1
|
Ingram RU, Ocal D, Halai A, Pobric G, Cash DM, Crutch S, Yong KX, Lambon Ralph MA. Graded Multidimensional Clinical and Radiologic Variation in Patients With Alzheimer Disease and Posterior Cortical Atrophy. Neurology 2024; 103:e209679. [PMID: 39042846 PMCID: PMC11314952 DOI: 10.1212/wnl.0000000000209679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 05/17/2024] [Indexed: 07/25/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Alzheimer disease (AD) spans heterogeneous typical and atypical phenotypes. Posterior cortical atrophy (PCA) is a striking example, characterized by prominent impairment in visual and other posterior functions in contrast to typical, amnestic AD. The primary study objective was to establish how the similarities and differences of cognition and brain volumes within AD and PCA (and by extension other AD variants) can be conceptualized as systematic variations across a transdiagnostic, graded multidimensional space. METHODS This was a cross-sectional, single-center, observational, cohort study performed at the National Hospital for Neurology & Neurosurgery, London, United Kingdom. Data were collected from a cohort of patients with PCA and AD, matched for age, disease duration, and Mini-Mental State Examination (MMSE) scores. There were 2 sets of outcome measures: (1) scores on a neuropsychological battery containing 22 tests spanning visuoperceptual and visuospatial processing, episodic memory, language, executive functions, calculation, and visuospatial processing and (2) measures extracted from high-resolution T1-weighted volumetric MRI scans. Principal component analysis was used to extract the transdiagnostic dimensions of phenotypical variation from the detailed neuropsychological data. Voxel-based morphometry was used to examine associations between the PCA-derived clinical phenotypes and the structural measures. RESULTS We enrolled 93 participants with PCA (mean: age = 59.9 years, MMSE = 21.2; 59/93 female) and 58 AD participants (mean: age = 57.1 years, MMSE = 19.7; 22/58 female). The principal component analysis for PCA (sample adequacy confirmed: Kaiser-Meyer-Olkin = 0.865) extracted 3 dimensions accounting for 61.0% of variance in patients' performance, reflecting general cognitive impairment, visuoperceptual deficits, and visuospatial impairments. Plotting AD cases into the PCA-derived multidimensional space, and vice versa, revealed graded, overlapping variations between cases along these dimensions, with no evidence for categorical-like patient clustering. Similarly, the relationship between brain volumes and scores on the extracted dimensions was overlapping for PCA and AD cases. DISCUSSION These results provide evidence supporting a reconceptualization of clinical and radiologic variation in these heterogenous AD phenotypes as being along shared phenotypic continua spanning PCA and AD, arising from systematic graded variations within a transdiagnostic, multidimensional neurocognitive geometry.
Collapse
Affiliation(s)
- Ruth U Ingram
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| | - Dilek Ocal
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| | - Ajay Halai
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| | - Gorana Pobric
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| | - David M Cash
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| | - Sebastian Crutch
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| | - Keir X Yong
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| | - Matthew A Lambon Ralph
- From the Division of Psychology and Mental Health (R.U.I., G.P.), University of Manchester; Dementia Research Centre (D.O., D.M.C., S.C., K.X.Y.), UCL Institute of Neurology, London; and MRC Cognition and Brain Sciences Unit (A.H., M.A.L.R.), University of Cambridge, United Kingdom
| |
Collapse
|
2
|
Shir D, Corriveau-Lecavalier N, Bermudez Noguera C, Barnard L, Pham NTT, Botha H, Duffy JR, Clark HM, Utianski RL, Knopman DS, Petersen RC, Boeve BF, Murray ME, Nguyen AT, Reichard RR, Dickson DW, Day GS, Kremers WK, Graff-Radford NR, Jones DT, Machulda MM, Fields JA, Whitwell JL, Josephs KA, Graff-Radford J. Clinicoradiological and neuropathological evaluation of primary progressive aphasia. J Neurol Neurosurg Psychiatry 2024; 95:812-821. [PMID: 38514176 PMCID: PMC11330364 DOI: 10.1136/jnnp-2023-332862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/28/2024] [Indexed: 03/23/2024]
Abstract
BACKGROUND Primary progressive aphasia (PPA) defines a group of neurodegenerative disorders characterised by language decline. Three PPA variants correlate with distinct underlying pathologies: semantic variant PPA (svPPA) with transactive response DNA-binding protein of 43 kD (TDP-43) proteinopathy, agrammatic variant PPA (agPPA) with tau deposition and logopenic variant PPA (lvPPA) with Alzheimer's disease (AD). Our objectives were to differentiate PPA variants using clinical and neuroimaging features, assess progression and evaluate structural MRI and a novel 18-F fluorodeoxyglucose positron emission tomography (FDG-PET) image decomposition machine learning algorithm for neuropathology prediction. METHODS We analysed 82 autopsied patients diagnosed with PPA from 1998 to 2022. Clinical histories, language characteristics, neuropsychological results and brain imaging were reviewed. A machine learning framework using a k-nearest neighbours classifier assessed FDG-PET scans from 45 patients compared with a large reference database. RESULTS PPA variant distribution: 35 lvPPA (80% AD), 28 agPPA (89% tauopathy) and 18 svPPA (72% frontotemporal lobar degeneration-TAR DNA-binding protein (FTLD-TDP)). Apraxia of speech was associated with 4R-tauopathy in agPPA, while pure agrammatic PPA without apraxia was linked to 3R-tauopathy. Longitudinal data revealed language dysfunction remained the predominant deficit for patients with lvPPA, agPPA evolved to corticobasal or progressive supranuclear palsy syndrome (64%) and svPPA progressed to behavioural variant frontotemporal dementia (44%). agPPA-4R-tauopathy exhibited limited pre-supplementary motor area atrophy, lvPPA-AD displayed temporal atrophy extending to the superior temporal sulcus and svPPA-FTLD-TDP had severe temporal pole atrophy. The FDG-PET-based machine learning algorithm accurately predicted clinical diagnoses and underlying pathologies. CONCLUSIONS Distinguishing 3R-taupathy and 4R-tauopathy in agPPA may rely on apraxia of speech presence. Additional linguistic and clinical features can aid neuropathology prediction. Our data-driven brain metabolism decomposition approach effectively predicts underlying neuropathology.
Collapse
Affiliation(s)
- Dror Shir
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Leland Barnard
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Joseph R Duffy
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Heather M Clark
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Rene L Utianski
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Aivi T Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - R Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Gregory S Day
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, Rochester, Minnesota, USA
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mary M Machulda
- Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | - Julie A Fields
- Psychiatry and Psychology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Keith A Josephs
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | |
Collapse
|
3
|
Yoon JH, Lee H, Kwon D, Lee D, Lee S, Cho E, Kim J, Kim D. Integrative approach of omics and imaging data to discover new insights for understanding brain diseases. Brain Commun 2024; 6:fcae265. [PMID: 39165479 PMCID: PMC11334939 DOI: 10.1093/braincomms/fcae265] [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: 12/11/2023] [Revised: 06/03/2024] [Accepted: 08/07/2024] [Indexed: 08/22/2024] Open
Abstract
Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.
Collapse
Affiliation(s)
- Jong Hyuk Yoon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Hagyeong Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Dayoung Kwon
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Dongha Lee
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Seulah Lee
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Eunji Cho
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Jaehoon Kim
- Neurodegenerative Diseases Research Group, Korea Brain Research Institute, Daegu 41062, Republic of Korea
| | - Dayea Kim
- New Drug Development Center, Daegu-Gyeongbuk Medical Innovation Foundation (K-MEDI hub), Daegu 41061, Republic of Korea
| |
Collapse
|
4
|
Pramotton FM, Spitz S, Kamm RD. Challenges and Future Perspectives in Modeling Neurodegenerative Diseases Using Organ-on-a-Chip Technology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2403892. [PMID: 38922799 PMCID: PMC11348103 DOI: 10.1002/advs.202403892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/01/2024] [Indexed: 06/28/2024]
Abstract
Neurodegenerative diseases (NDDs) affect more than 50 million people worldwide, posing a significant global health challenge as well as a high socioeconomic burden. With aging constituting one of the main risk factors for some NDDs such as Alzheimer's disease (AD) and Parkinson's disease (PD), this societal toll is expected to rise considering the predicted increase in the aging population as well as the limited progress in the development of effective therapeutics. To address the high failure rates in clinical trials, legislative changes permitting the use of alternatives to traditional pre-clinical in vivo models are implemented. In this regard, microphysiological systems (MPS) such as organ-on-a-chip (OoC) platforms constitute a promising tool, due to their ability to mimic complex and human-specific tissue niches in vitro. This review summarizes the current progress in modeling NDDs using OoC technology and discusses five critical aspects still insufficiently addressed in OoC models to date. Taking these aspects into consideration in the future MPS will advance the modeling of NDDs in vitro and increase their translational value in the clinical setting.
