1
|
Jiang J, Shi R, Lu J, Wang M, Zhang Q, Zhang S, Wang L, Alberts I, Rominger A, Zuo C, Shi K. Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network. Neuroimage 2024; 291:120593. [PMID: 38554780 DOI: 10.1016/j.neuroimage.2024.120593] [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: 11/27/2023] [Revised: 03/17/2024] [Accepted: 03/25/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVE The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. METHODS The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. RESULTS The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. INTERPRETATION The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.
Collapse
Affiliation(s)
- Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.
| | - Rong Shi
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China.
| | - Qi Zhang
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Shuoyan Zhang
- School of Information and Communication Engineering, Shanghai University, Shanghai, China
| | - Luyao Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, China
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
| |
Collapse
|
2
|
Salvadó G, Horie K, Barthélemy NR, Vogel JW, Pichet Binette A, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TLS, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Disease staging of Alzheimer's disease using a CSF-based biomarker model. NATURE AGING 2024; 4:694-708. [PMID: 38514824 PMCID: PMC11108782 DOI: 10.1038/s43587-024-00599-y] [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: 05/03/2023] [Accepted: 02/20/2024] [Indexed: 03/23/2024]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic workup of dementia in clinical practice and the design of clinical trials. In this study, we used the Subtype and Stage Inference (SuStaIn) algorithm to establish a robust biological staging model for AD using cerebrospinal fluid (CSF) biomarkers. Our analysis involved 426 participants from BioFINDER-2 and was validated in 222 participants from the Knight Alzheimer Disease Research Center cohort. SuStaIn identified a singular biomarker sequence and revealed that five CSF biomarkers effectively constituted a reliable staging model (ordered: Aβ42/40, pT217/T217, pT205/T205, MTBR-tau243 and non-phosphorylated mid-region tau). The CSF stages (0-5) demonstrated a correlation with increased abnormalities in other AD-related biomarkers, such as Aβ-PET and tau-PET, and aligned with longitudinal biomarker changes reflective of AD progression. Higher CSF stages at baseline were associated with an elevated hazard ratio of clinical decline. This study highlights a common molecular pathway underlying AD pathophysiology across all patients, suggesting that a single CSF collection can accurately indicate the presence of AD pathologies and characterize the stage of disease progression. The proposed staging model has implications for enhancing diagnostic and prognostic assessments in both clinical practice and the design of clinical trials.
Collapse
Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
| | - Kanta Horie
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Eisai, Inc., Nutley, NJ, USA
| | - Nicolas R Barthélemy
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacob W Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Tracy Family Stable Isotope Labeling Quantitation (SILQ) Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
| |
Collapse
|
3
|
Totzek JF, Chakravarty MM, Joober R, Malla A, Shah JL, Raucher-Chéné D, Young AL, Hernaus D, Lepage M, Lavigne KM. Longitudinal inference of multiscale markers in psychosis: from hippocampal centrality to functional outcome. Mol Psychiatry 2024:10.1038/s41380-024-02549-x. [PMID: 38605172 DOI: 10.1038/s41380-024-02549-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 03/28/2024] [Accepted: 04/02/2024] [Indexed: 04/13/2024]
Abstract
Multiscale neuroscience conceptualizes mental illness as arising from aberrant interactions across and within multiple biopsychosocial scales. We leverage this framework to propose a multiscale disease progression model of psychosis, in which hippocampal-cortical dysconnectivity precedes impairments in episodic memory and social cognition, which lead to more severe negative symptoms and lower functional outcome. As psychosis represents a heterogeneous collection of biological and behavioral alterations that evolve over time, we further predict this disease progression for a subtype of the patient sample, with other patients showing normal-range performance on all variables. We sampled data from two cross-sectional datasets of first- and multi-episode psychosis, resulting in a sample of 163 patients and 119 non-clinical controls. To address our proposed disease progression model and evaluate potential heterogeneity, we applied a machine-learning algorithm, SuStaIn, to the patient data. SuStaIn uniquely integrates clustering and disease progression modeling and identified three patient subtypes. Subtype 0 showed normal-range performance on all variables. In comparison, Subtype 1 showed lower episodic memory, social cognition, functional outcome, and higher negative symptoms, while Subtype 2 showed lower hippocampal-cortical connectivity and episodic memory. Subtype 1 deteriorated from episodic memory to social cognition, negative symptoms, functional outcome to bilateral hippocampal-cortical dysconnectivity, while Subtype 2 deteriorated from bilateral hippocampal-cortical dysconnectivity to episodic memory and social cognition, functional outcome to negative symptoms. This first application of SuStaIn in a multiscale psychiatric model provides distinct disease trajectories of hippocampal-cortical connectivity, which might underlie the heterogeneous behavioral manifestations of psychosis.
