter intensity of right-lateralized temporoparietal, middle frontal regions and their underlying white matter connectivity. Importantly, logopenic progressive aphasia patients with poorer visuospatial and executive factor scores demonstrated greater right-lateralized temporoparietal and frontal atrophy. Our findings demonstrate the inherent variation in cognitive performance at an individual- and group-level in logopenic progressive aphasia, suggesting the presence of a genuine co-occurring cognitive impairment that is statistically independent of language function and disease severity.
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Affiliation(s)
- Siddharth Ramanan
- The University of Sydney, Brain and Mind Centre, Sydney, NSW, Australia
- The University of Sydney, School of Psychology, Sydney, NSW, Australia
- ARC Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
| | - Daniel Roquet
- The University of Sydney, Brain and Mind Centre, Sydney, NSW, Australia
- The University of Sydney, School of Psychology, Sydney, NSW, Australia
- ARC Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
| | - Zoë-Lee Goldberg
- The University of Sydney, Brain and Mind Centre, Sydney, NSW, Australia
| | - John R Hodges
- The University of Sydney, Brain and Mind Centre, Sydney, NSW, Australia
- ARC Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
- The University of Sydney, School of Medical Sciences, Sydney, NSW, Australia
| | - Olivier Piguet
- The University of Sydney, Brain and Mind Centre, Sydney, NSW, Australia
- The University of Sydney, School of Psychology, Sydney, NSW, Australia
- ARC Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
| | - Muireann Irish
- The University of Sydney, Brain and Mind Centre, Sydney, NSW, Australia
- The University of Sydney, School of Psychology, Sydney, NSW, Australia
- ARC Centre of Excellence in Cognition and its Disorders, Sydney, NSW, Australia
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28
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Groot C, Yeo BTT, Vogel JW, Zhang X, Sun N, Mormino EC, Pijnenburg YAL, Miller BL, Rosen HJ, La Joie R, Barkhof F, Scheltens P, van der Flier WM, Rabinovici GD, Ossenkoppele R. Latent atrophy factors related to phenotypical variants of posterior cortical atrophy. Neurology 2020; 95:e1672-e1685. [PMID: 32675078 PMCID: PMC7713727 DOI: 10.1212/wnl.0000000000010362] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 04/06/2020] [Indexed: 01/30/2023] Open
Abstract
OBJECTIVE To determine whether atrophy relates to phenotypical variants of posterior cortical atrophy (PCA) recently proposed in clinical criteria (i.e., dorsal, ventral, dominant-parietal, and caudal) we assessed associations between latent atrophy factors and cognition. METHODS We employed a data-driven Bayesian modeling framework based on latent Dirichlet allocation to identify latent atrophy factors in a multicenter cohort of 119 individuals with PCA (age 64 ± 7 years, 38% male, Mini-Mental State Examination 21 ± 5, 71% β-amyloid positive, 29% β-amyloid status unknown). The model uses standardized gray matter density images as input (adjusted for age, sex, intracranial volume, MRI scanner field strength, and whole-brain gray matter volume) and provides voxelwise probabilistic maps for a predetermined number of atrophy factors, allowing every individual to express each factor to a degree without a priori classification. Individual factor expressions were correlated to 4 PCA-specific cognitive domains (object perception, space perception, nonvisual/parietal functions, and primary visual processing) using general linear models. RESULTS The model revealed 4 distinct yet partially overlapping atrophy factors: right-dorsal, right-ventral, left-ventral, and limbic. We found that object perception and primary visual processing were associated with atrophy that predominantly reflects the right-ventral factor. Furthermore, space perception was associated with atrophy that predominantly represents the right-dorsal and right-ventral factors. However, individual participant profiles revealed that the large majority expressed multiple atrophy factors and had mixed clinical profiles with impairments across multiple domains, rather than displaying a discrete clinical-radiologic phenotype. CONCLUSION Our results indicate that specific brain behavior networks are vulnerable in PCA, but most individuals display a constellation of affected brain regions and symptoms, indicating that classification into 4 mutually exclusive variants is unlikely to be clinically useful.