Collapse
Affiliation(s)
- Francesca Michela Pramotton
- Department of Mechanical Engineering and Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Sarah Spitz
- Department of Mechanical Engineering and Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Roger D. Kamm
- Department of Mechanical Engineering and Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| |
Collapse
|
5
|
Bao J, Lee BN, Wen J, Kim M, Mu S, Yang S, Davatzikos C, Long Q, Ritchie MD, Shen L. Employing Informatics Strategies in Alzheimer's Disease Research: A Review from Genetics, Multiomics, and Biomarkers to Clinical Outcomes. Annu Rev Biomed Data Sci 2024; 7:391-418. [PMID: 38848574 DOI: 10.1146/annurev-biodatasci-102423-121021] [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] [Indexed: 06/09/2024]
Abstract
Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.
Collapse
Affiliation(s)
- Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Brian N Lee
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Junhao Wen
- Laboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA
| | - Mansu Kim
- AI Graduate School, Gwangju Institute of Science and Technology, Gwangju, South Korea
| | - Shizhuo Mu
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Shu Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Qi Long
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA;
| |
Collapse
|
6
|
Li W, Varatharajah Y, Dicks E, Barnard L, Brinkmann BH, Crepeau D, Worrell G, Fan W, Kremers W, Boeve B, Botha H, Gogineni V, Jones DT. Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases. Brain Commun 2024; 6:fcae227. [PMID: 39086629 PMCID: PMC11289732 DOI: 10.1093/braincomms/fcae227] [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: 06/06/2023] [Revised: 05/11/2024] [Accepted: 06/28/2024] [Indexed: 08/02/2024] Open
Abstract
Electrophysiologic disturbances due to neurodegenerative disorders such as Alzheimer's disease and Lewy Body disease are detectable by scalp EEG and can serve as a functional measure of disease severity. Traditional quantitative methods of EEG analysis often require an a-priori selection of clinically meaningful EEG features and are susceptible to bias, limiting the clinical utility of routine EEGs in the diagnosis and management of neurodegenerative disorders. We present a data-driven tensor decomposition approach to extract the top 6 spectral and spatial features representing commonly known sources of EEG activity during eyes-closed wakefulness. As part of their neurologic evaluation at Mayo Clinic, 11 001 patients underwent 12 176 routine, standard 10-20 scalp EEG studies. From these raw EEGs, we developed an algorithm based on posterior alpha activity and eye movement to automatically select awake-eyes-closed epochs and estimated average spectral power density (SPD) between 1 and 45 Hz for each channel. We then created a three-dimensional (3D) tensor (record × channel × frequency) and applied a canonical polyadic decomposition to extract the top six factors. We further identified an independent cohort of patients meeting consensus criteria for mild cognitive impairment (30) or dementia (39) due to Alzheimer's disease and dementia with Lewy Bodies (31) and similarly aged cognitively normal controls (36). We evaluated the ability of the six factors in differentiating these subgroups using a Naïve Bayes classification approach and assessed for linear associations between factor loadings and Kokmen short test of mental status scores, fluorodeoxyglucose (FDG) PET uptake ratios and CSF Alzheimer's Disease biomarker measures. Factors represented biologically meaningful brain activities including posterior alpha rhythm, anterior delta/theta rhythms and centroparietal beta, which correlated with patient age and EEG dysrhythmia grade. These factors were also able to distinguish patients from controls with a moderate to high degree of accuracy (Area Under the Curve (AUC) 0.59-0.91) and Alzheimer's disease dementia from dementia with Lewy Bodies (AUC 0.61). Furthermore, relevant EEG features correlated with cognitive test performance, PET metabolism and CSF AB42 measures in the Alzheimer's subgroup. This study demonstrates that data-driven approaches can extract biologically meaningful features from population-level clinical EEGs without artefact rejection or a-priori selection of channels or frequency bands. With continued development, such data-driven methods may improve the clinical utility of EEG in memory care by assisting in early identification of mild cognitive impairment and differentiating between different neurodegenerative causes of cognitive impairment.
Collapse
Affiliation(s)
- Wentao Li
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Neurology, Kaiser Permanente Northern California, Sacramento, CA 95758, USA
| | - Yogatheesan Varatharajah
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Bioengineering, University of Illinois, Urbana, IL 61801, USA
- Department of Computer Science & Engineering, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Leland Barnard
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Daniel Crepeau
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Gregory Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Winnie Fan
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Walter Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
7
|
Ali F, Clark H, Machulda M, Senjem ML, Lowe VJ, Jack CR, Josephs KA, Whitwell J, Botha H. Patterns of brain volume and metabolism predict clinical features in the progressive supranuclear palsy spectrum. Brain Commun 2024; 6:fcae233. [PMID: 39056025 PMCID: PMC11272075 DOI: 10.1093/braincomms/fcae233] [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: 04/20/2023] [Revised: 03/26/2024] [Accepted: 07/14/2024] [Indexed: 07/28/2024] Open
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative tauopathy that presents with highly heterogenous clinical syndromes. We perform cross-sectional data-driven discovery of independent patterns of brain atrophy and hypometabolism across the entire PSP spectrum. We then use these patterns to predict specific clinical features and to assess their relationship to phenotypic heterogeneity. We included 111 patients with PSP (60 with Richardson syndrome and 51 with cortical and subcortical variant subtypes). Ninety-one were used as the training set and 20 as a test set. The presence and severity of granular clinical variables such as postural instability, parkinsonism, apraxia and supranuclear gaze palsy were noted. Domains of akinesia, ocular motor impairment, postural instability and cognitive dysfunction as defined by the Movement Disorders Society criteria for PSP were also recorded. Non-negative matrix factorization was used on cross-sectional MRI and fluorodeoxyglucose-positron emission tomography (FDG-PET) scans. Independent models for each as well as a combined model for MRI and FDG-PET were developed and used to predict the granular clinical variables. Both MRI and FDG-PET were better at predicting presence of a symptom than severity, suggesting identification of disease state may be more robust than disease stage. FDG-PET predicted predominantly cortical abnormalities better than MRI such as ideomotor apraxia, apraxia of speech and frontal dysexecutive syndrome. MRI demonstrated prediction of cortical and more so sub-cortical abnormalities, such as parkinsonism. Distinct neuroanatomical foci were predictive in MRI- and FDG-PET-based models. For example, vertical gaze palsy was predicted by midbrain atrophy on MRI, but frontal eye field hypometabolism on FDG-PET. Findings also differed by scale or instrument used. For example, prediction of ocular motor abnormalities using the PSP Saccadic Impairment Scale was stronger than with the Movement Disorders Society Diagnostic criteria for PSP oculomotor impairment designation. Combination of MRI and FDG-PET demonstrated enhanced detection of parkinsonism and frontal syndrome presence and apraxia, cognitive impairment and bradykinesia severity. Both MRI and FDG-PET patterns were able to predict some measures in the test set; however, prediction of global cognition measured by Montreal Cognitive Assessment was the strongest. MRI predictions generalized more robustly to the test set. PSP leads to neurodegeneration in motor, cognitive and ocular motor networks at cortical and subcortical foci, leading to diverse yet overlapping clinical syndromes. To advance understanding of phenotypic heterogeneity in PSP, it is essential to consider data-driven approaches to clinical neuroimaging analyses.
Collapse
Affiliation(s)
- Farwa Ali
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather Clark
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Keith A Josephs
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
8
|
Chauhan K, Neiman AB, Tass PA. Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity. PLoS Comput Biol 2024; 20:e1012261. [PMID: 38980898 PMCID: PMC11259284 DOI: 10.1371/journal.pcbi.1012261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 07/19/2024] [Accepted: 06/20/2024] [Indexed: 07/11/2024] Open
Abstract
Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.
Collapse
Affiliation(s)
- Kanishk Chauhan
- Department of Physics and Astronomy, Ohio University, Athens, Ohio, United States of America
- Neuroscience Program, Ohio University, Athens, Ohio, United States of America
| | - Alexander B. Neiman
- Department of Physics and Astronomy, Ohio University, Athens, Ohio, United States of America
- Neuroscience Program, Ohio University, Athens, Ohio, United States of America
| | - Peter A. Tass
- Department of Neurosurgery, Stanford University, Stanford, California, United States of America
| |
Collapse
|
9
|
Ottoy J, Kang MS, Tan JXM, Boone L, Vos de Wael R, Park BY, Bezgin G, Lussier FZ, Pascoal TA, Rahmouni N, Stevenson J, Fernandez Arias J, Therriault J, Hong SJ, Stefanovic B, McLaurin J, Soucy JP, Gauthier S, Bernhardt BC, Black SE, Rosa-Neto P, Goubran M. Tau follows principal axes of functional and structural brain organization in Alzheimer's disease. Nat Commun 2024; 15:5031. [PMID: 38866759 PMCID: PMC11169286 DOI: 10.1038/s41467-024-49300-2] [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: 09/22/2023] [Accepted: 05/24/2024] [Indexed: 06/14/2024] Open
Abstract
Alzheimer's disease (AD) is a brain network disorder where pathological proteins accumulate through networks and drive cognitive decline. Yet, the role of network connectivity in facilitating this accumulation remains unclear. Using in-vivo multimodal imaging, we show that the distribution of tau and reactive microglia in humans follows spatial patterns of connectivity variation, the so-called gradients of brain organization. Notably, less distinct connectivity patterns ("gradient contraction") are associated with cognitive decline in regions with greater tau, suggesting an interaction between reduced network differentiation and tau on cognition. Furthermore, by modeling tau in subject-specific gradient space, we demonstrate that tau accumulation in the frontoparietal and temporo-occipital cortices is associated with greater baseline tau within their functionally and structurally connected hubs, respectively. Our work unveils a role for both functional and structural brain organization in pathology accumulation in AD, and supports subject-specific gradient space as a promising tool to map disease progression.