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Affiliation(s)
- Colin Groot
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden.
| | - B T Thomas Yeo
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Jacob W Vogel
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Xiuming Zhang
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Nanbo Sun
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Elizabeth C Mormino
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Yolande A L Pijnenburg
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Bruce L Miller
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Howard J Rosen
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Renaud La Joie
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Frederik Barkhof
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Philip Scheltens
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Wiesje M van der Flier
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Gil D Rabinovici
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
| | - Rik Ossenkoppele
- From the Department of Neurology and Alzheimer Center (C.G., Y.A.L.P., P.S., W.M.v.d.F., R.O.), and Departments of Radiology and Nuclear Medicine (F.B.) and Epidemiology and Biostatistics (W.M.v.d.F.), Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands; Department of Electrical and Computer Engineering (B.T.T.Y., X.Z., N.S.), Clinical Imaging Research Centre, N1 Institute for Health and Memory Networks Program, National University of Singapore; Montreal Neurological Institute (J.W.V.), McGill University, Montreal, Canada; Computer Science and Artificial Intelligence Laboratory (X.Z.), Massachusetts Institute of Technology, Cambridge; Department of Neurology and Neurological Sciences (E.C.M.), Stanford University, CA; Departments of Neurology, Radiology and Biomedical Imaging (B.L.M., H.J.R., R.L.J., G.D.R.), University of California, San Francisco; Institutes of Neurology & Healthcare Engineering (F.B.), University College London, UK; and Clinical Memory Research Unit (R.O.), Lund University, Sweden
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29
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Hardy CJD, Yong KXX, Goll JC, Crutch SJ, Warren JD. Impairments of auditory scene analysis in posterior cortical atrophy. Brain 2020; 143:2689-2695. [PMID: 32875326 PMCID: PMC7523698 DOI: 10.1093/brain/awaa221] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 05/27/2020] [Accepted: 05/27/2020] [Indexed: 01/08/2023] Open
Abstract
Although posterior cortical atrophy is often regarded as the canonical 'visual dementia', auditory symptoms may also be salient in this disorder. Patients often report particular difficulty hearing in busy environments; however, the core cognitive process-parsing of the auditory environment ('auditory scene analysis')-has been poorly characterized. In this cross-sectional study, we used customized perceptual tasks to assess two generic cognitive operations underpinning auditory scene analysis-sound source segregation and sound event grouping-in a cohort of 21 patients with posterior cortical atrophy, referenced to 15 healthy age-matched individuals and 21 patients with typical Alzheimer's disease. After adjusting for peripheral hearing function and performance on control tasks assessing perceptual and executive response demands, patients with posterior cortical atrophy performed significantly worse on both auditory scene analysis tasks relative to healthy controls and patients with typical Alzheimer's disease (all P < 0.05). Our findings provide further evidence of central auditory dysfunction in posterior cortical atrophy, with implications for our pathophysiological understanding of Alzheimer syndromes as well as clinical diagnosis and management.
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Affiliation(s)
- Chris J D Hardy
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Keir X X Yong
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Johanna C Goll
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Sebastian J Crutch
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Jason D Warren
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
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30
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Vanhoutte M, Semah F, Leclerc X, Sillaire AR, Jaillard A, Kuchcinski G, Delbeuck X, Fahmi R, Pasquier F, Lopes R. Three-year changes of cortical 18F-FDG in amnestic vs. non-amnestic sporadic early-onset Alzheimer's disease. Eur J Nucl Med Mol Imaging 2019; 47:304-318. [PMID: 31606833 DOI: 10.1007/s00259-019-04519-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/30/2019] [Indexed: 11/27/2022]
Abstract
PURPOSE To examine and compare longitudinal changes of cortical glucose metabolism in amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease and assess potential associations with neuropsychological performance over a 3-year period time. METHODS Eighty-two participants meeting criteria for early-onset (< 65 years) sporadic form of probable Alzheimer's disease and presenting with a variety of clinical phenotypes (47 amnestic and 35 non-amnestic forms) were included at baseline and followed up for 1.44 ± 1.23 years. All of the participants underwent a work-up at baseline and every year during the follow-up period, which includes clinical examination, neuropsychological testing, genotyping, cerebrospinal fluid biomarker assays, and structural MRI and 18F-FDG PET. Vertex-wise partial volume-corrected glucose metabolic maps across the entire cortical surface were generated and longitudinally assessed together with the neuropsychological scores using linear mixed-effects modeling as a function of amnestic and non-amnestic sporadic forms of early-onset Alzheimer's disease. RESULTS Similar evolution patterns of glucose metabolic decline between amnestic and non-amnestic forms were observed in widespread neocortical cortices. However, only non-amnestic forms appeared to have a greater reduction of glucose metabolism in lateral orbitofrontal and bilateral medial temporal cortices associated with more severe declines of neuropsychological performance compared with amnestic forms. Furthermore, results suggest that glucose metabolic decline in amnestic forms would progress along an anterior-to-posterior axis, whereas glucose metabolic decline in non-amnestic forms would progress along a posterior-to-anterior axis. CONCLUSIONS We found differences in spatial distribution and temporal trajectory of glucose metabolic decline between amnestic and non-amnestic early-onset Alzheimer's disease groups, suggesting that one might want to consider treating the two forms of the disease as two separate entities.