Collapse
Affiliation(s)
- Julie Ottoy
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Min Su Kang
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | | | - Lyndon Boone
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Bo-Yong Park
- Department of Data Science, Inha University, Incheon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Gleb Bezgin
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
- Neuroinformatics for Personalized Medicine lab, Montreal Neurological Institute, McGill University, Montréal, QC, Canada
| | - Firoza Z Lussier
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Nesrine Rahmouni
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jenna Stevenson
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jaime Fernandez Arias
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Joseph Therriault
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Bojana Stefanovic
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - JoAnne McLaurin
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
- Biological Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Jean-Paul Soucy
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Serge Gauthier
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Sandra E Black
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
- Department of Medicine (Division of Neurology), University of Toronto, Toronto, ON, Canada
| | - Pedro Rosa-Neto
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Translational Neuroimaging laboratory, McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Maged Goubran
- Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.
- Physical Sciences Platform, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
10
|
Boccalini C, Caminiti SP, Chiti A, Frisoni GB, Garibotto V, Perani D. The diagnostic and prognostic value of tau-PET in amnestic MCI with different FDG-PET subtypes. Ann Clin Transl Neurol 2024; 11:1236-1249. [PMID: 38553802 PMCID: PMC11093253 DOI: 10.1002/acn3.52039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 02/21/2024] [Accepted: 02/26/2024] [Indexed: 05/15/2024] Open
Abstract
OBJECTIVES Mild cognitive impairment presenting with an amnestic syndrome (aMCI) and amyloid positivity is considered due to AD. Many subjects, however, can show an overall very slow progression relevant for differential diagnosis, prognosis, and treatment. This study assessed PET biomarkers, including brain glucose metabolism, tau, and amyloid load, in a series of comparable aMCI at baseline, clinically evaluated at follow-up. METHODS We included 72 aMCI subjects from Geneva Memory Center (N = 31) and ADNI cohorts (N = 41), selected based on available FDG-PET, tau-PET, amyloid-PET, and clinical follow-up (2.3 years ± 1.2). A data-driven algorithm classified brain metabolic patterns into subtypes that were then compared for clinical and PET biomarker measures and cognitive decline. Voxel-wise comparisons were performed both with FDG-PET and tau-PET data. RESULTS The algorithm classified three metabolic subtypes, namely "Hippocampal-sparing with cortical hypometabolism" (Type1; N = 27), "Hippocampal and cortical hypometabolism" (Type 2; N = 23), and "Medial temporal hypometabolism" (Type 3; N = 22). Amyloid positivity and tau accumulation in the medial temporal and neocortical regions characterized Type 1 and Type 2, whereas Type 3 showed no significant tau pathology, variable amyloid positivity, and stability at follow-up. All tau-positive patients, independently of the FDG-based subtype, showed faster cognitive decline. INTERPRETATION aMCI subjects can differ in metabolic patterns, tau and amyloid pathology, and clinical progression. Here, we complemented with PET tau biomarker the specific brain hypometabolic patterns at the individual level in the prodromal phase, contributing to the patient's classification. Tau PET is the most accurate biomarker in supporting or excluding the AD diagnosis in aMCI across metabolic subtypes and also predicting the risk of decline.
Collapse
Affiliation(s)
- Cecilia Boccalini
- Vita‐Salute San Raffaele UniversityMilanItaly
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of MedicineUniversity of GenevaGenevaSwitzerland
| | - Silvia Paola Caminiti
- Vita‐Salute San Raffaele UniversityMilanItaly
- Department of Brain and Behavioral SciencesUniversity of PaviaPaviaItaly
- Nuclear Medicine DepartmentIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Arturo Chiti
- Vita‐Salute San Raffaele UniversityMilanItaly
- Nuclear Medicine DepartmentIRCCS San Raffaele Scientific InstituteMilanItaly
| | - Giovanni B. Frisoni
- Laboratory of Neuroimaging of Aging (LANVIE)University of GenevaGenevaSwitzerland
- Memory ClinicGeneva University HospitalsGenevaSwitzerland
| | - Valentina Garibotto
- Laboratory of Neuroimaging and Innovative Molecular Tracers (NIMTlab), Geneva University Neurocenter and Faculty of MedicineUniversity of GenevaGenevaSwitzerland
- Division of Nuclear Medicine and Molecular ImagingGeneva University HospitalsGenevaSwitzerland
- CIBM Center for Biomedical ImagingGenevaSwitzerland
| | - Daniela Perani
- Vita‐Salute San Raffaele UniversityMilanItaly
- Nuclear Medicine DepartmentIRCCS San Raffaele Scientific InstituteMilanItaly
| | | |
Collapse
|
11
|
Corriveau-Lecavalier N, Barnard LR, Botha H, Graff-Radford J, Ramanan VK, Lee J, Dicks E, Rademakers R, Boeve BF, Machulda MM, Fields JA, Dickson DW, Graff-Radford N, Knopman DS, Lowe VJ, Petersen RC, Jack CR, Jones DT. Uncovering the distinct macro-scale anatomy of dysexecutive and behavioural degenerative diseases. Brain 2024; 147:1483-1496. [PMID: 37831661 PMCID: PMC10994526 DOI: 10.1093/brain/awad356] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/28/2023] [Accepted: 10/03/2023] [Indexed: 10/15/2023] Open
Abstract
There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer's disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer's disease pathology (behavioural Alzheimer's disease) and 28 had amnestic Alzheimer's disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as 'eigenbrains'. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer's disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer's disease, then amnestic Alzheimer's disease. Dysexecutive Alzheimer's disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer's disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer's disease, temporo-parietal areas in amnestic Alzheimer's disease and frontotemporal areas in bvFTD and behavioural Alzheimer's disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer's disease patient for behavioural Alzheimer's disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.
Collapse
Affiliation(s)
| | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Vijay K Ramanan
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
- Center for Molecular Neurology, Antwerp University, Antwerp, Belgium
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN 55905, USA
| | - Dennis W Dickson
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | | | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
12
|
da Silva Rosa SC, Barzegar Behrooz A, Guedes S, Vitorino R, Ghavami S. Prioritization of genes for translation: a computational approach. Expert Rev Proteomics 2024; 21:125-147. [PMID: 38563427 DOI: 10.1080/14789450.2024.2337004] [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: 05/26/2023] [Accepted: 02/21/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation. AREAS COVERED In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes. EXPERT OPINION Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.
Collapse
Affiliation(s)
- Simone C da Silva Rosa
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
| | - Amir Barzegar Behrooz
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Electrophysiology Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Sofia Guedes
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Rui Vitorino
- LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, Aveiro, Portugal
- Department of Medical Sciences, Institute of Biomedicine-iBiMED, University of Aveiro, Aveiro, Portugal
- UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Saeid Ghavami
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Science, University of Manitoba, Winnipeg, Canada
- Faculty of Medicine in Zabrze, Academia of Silesia, Katowice, Poland
- Research Institute of Oncology and Hematology, Cancer Care Manitoba, University of Manitoba, Winnipeg, Canada
| |
Collapse
|
13
|
Ibanez A, Kringelbach ML, Deco G. A synergetic turn in cognitive neuroscience of brain diseases. Trends Cogn Sci 2024; 28:319-338. [PMID: 38246816 DOI: 10.1016/j.tics.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 12/15/2023] [Accepted: 12/27/2023] [Indexed: 01/23/2024]
Abstract
Despite significant improvements in our understanding of brain diseases, many barriers remain. Cognitive neuroscience faces four major challenges: complex structure-function associations; disease phenotype heterogeneity; the lack of transdiagnostic models; and oversimplified cognitive approaches restricted to the laboratory. Here, we propose a synergetics framework that can help to perform the necessary dimensionality reduction of complex interactions between the brain, body, and environment. The key solutions include low-dimensional spatiotemporal hierarchies for brain-structure associations, whole-brain modeling to handle phenotype diversity, model integration of shared transdiagnostic pathophysiological pathways, and naturalistic frameworks balancing experimental control and ecological validity. Creating whole-brain models with reduced manifolds combined with ecological measures can improve our understanding of brain disease and help identify novel interventions. Synergetics provides an integrated framework for future progress in clinical and cognitive neuroscience, pushing the boundaries of brain health and disease toward more mature, naturalistic approaches.
Collapse
Affiliation(s)
- Agustin Ibanez
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile; Global Brain Health Institute (GBHI), University California San Francisco (UCSF), San Francisco, CA, USA; Global Brain Health Institute (GBHI), Trinity College Dublin, Dublin, Ireland; Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina; Department of Psychiatry, University of Oxford, Oxford, UK.
| | - Morten L Kringelbach
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, UK
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain.
| |
Collapse
|
14
|
Therriault J, Schindler SE, Salvadó G, Pascoal TA, Benedet AL, Ashton NJ, Karikari TK, Apostolova L, Murray ME, Verberk I, Vogel JW, La Joie R, Gauthier S, Teunissen C, Rabinovici GD, Zetterberg H, Bateman RJ, Scheltens P, Blennow K, Sperling R, Hansson O, Jack CR, Rosa-Neto P. Biomarker-based staging of Alzheimer disease: rationale and clinical applications. Nat Rev Neurol 2024; 20:232-244. [PMID: 38429551 DOI: 10.1038/s41582-024-00942-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/05/2024] [Indexed: 03/03/2024]
Abstract
Disease staging, whereby the spatial extent and load of brain pathology are used to estimate the severity of Alzheimer disease (AD), is pivotal to the gold-standard neuropathological diagnosis of AD. Current in vivo diagnostic frameworks for AD are based on abnormal concentrations of amyloid-β and tau in the cerebrospinal fluid or on PET scans, and breakthroughs in molecular imaging have opened up the possibility of in vivo staging of AD. Focusing on the key principles of disease staging shared across several areas of medicine, this Review highlights the potential for in vivo staging of AD to transform our understanding of preclinical AD, refine enrolment criteria for trials of disease-modifying therapies and aid clinical decision-making in the era of anti-amyloid therapeutics. We provide a state-of-the-art review of recent biomarker-based AD staging systems and highlight their contributions to the understanding of the natural history of AD. Furthermore, we outline hypothetical frameworks to stage AD severity using more accessible fluid biomarkers. In addition, by applying amyloid PET-based staging to recently published anti-amyloid therapeutic trials, we highlight how biomarker-based disease staging frameworks could illustrate the numerous pathological changes that have already taken place in individuals with mildly symptomatic AD. Finally, we discuss challenges related to the validation and standardization of disease staging and provide a forward-looking perspective on potential clinical applications.
Collapse
Affiliation(s)
- Joseph Therriault
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada.
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada.
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Tharick A Pascoal
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andréa Lessa Benedet
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation, London, UK
| | - Thomas K Karikari
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Liana Apostolova
- Department of Neurology, University of Indiana School of Medicine, Indianapolis, IN, USA
| | | | - Inge Verberk
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Sciences, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Renaud La Joie
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Serge Gauthier
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| | - Charlotte Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Gil D Rabinovici
- Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Tracy Family SILQ Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Philip Scheltens
- Alzheimer Centre Amsterdam, Amsterdam Neuroscience, Amsterdam, Netherlands
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden
| | - Reisa Sperling
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | | | - Pedro Rosa-Neto
- Translational Neuroimaging Laboratory, McGill Research Centre for Studies in Aging, Alzheimer's Disease Research Unit, Douglas Research Institute, Le Centre intégré universitaire de santé et de services sociaux (CIUSSS) de l'Ouest-de-l'Île-de-Montréal, Montreal, Quebec, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada
| |
Collapse
|
15
|
Lee J, Burkett BJ, Min HK, Senjem ML, Dicks E, Corriveau-Lecavalier N, Mester CT, Wiste HJ, Lundt ES, Murray ME, Nguyen AT, Reichard RR, Botha H, Graff-Radford J, Barnard LR, Gunter JL, Schwarz CG, Kantarci K, Knopman DS, Boeve BF, Lowe VJ, Petersen RC, Jack CR, Jones DT. Synthesizing images of tau pathology from cross-modal neuroimaging using deep learning. Brain 2024; 147:980-995. [PMID: 37804318 PMCID: PMC10907092 DOI: 10.1093/brain/awad346] [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: 02/20/2023] [Revised: 08/30/2023] [Accepted: 09/24/2023] [Indexed: 10/09/2023] Open
Abstract
Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer's disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging's use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.
Collapse
Affiliation(s)
- Jeyeon Lee
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
| | - Brian J Burkett
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hoon-Ki Min
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Matthew L Senjem
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Carly T Mester
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Heather J Wiste
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Emily S Lundt
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL 32224, USA
| | - Aivi T Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ross R Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | | | - Kejal Kantarci
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
16
|
Young AL, Oxtoby NP, Garbarino S, Fox NC, Barkhof F, Schott JM, Alexander DC. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat Rev Neurosci 2024; 25:111-130. [PMID: 38191721 DOI: 10.1038/s41583-023-00779-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.
Collapse
Affiliation(s)
- Alexandra L Young
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Sara Garbarino
- Life Science Computational Laboratory, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| |
Collapse
|
17
|
Corriveau-Lecavalier N, Barnard LR, Przybelski SA, Gogineni V, Botha H, Graff-Radford J, Ramanan VK, Forsberg LK, Fields JA, Machulda MM, Rademakers R, Gavrilova RH, Lapid MI, Boeve BF, Knopman DS, Lowe VJ, Petersen RC, Jack CR, Kantarci K, Jones DT. Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET. Neuroimage Clin 2023; 41:103559. [PMID: 38147792 PMCID: PMC10944211 DOI: 10.1016/j.nicl.2023.103559] [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: 10/16/2023] [Revised: 11/21/2023] [Accepted: 12/19/2023] [Indexed: 12/28/2023]
Abstract
Genetic mutations causative of frontotemporal lobar degeneration (FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter-individual variability in their patterns of network degeneration and clinical manifestations. We collected clinical and 18Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism ("eigenbrains" or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.
Collapse
Affiliation(s)
- Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic Rochester, USA; Department of Psychiatry and Psychology, Mayo Clinic Rochester, USA
| | | | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic Rochester, USA
| | | | | | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic Rochester, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic Rochester, USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic Jacksonville, USA; VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Belgium
| | | | - Maria I Lapid
- Department of Psychiatry and Psychology, Mayo Clinic Rochester, USA
| | | | | | - Val J Lowe
- Department of Radiology, Mayo Clinic Rochester, USA
| | | | | | | | - David T Jones
- Department of Neurology, Mayo Clinic Rochester, USA; Department of Radiology, Mayo Clinic Rochester, USA.
| |
Collapse
|
18
|
Whiteside DJ, Holland N, Tsvetanov KA, Mak E, Malpetti M, Savulich G, Jones PS, Naessens M, Rouse MA, Fryer TD, Hong YT, Aigbirhio FI, Mulroy E, Bhatia KP, Rittman T, O'Brien JT, Rowe JB. Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration. Nat Commun 2023; 14:8458. [PMID: 38114493 PMCID: PMC10730886 DOI: 10.1038/s41467-023-44307-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
There is extensive synaptic loss from frontotemporal lobar degeneration, in preclinical models and human in vivo and post mortem studies. Understanding the consequences of synaptic loss for network function is important to support translational models and guide future therapeutic strategies. To examine this relationship, we recruited 55 participants with syndromes associated with frontotemporal lobar degeneration and 24 healthy controls. We measured synaptic density with positron emission tomography using the radioligand [11C]UCB-J, which binds to the presynaptic vesicle glycoprotein SV2A, neurite dispersion with diffusion magnetic resonance imaging, and network function with task-free magnetic resonance imaging functional connectivity. Synaptic density and neurite dispersion in patients was associated with reduced connectivity beyond atrophy. Functional connectivity moderated the relationship between synaptic density and clinical severity. Our findings confirm the importance of synaptic loss in frontotemporal lobar degeneration syndromes, and the resulting effect on behaviour as a function of abnormal connectivity.