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Affiliation(s)
- Matthieu Vanhoutte
- Inserm U1171, CHU Lille, University of Lille, F-59000, Lille, France. .,Department of Nuclear Medicine, CHU Lille, F-59000, Lille, France. .,Department of Neuroradiology, CHU Lille, F-59000, Lille, France.
| | - Franck Semah
- Inserm U1171, CHU Lille, University of Lille, F-59000, Lille, France.,Department of Nuclear Medicine, CHU Lille, F-59000, Lille, France
| | - Xavier Leclerc
- Inserm U1171, CHU Lille, University of Lille, F-59000, Lille, France.,Department of Neuroradiology, CHU Lille, F-59000, Lille, France
| | - Adeline Rollin Sillaire
- Department of Neurology, CHU Lille, F-59000, Lille, France.,Inserm U1171, CHU Lille, Memory Center, DISTALZ, University of Lille, F-59000, Lille, France
| | - Alice Jaillard
- Inserm U1171, CHU Lille, University of Lille, F-59000, Lille, France.,Department of Nuclear Medicine, CHU Lille, F-59000, Lille, France
| | - Grégory Kuchcinski
- Inserm U1171, CHU Lille, University of Lille, F-59000, Lille, France.,Department of Neuroradiology, CHU Lille, F-59000, Lille, France
| | - Xavier Delbeuck
- Inserm U1171, CHU Lille, Memory Center, DISTALZ, University of Lille, F-59000, Lille, France.,Department of Neuropsychology, CHU Lille, F-59000, Lille, France
| | - Rachid Fahmi
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - Florence Pasquier
- Department of Neurology, CHU Lille, F-59000, Lille, France.,Inserm U1171, CHU Lille, Memory Center, DISTALZ, University of Lille, F-59000, Lille, France
| | - Renaud Lopes
- Inserm U1171, CHU Lille, University of Lille, F-59000, Lille, France.,Department of Neuroradiology, CHU Lille, F-59000, Lille, France
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31
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Guerrier L, Cransac C, Pages B, Saint-Aubert L, Payoux P, Péran P, Pariente J. Posterior Cortical Atrophy: Does Complaint Match the Impairment? A Neuropsychological and FDG-PET Study. Front Neurol 2019; 10:1010. [PMID: 31616363 PMCID: PMC6764288 DOI: 10.3389/fneur.2019.01010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Accepted: 09/04/2019] [Indexed: 12/12/2022] Open
Abstract
Objective: Posterior Cortical Atrophy (PCA) is a neurodegenerative disease characterized predominantly by visual impairment. However, diagnosis of PCA remains complicated with an interval of several years between initial reporting of symptoms and diagnosis. The aim of the present study is to define if patients' visual and gestural complaints are consistent with their clinical profile. Method: An evaluation of daily visual problems as well as a full neuropsychological assessment and FDG-PET were performed in 15 PCA patients. We compared glucose metabolism between these PCA patients and 18 healthy controls. Correlation analyses were conducted in PCA patients between visual and gestural complaint, clinical impairments, and brain glucose metabolism. Results: Major impairment of cognitive functions was detected in PCA patients specifically in visual domains. Positive correlations were found between visual impairments and hypometabolism in the right temporo-parieto-occipital cortices. However, no correlation was found between complaint and visual impairment in PCA patients. Discussion: Our main results suggest a consistent relationship between clinical impairment and brain metabolism. However, the patient's complaint and visual performance are not linked. Combining the literature and our results, it seems that patients are generally aware of difficulties but misinterpret them. This misinterpretation may be responsible for the delayed diagnosis.
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Affiliation(s)
- Laura Guerrier
- ToNIC, Toulouse NeuroImaging Centre, University of Toulouse, Inserm, UPS, Toulouse, France
| | - Camille Cransac
- Department of Neurology, University Hospital of Toulouse, Toulouse, France
| | - Bérengère Pages
- Department of Neurology, University Hospital of Toulouse, Toulouse, France
| | - Laure Saint-Aubert
- ToNIC, Toulouse NeuroImaging Centre, University of Toulouse, Inserm, UPS, Toulouse, France.,Department of Nuclear Medicine, University Hospital of Toulouse, Toulouse, France
| | - Pierre Payoux
- ToNIC, Toulouse NeuroImaging Centre, University of Toulouse, Inserm, UPS, Toulouse, France.,Department of Nuclear Medicine, University Hospital of Toulouse, Toulouse, France
| | - Patrice Péran
- ToNIC, Toulouse NeuroImaging Centre, University of Toulouse, Inserm, UPS, Toulouse, France
| | - Jérémie Pariente
- ToNIC, Toulouse NeuroImaging Centre, University of Toulouse, Inserm, UPS, Toulouse, France.,Department of Neurology, University Hospital of Toulouse, Toulouse, France
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