Collapse
Affiliation(s)
- David J Whiteside
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - Negin Holland
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Kamen A Tsvetanov
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Elijah Mak
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Maura Malpetti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - George Savulich
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - P Simon Jones
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Michelle Naessens
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Matthew A Rouse
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Tim D Fryer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Young T Hong
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Franklin I Aigbirhio
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Eoin Mulroy
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Kailash P Bhatia
- UCL Queen Square Institute of Neurology, University College London, London, WC1N 3BG, UK
| | - Timothy Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - John T O'Brien
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| |
Collapse
|
19
|
Brown JA, Lee AJ, Fernhoff K, Pistone T, Pasquini L, Wise AB, Staffaroni AM, Luisa Mandelli M, Lee SE, Boxer AL, Rankin KP, Rabinovici GD, Luisa Gorno Tempini M, Rosen HJ, Kramer JH, Miller BL, Seeley WW. Functional network collapse in neurodegenerative disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569654. [PMID: 38106054 PMCID: PMC10723363 DOI: 10.1101/2023.12.01.569654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Cognitive and behavioral deficits in Alzheimer's disease (AD) and frontotemporal dementia (FTD) result from brain atrophy and altered functional connectivity. However, it is unclear how atrophy relates to functional connectivity disruptions across dementia subtypes and stages. We addressed this question using structural and functional MRI from 221 patients with AD (n=82), behavioral variant FTD (n=41), corticobasal syndrome (n=27), nonfluent (n=34) and semantic (n=37) variant primary progressive aphasia, and 100 cognitively normal individuals. Using partial least squares regression, we identified three principal structure-function components. The first component showed overall atrophy correlating with primary cortical hypo-connectivity and subcortical/association cortical hyper-connectivity. Components two and three linked focal syndrome-specific atrophy to peri-lesional hypo-connectivity and distal hyper-connectivity. Structural and functional component scores predicted global and domain-specific cognitive deficits. Anatomically, functional connectivity changes reflected alterations in specific brain activity gradients. Eigenmode analysis identified temporal phase and amplitude collapse as an explanation for atrophy-driven functional connectivity changes.
Collapse
Affiliation(s)
- Jesse A. Brown
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Alex J. Lee
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Kristen Fernhoff
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Taylor Pistone
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Lorenzo Pasquini
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Amy B. Wise
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Adam M. Staffaroni
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Maria Luisa Mandelli
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Suzee E. Lee
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Adam L. Boxer
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Katherine P. Rankin
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Gil D. Rabinovici
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Maria Luisa Gorno Tempini
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Howard J. Rosen
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Joel H. Kramer
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - Bruce L. Miller
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | - William W. Seeley
- University of California, San Francisco, Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, San Francisco, CA, USA
| | | |
Collapse
|
20
|
Rubido N, Riedel G, Vuksanović V. Genetic basis of anatomical asymmetry and aberrant dynamic functional networks in Alzheimer's disease. Brain Commun 2023; 6:fcad320. [PMID: 38173803 PMCID: PMC10763534 DOI: 10.1093/braincomms/fcad320] [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: 05/09/2023] [Revised: 10/14/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Genetic associations with macroscopic brain networks can provide insights into healthy and aberrant cortical connectivity in disease. However, associations specific to dynamic functional connectivity in Alzheimer's disease are still largely unexplored. Understanding the association between gene expression in the brain and functional networks may provide useful information about the molecular processes underlying variations in impaired brain function. Given the potential of dynamic functional connectivity to uncover brain states associated with Alzheimer's disease, it is interesting to ask: How does gene expression associated with Alzheimer's disease map onto the dynamic functional brain connectivity? If genetic variants associated with neurodegenerative processes involved in Alzheimer's disease are to be correlated with brain function, it is essential to generate such a map. Here, we investigate how the relation between gene expression in the brain and dynamic functional connectivity arises from nodal interactions, quantified by their role in network centrality (i.e. the drivers of the metastability), and the principal component of genetic co-expression across the brain. Our analyses include genetic variations associated with Alzheimer's disease and also genetic variants expressed within the cholinergic brain pathways. Our findings show that contrasts in metastability of functional networks between Alzheimer's and healthy individuals can in part be explained by the two combinations of genetic co-variations in the brain with the confidence interval between 72% and 92%. The highly central nodes, driving the brain aberrant metastable dynamics in Alzheimer's disease, highly correlate with the magnitude of variations from two combinations of genes expressed in the brain. These nodes include mainly the white matter, parietal and occipital brain regions, each of which (or their combinations) are involved in impaired cognitive function in Alzheimer's disease. In addition, our results provide evidence of the role of genetic associations across brain regions in asymmetric changes in ageing. We validated our findings on the same cohort using alternative brain parcellation methods. This work demonstrates how genetic variations underpin aberrant dynamic functional connectivity in Alzheimer's disease.
Collapse
Affiliation(s)
- Nicolás Rubido
- Institute of Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen AB24 3UE, UK
| | - Gernot Riedel
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
| | - Vesna Vuksanović
- Health Data Science, Swansea University Medical School, Swansea University, Swansea SA2 8PP, UK
| |
Collapse
|
21
|
Corriveau-Lecavalier N, Botha H, Graff-Radford J, Switzer AR, Przybelski SA, Wiste HJ, Murray ME, Reichard RR, Dickson DW, Nguyen AT, Ramanan VK, McCarter SJ, Boeve BF, Machulda MM, Fields JA, Stricker NH, Nelson PT, Grothe MJ, Knopman DS, Lowe VJ, Petersen RC, Jack CR, Jones DT. A limbic-predominant amnestic neurodegenerative syndrome associated with TDP-43 pathology. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.19.23298314. [PMID: 38045300 PMCID: PMC10690340 DOI: 10.1101/2023.11.19.23298314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Limbic-predominant age-related TDP-43 encephalopathy (LATE) is a neuropathologically-defined disease that affects 40% of persons in advanced age, but its associated neurological syndrome is not defined. LATE neuropathological changes (LATE-NC) are frequently comorbid with Alzheimer's disease neuropathologic changes (ADNC). When seen in isolation, LATE-NC have been associated with a predominantly amnestic profile and slow clinical progression. We propose a set of clinical criteria for a limbic-predominant amnestic neurodegenerative syndrome (LANS) that is highly associated with LATE-NC but also other pathologic entities. The LANS criteria incorporate core, standard and advanced features that are measurable in vivo, including older age at evaluation, mild clinical syndrome, disproportionate hippocampal atrophy, impaired semantic memory, limbic hypometabolism, absence of neocortical degenerative patterns and low likelihood of neocortical tau, with degrees of certainty (highest, high, moderate, low). We operationalized this set of criteria using clinical, imaging and biomarker data to validate its associations with clinical and pathologic outcomes. We screened autopsied patients from Mayo Clinic (n = 922) and ADNI (n = 93) cohorts and applied the LANS criteria to those with an antemortem predominant amnestic syndrome (Mayo, n = 165; ADNI, n = 53). ADNC, ADNC/LATE-NC and LATE-NC accounted for 35%, 37% and 4% of cases in the Mayo cohort, respectively, and 30%, 22%, and 9% of cases in the ADNI cohort, respectively. The LANS criteria effectively categorized these cases, with ADNC having the lowest LANS likelihoods, LATE-NC patients having the highest likelihoods, and ADNC/LATE-NC patients having intermediate likelihoods. A logistic regression model using the LANS features as predictors of LATE-NC achieved a balanced accuracy of 74.6% in the Mayo cohort, and out-of-sample predictions in the ADNI cohort achieved a balanced accuracy of 73.3%. Patients with high LANS likelihoods had a milder and slower clinical course and more severe temporo-limbic degeneration compared to those with low likelihoods. Stratifying ADNC/LATE-NC patients from the Mayo cohort according to their LANS likelihood revealed that those with higher likelihoods had more temporo-limbic degeneration and a slower rate of cognitive decline, and those with lower likelihoods had more lateral temporo-parietal degeneration and a faster rate of cognitive decline. The implementation of LANS criteria has implications to disambiguate the different driving etiologies of progressive amnestic presentations in older age and guide prognosis, treatment, and clinical trials. The development of in vivo biomarkers specific to TDP-43 pathology are needed to refine molecular associations between LANS and LATE-NC and precise antemortem diagnoses of LATE.
Collapse
Affiliation(s)
- Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | | | | | | | - Heather J. Wiste
- Department of Quantitative Health Sciences, Mayo Clinic Rochester, MN, USA
| | | | - R. Ross Reichard
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, MN, USA
| | | | - Aivi T. Nguyen
- Department of Laboratory Medicine and Pathology, Mayo Clinic Rochester, MN, USA
| | | | | | | | - Mary M. Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Julie A. Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nikki H. Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Peter T. Nelson
- Department of Pathology, University of Kentucky, Lexington, KY, USA
| | - Michel J. Grothe
- CIEN Foundation/Queen Sofia Foundation Alzheimer Center, Madrid, Spain
- Wallenberg Center for Molecular and Translational Medicine and Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
| | | | - Val J. Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Clifford R. Jack
- Department of Neuroscience, Mayo Clinic Jacksonville, FL, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - David T. Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|
22
|
Lee JJ, Earnest T, Ha SM, Bani A, Kothapalli D, Liu P, Sotiras A. Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.10.23298396. [PMID: 38116031 PMCID: PMC10729728 DOI: 10.1101/2023.11.10.23298396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease. Highlights ▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.
Collapse
|
23
|
Zhao K, Chen P, Alexander-Bloch A, Wei Y, Dyrba M, Yang F, Kang X, Wang D, Fan D, Ye S, Tang Y, Yao H, Zhou B, Lu J, Yu C, Wang P, Liao Z, Chen Y, Huang L, Zhang X, Han Y, Li S, Liu Y. A neuroimaging biomarker for Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN): a cross-sectional study. EClinicalMedicine 2023; 65:102276. [PMID: 37954904 PMCID: PMC10632687 DOI: 10.1016/j.eclinm.2023.102276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 09/27/2023] [Accepted: 09/29/2023] [Indexed: 11/14/2023] Open
Abstract
Background Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems. Methods Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD. Findings IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p < 1 × 10-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aβ (HR = 3.78 [95% CI: 2.63∼5.43], p = 2.13 × 10-14) and CSF Tau (HR = 3.77 [95% CI: 2.64∼5.39], p = 9.53 × 10-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta = -0.70, p < 1 × 10-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aβ (beta = -0.26, p = 4.40 × 10-9) and CSF Tau (beta = 0.12, p = 1.02 × 10-5). Interpretation Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials. Funding Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.
Collapse
Affiliation(s)
- Kun Zhao
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Pindong Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China
| | - Aaron Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, USA
| | - Yongbin Wei
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Martin Dyrba
- German Centre for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Fan Yang
- CAS Key Laboratory of Molecular Imaging, Institute of Automation, Beijing, China
| | - Xiaopeng Kang
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Ji'nan, China
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, Beijing, China
- Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China
- Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Diseases, Beijing, China
| | - Shan Ye
- Department of Neurology, Peking University Third Hospital, Beijing, China
- Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China
- Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Diseases, Beijing, China
| | - Yi Tang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Hongxiang Yao
- Department of Radiology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Bo Zhou
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Zhengluan Liao
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Yan Chen
- Department of Psychiatry, People's Hospital of Hangzhou Medical College, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Longjian Huang
- Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Xi Zhang
- Department of Neurology, The Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- National Clinical Research Centre for Geriatric Disorders, Beijing, China
- Centre of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences & Brainnetome Centre, Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
24
|
He Y, Li Q, Fu Z, Zeng D, Han Y, Li S. Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease. Cereb Cortex 2023; 33:10836-10847. [PMID: 37718155 DOI: 10.1093/cercor/bhad328] [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: 03/15/2023] [Revised: 07/26/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.
Collapse
Affiliation(s)
- Yirong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhenrong Fu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- Biomedical Engineering Institute, Hainan University, Haikou 570228, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100050, China
- National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| |
Collapse
|
25
|
Vogel JW, Corriveau-Lecavalier N, Franzmeier N, Pereira JB, Brown JA, Maass A, Botha H, Seeley WW, Bassett DS, Jones DT, Ewers M. Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight. Nat Rev Neurosci 2023; 24:620-639. [PMID: 37620599 DOI: 10.1038/s41583-023-00731-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/26/2023] [Indexed: 08/26/2023]
Abstract
Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.
Collapse
Affiliation(s)
- Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden.
| | - Nick Corriveau-Lecavalier
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Acadamy, University of Gothenburg, Mölndal and Gothenburg, Sweden
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
- Neuro Division, Department of Clinical Neurosciences, Karolinska Institute, Stockholm, Sweden
| | - Jesse A Brown
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
| | - Anne Maass
- German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - William W Seeley
- Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA
- Department of Pathology, University of California, San Francisco, CA, USA
| | - Dani S Bassett
- Departments of Bioengineering, Electrical and Systems Engineering, Physics and Astronomy, Neurology and Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany.
| |
Collapse
|
26
|
Ali F, Botha H. DATScan in Mild Cognitive Impairment: Some Answers but More Questions. Neurology 2023; 101:505-506. [PMID: 37524534 DOI: 10.1212/wnl.0000000000207707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2023] [Accepted: 06/02/2023] [Indexed: 08/02/2023] Open
Affiliation(s)
- Farwa Ali
- From the Department of Neurology, Mayo Clinic, Rochester, MN
| | - Hugo Botha
- From the Department of Neurology, Mayo Clinic, Rochester, MN.
| |
Collapse
|
27
|
Avelar-Pereira B, Belloy ME, O'Hara R, Hosseini SMH. Decoding the heterogeneity of Alzheimer's disease diagnosis and progression using multilayer networks. Mol Psychiatry 2023; 28:2423-2432. [PMID: 36539525 PMCID: PMC10279806 DOI: 10.1038/s41380-022-01886-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 09/19/2022] [Accepted: 11/10/2022] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD) is a multifactorial and heterogeneous disorder, which makes early detection a challenge. Studies have attempted to combine biomarkers to improve AD detection and predict progression. However, most of the existing work reports results in parallel or compares normalized findings but does not analyze data simultaneously. We tested a multi-dimensional network framework, applied to 490 subjects (cognitively normal [CN] = 147; mild cognitive impairment [MCI] = 287; AD = 56) from ADNI, to create a single model capable of capturing the heterogeneity and progression of AD. First, we constructed subject similarity networks for structural magnetic resonance imaging, amyloid-β positron emission tomography, cerebrospinal fluid, cognition, and genetics data and then applied multilayer community detection to find groups with shared similarities across modalities. Individuals were also followed-up longitudinally, with AD subjects having, on average, 4.5 years of follow-up. Our findings show that multilayer community detection allows for accurate identification of present and future AD (≈90%) and is also able to identify cases that were misdiagnosed clinically. From all MCI participants who developed AD or reverted to CN, the multilayer model correctly identified 90.8% and 88.5% of cases respectively. We observed similar subtypes across the full sample and when examining multimodal data from subjects with no AD pathology (i.e., amyloid negative). Finally, these results were also validated using an independent testing set. In summary, the multilayer framework is successful in detecting AD and provides unique insight into the heterogeneity of the disease by identifying subtypes that share similar multidisciplinary profiles of neurological, cognitive, pathological, and genetics information.
Collapse
Affiliation(s)
- Bárbara Avelar-Pereira
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA.
| | - Michael E Belloy
- Department of Neurology and Neurological Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA
| | - Ruth O'Hara
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA
| | - S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, 94304, USA.
| |
Collapse
|
28
|
Pan F, Huang Y, Cai X, Wang Y, Guan Y, Deng J, Yang D, Zhu J, Zhao Y, Xie F, Fang Z, Guo Q. Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain β-amyloid pathology. COMMUNICATIONS MEDICINE 2023; 3:65. [PMID: 37165172 PMCID: PMC10172320 DOI: 10.1038/s43856-023-00295-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 04/27/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND Accurate prediction of cerebral amyloidosis with easily available indicators is urgently needed for diagnosis and treatment of Alzheimer's disease (AD). METHODS We examined plasma Aβ42, Aβ40, T-tau, P-tau181, and NfL, with APOE genotypes, cognitive test scores and key demographics in a large Chinese cohort (N = 609, aged 40 to 84 years) covering full AD spectrum. Data-driven integrated computational models were developed to predict brain β-amyloid (Aβ) pathology. RESULTS Our computational models accurately predict brain Aβ positivity (area under the ROC curves (AUC) = 0.94). The results are validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Particularly, the models have the highest prediction power (AUC = 0.97) in mild cognitive impairment (MCI) participants. Three levels of models are designed with different accuracies and complexities. The model which only consists of plasma biomarkers can predict Aβ positivity in amnestic MCI (aMCI) patients with AUC = 0.89. Generally the models perform better in participants without comorbidities or family histories. CONCLUSIONS The innovative integrated models provide opportunity to assess Aβ pathology in a non-invasive and cost-effective way, which might facilitate AD-drug development, early screening, clinical diagnosis and prognosis evaluation.
Collapse
Affiliation(s)
- Fengfeng Pan
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yanlu Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xiao Cai
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiale Deng
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Dake Yang
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Jinhang Zhu
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Yike Zhao
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Fang Xie
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.
| | - Zhuo Fang
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China.
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
| |
Collapse
|
29
|
Corriveau-Lecavalier N, Gunter JL, Kamykowski M, Dicks E, Botha H, Kremers WK, Graff-Radford J, Wiepert DA, Schwarz CG, Yacoub E, Knopman DS, Boeve BF, Ugurbil K, Petersen RC, Jack CR, Terpstra MJ, Jones DT. Default mode network failure and neurodegeneration across aging and amnestic and dysexecutive Alzheimer's disease. Brain Commun 2023; 5:fcad058. [PMID: 37013176 PMCID: PMC10066575 DOI: 10.1093/braincomms/fcad058] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 12/15/2022] [Accepted: 03/07/2023] [Indexed: 03/09/2023] Open
Abstract
From a complex systems perspective, clinical syndromes emerging from neurodegenerative diseases are thought to result from multiscale interactions between aggregates of misfolded proteins and the disequilibrium of large-scale networks coordinating functional operations underpinning cognitive phenomena. Across all syndromic presentations of Alzheimer's disease, age-related disruption of the default mode network is accelerated by amyloid deposition. Conversely, syndromic variability may reflect selective neurodegeneration of modular networks supporting specific cognitive abilities. In this study, we leveraged the breadth of the Human Connectome Project-Aging cohort of non-demented individuals (N = 724) as a normative cohort to assess the robustness of a biomarker of default mode network dysfunction in Alzheimer's disease, the network failure quotient, across the aging spectrum. We then examined the capacity of the network failure quotient and focal markers of neurodegeneration to discriminate patients with amnestic (N = 8) or dysexecutive (N = 10) Alzheimer's disease from the normative cohort at the patient level, as well as between Alzheimer's disease phenotypes. Importantly, all participants and patients were scanned using the Human Connectome Project-Aging protocol, allowing for the acquisition of high-resolution structural imaging and longer resting-state connectivity acquisition time. Using a regression framework, we found that the network failure quotient related to age, global and focal cortical thickness, hippocampal volume, and cognition in the normative Human Connectome Project-Aging cohort, replicating previous results from the Mayo Clinic Study of Aging that used a different scanning protocol. Then, we used quantile curves and group-wise comparisons to show that the network failure quotient commonly distinguished both dysexecutive and amnestic Alzheimer's disease patients from the normative cohort. In contrast, focal neurodegeneration markers were more phenotype-specific, where the neurodegeneration of parieto-frontal areas associated with dysexecutive Alzheimer's disease, while the neurodegeneration of hippocampal and temporal areas associated with amnestic Alzheimer's disease. Capitalizing on a large normative cohort and optimized imaging acquisition protocols, we highlight a biomarker of default mode network failure reflecting shared system-level pathophysiological mechanisms across aging and dysexecutive and amnestic Alzheimer's disease and biomarkers of focal neurodegeneration reflecting distinct pathognomonic processes across the amnestic and dysexecutive Alzheimer's disease phenotypes. These findings provide evidence that variability in inter-individual cognitive impairment in Alzheimer's disease may relate to both modular network degeneration and default mode network disruption. These results provide important information to advance complex systems approaches to cognitive aging and degeneration, expand the armamentarium of biomarkers available to aid diagnosis, monitor progression and inform clinical trials.
Collapse
Affiliation(s)
| | | | - Michael Kamykowski
- Department of Information Technology, Mayo Clinic, Rochester, MN 55905, USA
| | - Ellen Dicks
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Walter K Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA
| | | | | | | | - Essa Yacoub
- Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | - David S Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Kamil Ugurbil
- Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| | - Melissa J Terpstra
- Department of Radiology, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Radiology, University of Missouri, Columbia, MO 65211, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
| |
Collapse
|
30
|
Ramanan S, El-Omar H, Roquet D, Ahmed RM, Hodges JR, Piguet O, Lambon Ralph MA, Irish M. Mapping behavioural, cognitive and affective transdiagnostic dimensions in frontotemporal dementia. Brain Commun 2023; 5:fcac344. [PMID: 36687395 PMCID: PMC9847565 DOI: 10.1093/braincomms/fcac344] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 09/26/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023] Open
Abstract
Two common clinical variants of frontotemporal dementia are the behavioural variant frontotemporal dementia, presenting with behavioural and personality changes attributable to prefrontal atrophy, and semantic dementia, displaying early semantic dysfunction primarily due to anterior temporal degeneration. Despite representing independent diagnostic entities, mounting evidence indicates overlapping cognitive-behavioural profiles in these syndromes, particularly with disease progression. Why such overlap occurs remains unclear. Understanding the nature of this overlap, however, is essential to improve early diagnosis, characterization and management of those affected. Here, we explored common cognitive-behavioural and neural mechanisms contributing to heterogeneous frontotemporal dementia presentations, irrespective of clinical diagnosis. This transdiagnostic approach allowed us to ascertain whether symptoms not currently considered core to these two syndromes are present in a significant proportion of cases and to explore the neural basis of clinical heterogeneity. Sixty-two frontotemporal dementia patients (31 behavioural variant frontotemporal dementia and 31 semantic dementia) underwent comprehensive neuropsychological, behavioural and structural neuroimaging assessments. Orthogonally rotated principal component analysis of neuropsychological and behavioural data uncovered eight statistically independent factors explaining the majority of cognitive-behavioural performance variation in behavioural variant frontotemporal dementia and semantic dementia. These factors included Behavioural changes, Semantic dysfunction, General Cognition, Executive function, Initiation, Disinhibition, Visuospatial function and Affective changes. Marked individual-level overlap between behavioural variant frontotemporal dementia and semantic dementia was evident on the Behavioural changes, General Cognition, Initiation, Disinhibition and Affective changes factors. Compared to behavioural variant frontotemporal dementia, semantic dementia patients displayed disproportionate impairment on the Semantic dysfunction factor, whereas greater impairment on Executive and Visuospatial function factors was noted in behavioural variant frontotemporal dementia. Both patient groups showed comparable magnitude of atrophy to frontal regions, whereas severe temporal lobe atrophy was characteristic of semantic dementia. Whole-brain voxel-based morphometry correlations with emergent factors revealed associations between fronto-insular and striatal grey matter changes with Behavioural, Executive and Initiation factor performance, bilateral temporal atrophy with Semantic dysfunction factor scores, parietal-subcortical regions with General Cognitive performance and ventral temporal atrophy associated with Visuospatial factor scores. Together, these findings indicate that cognitive-behavioural overlap (i) occurs systematically in frontotemporal dementia; (ii) varies in a graded manner between individuals and (iii) is associated with degeneration of different neural systems. Our findings suggest that phenotypic heterogeneity in frontotemporal dementia syndromes can be captured along continuous, multidimensional spectra of cognitive-behavioural changes. This has implications for the diagnosis of both syndromes amidst overlapping features as well as the design of symptomatic treatments applicable to multiple syndromes.
Collapse
Affiliation(s)
- Siddharth Ramanan
- Medical Research Council Cognition and Brain Sciences Unit, The University of Cambridge, Cambridge CB3 1AU, UK
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- School of Psychology, The University of Sydney, Sydney, NSW 2050, Australia
| | - Hashim El-Omar
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
| | - Daniel Roquet
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- School of Psychology, The University of Sydney, Sydney, NSW 2050, Australia
| | - Rebekah M Ahmed
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- Memory and Cognition Clinic, Department of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, NSW 2050, Australia
| | - John R Hodges
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- School of Psychology, The University of Sydney, Sydney, NSW 2050, Australia
- School of Medical Sciences, The University of Sydney, Sydney, NSW 2050, Australia
| | - Olivier Piguet
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- School of Psychology, The University of Sydney, Sydney, NSW 2050, Australia
| | - Matthew A Lambon Ralph
- Medical Research Council Cognition and Brain Sciences Unit, The University of Cambridge, Cambridge CB3 1AU, UK
| | - Muireann Irish
- Brain and Mind Centre, The University of Sydney, Sydney, NSW 2050, Australia
- School of Psychology, The University of Sydney, Sydney, NSW 2050, Australia
| |
Collapse
|
31
|
Stecker M. A Perspective: Challenges in Dementia Research. MEDICINA (KAUNAS, LITHUANIA) 2022; 58:1368. [PMID: 36295529 PMCID: PMC9609997 DOI: 10.3390/medicina58101368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/21/2022] [Accepted: 09/27/2022] [Indexed: 11/17/2022]
Abstract
Although dementia is a common and devastating disease that has been studied intensely for more than 100 years, no effective disease modifying treatment has been found. At this impasse, new approaches are important. The purpose of this paper is to provide, in the context of current research, one clinician's perspective regarding important challenges in the field in the form of specific challenges. These challenges not only illustrate the scope of the problems inherent in finding treatments for dementia, but can also be specific targets to foster discussion, criticism and new research. One common theme is the need to transform research activities from small projects in individual laboratories/clinics to larger multinational projects, in which each clinician and researcher works as an integral part. This transformation will require collaboration between researchers, large corporations, regulatory/governmental authorities and the general population, as well as significant financial investments. However, the costs of transforming the approach are small in comparison with the cost of dementia.
Collapse
Affiliation(s)
- Mark Stecker
- Fresno Institute of Neuroscience, Fresno, CA 93720, USA
| |
Collapse
|
32
|
Corriveau-Lecavalier N, Machulda MM, Botha H, Graff-Radford J, Knopman DS, Lowe VJ, Fields JA, Stricker NH, Boeve BF, Jack CR, Petersen RC, Jones DT. Phenotypic subtypes of progressive dysexecutive syndrome due to Alzheimer's disease: a series of clinical cases. J Neurol 2022; 269:4110-4128. [PMID: 35211780 PMCID: PMC9308626 DOI: 10.1007/s00415-022-11025-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 02/07/2022] [Accepted: 02/11/2022] [Indexed: 10/19/2022]
Abstract
Diagnostic criteria for a progressive dysexecutive syndrome due to Alzheimer's disease (dAD) were proposed. Clinical observations suggest substantial variability in the clinico-radiological profiles within this syndrome. We report a case series of 6 patients with dAD highlighting this heterogeneity. Average age at diagnosis was 57.3 years, and patients were followed annually with clinical, cognitive, and multimodal imaging assessments for an average of 3.7 years. Cases were divided based into three subtypes based on their pattern of FDG-PET hypometabolism: predominantly left parieto-frontal (ldAD), predominantly right parieto-frontal (rdAD), or predominantly biparietal (bpdAD) (n = 2 for each). Prominent executive dysfunction was evidenced in all patients. ldAD cases showed greater impairment on measures of verbal working memory and verbal fluency compared to other subtypes. rdAD cases showed more severe alterations in measures of visual abilities compared to language-related domains and committed more perseverative errors on a measure of cognitive flexibility. bpdAD cases presented with predominant cognitive flexibility and inhibition impairment with relative sparing of working memory and a slower rate of clinical progression. rdAD and bpdAD patients developed neuropsychiatric symptoms, whereas none of the ldAD patients did. For each subtype, patterns of tau deposition relatively corresponded to the spatial pattern of FDG hypometabolism. dAD cases could be differentiated from two clinical cases of atypical AD variants (language and visual) in terms of clinical, cognitive and neuroimaging profiles, suggesting that dAD subtypes represent clinical entities separable from other variants of the disease. The recognition of distinct dAD phenotypes has clinical relevance for diagnosis, prognosis, and symptom management.
Collapse
Affiliation(s)
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, 200 First Street S.W., Rochester, MN, 55905, USA
| | | | - David S Knopman
- Department of Neurology, Mayo Clinic, 200 First Street S.W., Rochester, MN, 55905, USA
| | - Val J Lowe
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Nikki H Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Bradley F Boeve
- Department of Neurology, Mayo Clinic, 200 First Street S.W., Rochester, MN, 55905, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Ronald C Petersen
- Department of Neurology, Mayo Clinic, 200 First Street S.W., Rochester, MN, 55905, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, 200 First Street S.W., Rochester, MN, 55905, USA.
- Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
| |
Collapse
|
33
|
Camerucci E, Graff-Radford J, Jones DT, Elder BD, Gunter JL, Cutsforth-Gregory JK, Botha H, Murphy MC, Johnson DR, Davidge-Pitts C, Jack CR, Huston J, Cogswell PM. Change in Morphological Features of Enlarged Subarachnoid Spaces Following Treatment in Idiopathic Normal Pressure Hydrocephalus. J Magn Reson Imaging 2022; 57:1443-1450. [PMID: 35894392 DOI: 10.1002/jmri.28340] [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: 03/21/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Focally enlarged sulci (FES) are areas of proposed extraventricular fluid entrapment that may occur within idiopathic normal pressure hydrocephalus (iNPH) with radiographic evidence of disproportionately enlarged subarachnoid-space hydrocephalus (DESH), and should be differentiated from atrophy. PURPOSE To evaluate for change in FES size and pituitary height after shunt placement in iNPH. STUDY TYPE Retrospective. SUBJECTS A total of 125 iNPH patients who underwent shunt surgery and 40 age-matched controls. FIELD STRENGTH/SEQUENCE 1.5 T and 3 T. Axial T2w FLAIR, 3D T1w MPRAGE, 2D sagittal T1w. ASSESSMENT FES were measured in three dimensions and volume was estimated by assuming an ellipsoid shape. Pituitary gland height was measured in the mid third of the gland in iNPH patients and controls. STATISTICAL TESTS Wilcoxon signed-rank test for comparisons between MRI measurements; Wilcoxon rank sum test for comparison of cases/controls. Significance level was P < 0.05. RESULTS Fifty percent of the patients had FES. FES volume significantly decreased between the pre and first postshunt MRI by a median of 303 mm3 or 30.0%. Pituitary gland size significantly increased by 0.48 mm or 14.4%. FES decreased significantly by 190 mm3 or 23.1% and pituitary gland size increased significantly by 0.25 mm or 6% between the first and last postshunt MRI. DATA CONCLUSION Decrease in size of FES after shunt placement provides further evidence that these regions are due to disordered cerebrospinal fluid (CSF) dynamics and should not be misinterpreted as atrophy. A relatively smaller pituitary gland in iNPH patients that normalizes after shunt is a less-well recognized feature of altered CSF dynamics. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
| | | | - David T Jones
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.,Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin D Elder
- Department of Neurologic Surgery, Mayo Clinic, Rochester, Minnesota, USA.,Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Derek R Johnson
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA.,Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Caroline Davidge-Pitts
- Department of Medicine, Division of Endocrinology, Diabetes, Nutrition, Mayo Clinic, Rochester, Minnesota, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | - John Huston
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, USA
| | | |
Collapse
|
34
|
Perez-Valero E, Morillas C, Lopez-Gordo MA, Carrera-Muñoz I, López-Alcalde S, Vílchez-Carrillo RM. An Automated Approach for the Detection of Alzheimer's Disease From Resting State Electroencephalography. Front Neuroinform 2022; 16:924547. [PMID: 35898959 PMCID: PMC9309796 DOI: 10.3389/fninf.2022.924547] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/20/2022] [Indexed: 12/05/2022] Open
Abstract
Early detection is crucial to control the progression of Alzheimer's disease and to postpone intellectual decline. Most current detection techniques are costly, inaccessible, or invasive. Furthermore, they require laborious analysis, what delays the start of medical treatment. To overcome this, researchers have recently investigated AD detection based on electroencephalography, a non-invasive neurophysiology technique, and machine learning algorithms. However, these approaches typically rely on manual procedures such as visual inspection, that requires additional personnel for the analysis, or on cumbersome EEG acquisition systems. In this paper, we performed a preliminary evaluation of a fully-automated approach for AD detection based on a commercial EEG acquisition system and an automated classification pipeline. For this purpose, we recorded the resting state brain activity of 26 participants from three groups: mild AD, mild cognitive impairment (MCI-non-AD), and healthy controls. First, we applied automated data-driven algorithms to reject EEG artifacts. Then, we obtained spectral, complexity, and entropy features from the preprocessed EEG segments. Finally, we assessed two binary classification problems: mild AD vs. controls, and MCI-non-AD vs. controls, through leave-one-subject-out cross-validation. The preliminary results that we obtained are comparable to the best reported in literature, what suggests that AD detection could be automatically detected through automated processing and commercial EEG systems. This is promising, since it may potentially contribute to reducing costs related to AD screening, and to shortening detection times, what may help to advance medical treatment.
Collapse
Affiliation(s)
- Eduardo Perez-Valero
- Department of Computers Architecture and Technology, University of Granada, Granada, Spain
- Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain
| | - Christian Morillas
- Department of Computers Architecture and Technology, University of Granada, Granada, Spain
- Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain
| | - Miguel A. Lopez-Gordo
- Brain Computer Interface Laboratory, Research Center for Information and Communications Technologies, University of Granada, Granada, Spain
- Department of Signal Theory, Telematics, and Communications, University of Granada, Granada, Spain
- *Correspondence: Miguel A. Lopez-Gordo
| | - Ismael Carrera-Muñoz
- Cognitive Neurology Group, Hospital Universitario Virgen de las Nieves, Granada, Spain
| | | | | |
Collapse
|
35
|
Corriveau-Lecavalier N, Alden EC, Stricker NH, Machulda MM, Jones DT. OUP accepted manuscript. Arch Clin Neuropsychol 2022; 37:1199-1207. [PMID: 35435228 PMCID: PMC9396449 DOI: 10.1093/arclin/acac016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Individuals with early-onset dysexecutive Alzheimer's disease (dAD) have high rates of failed performance validity testing (PVT), which can lead to symptom misinterpretation and misdiagnosis. METHOD The aim of this retrospective study is to evaluate rates of failure on a common PVT, the test of memory malingering (TOMM), in a sample of clinical patients with biomarker-confirmed early-onset dAD who completed neuropsychological testing. RESULTS We identified seventeen patients with an average age of symptom onset at 52.25 years old. Nearly fifty percent of patients performed below recommended cut-offs on Trials 1 and 2 of the TOMM. Four of six patients who completed outside neuropsychological testing were misdiagnosed with alternative etiologies to explain their symptomatology, with two of these patients' performances deemed unreliable based on the TOMM. CONCLUSIONS Low scores on the TOMM should be interpreted in light of contextual and optimally biological information and do not necessarily rule out a neurodegenerative etiology.
Collapse
Affiliation(s)
- Nick Corriveau-Lecavalier
- Corresponding author at: 200 First Street S.W., Rochester, MN 55905, USA. Tel/Fax: 507-266-4106; E-mail address:
| | - Eva C Alden
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Nikki H Stricker
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Mary M Machulda
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| |
Collapse
|