Collapse
Affiliation(s)
- Jana F Totzek
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - M Mallar Chakravarty
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
- Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ridha Joober
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Ashok Malla
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Jai L Shah
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Delphine Raucher-Chéné
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Alexandra L Young
- Department of Computer Science, University College London, London, United Kingdom
| | - Dennis Hernaus
- Department of Psychiatry & Neuropsychology, School for Mental Health and NeuroScience MHeNS, Maastricht University, Maastricht, The Netherlands
| | - Martin Lepage
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Montreal, QC, Canada
| | - Katie M Lavigne
- Department of Psychiatry, McGill University, Montreal, QC, Canada.
- Douglas Research Centre, Montreal, QC, Canada.
| |
Collapse
|
4
|
Eteleeb AM, Novotny BC, Tarraga CS, Sohn C, Dhungel E, Brase L, Nallapu A, Buss J, Farias F, Bergmann K, Bradley J, Norton J, Gentsch J, Wang F, Davis AA, Morris JC, Karch CM, Perrin RJ, Benitez BA, Harari O. Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease. PLoS Biol 2024; 22:e3002607. [PMID: 38687811 PMCID: PMC11086901 DOI: 10.1371/journal.pbio.3002607] [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: 09/06/2023] [Revised: 05/10/2024] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.
Collapse
Affiliation(s)
- Abdallah M. Eteleeb
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
| | - Brenna C. Novotny
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Carolina Soriano Tarraga
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Christopher Sohn
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Eliza Dhungel
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Logan Brase
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Aasritha Nallapu
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Jared Buss
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Fabiana Farias
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Kristy Bergmann
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Joseph Bradley
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Joanne Norton
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Jen Gentsch
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Fengxian Wang
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Albert A. Davis
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - John C. Morris
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - Celeste M. Karch
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - Richard J. Perrin
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
- Department of Pathology and Immunology, Washington University, St. Louis, Missouri, United States of America
| | - Bruno A. Benitez
- Department of Neurology and Neuroscience, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Oscar Harari
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| |
Collapse
|
5
|
Scotton WJ, Shand C, Todd EG, Bocchetta M, Cash DM, VandeVrede L, Heuer HW, Young AL, Oxtoby N, Alexander DC, Rowe JB, Morris HR, Boxer AL, Rohrer JD, Wijeratne PA. Distinct spatiotemporal atrophy patterns in corticobasal syndrome are associated with different underlying pathologies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.14.24304298. [PMID: 38562801 PMCID: PMC10984071 DOI: 10.1101/2024.03.14.24304298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Objective To identify imaging subtypes of the cortico-basal syndrome (CBS) based solely on a data-driven assessment of MRI atrophy patterns, and investigate whether these subtypes provide information on the underlying pathology. Methods We applied Subtype and Stage Inference (SuStaIn), a machine learning algorithm that identifies groups of individuals with distinct biomarker progression patterns, to a large cohort of 135 CBS cases (52 had a pathological or biomarker defined diagnosis) and 252 controls. The model was fit using volumetric features extracted from baseline T1-weighted MRI scans and validated using follow-up MRI. We compared the clinical phenotypes of each subtype and investigated whether there were differences in associated pathology between the subtypes. Results SuStaIn identified two subtypes with distinct sequences of atrophy progression; four-repeat-tauopathy confirmed cases were most commonly assigned to the Subcortical subtype (83% of CBS-PSP and 75% of CBS-CBD), while CBS-AD was most commonly assigned to the Fronto-parieto-occipital subtype (81% of CBS-AD). Subtype assignment was stable at follow-up (98% of cases), and individuals consistently progressed to higher stages (100% stayed at the same stage or progressed), supporting the model's ability to stage progression. Interpretation By jointly modelling disease stage and subtype, we provide data-driven evidence for at least two distinct and longitudinally stable spatiotemporal subtypes of atrophy in CBS that are associated with different underlying pathologies. In the absence of sensitive and specific biomarkers, accurately subtyping and staging individuals with CBS at baseline has important implications for screening on entry into clinical trials, as well as for tracking disease progression.
Collapse
Affiliation(s)
- W J Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - C Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - E G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - M Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, UK
| | - D M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - L VandeVrede
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA, USA
| | - H W Heuer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA, USA
| | - A L Young
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - N Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - D C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - J B Rowe
- Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge UK
| | - H R Morris
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London, UK
- Movement Disorders Centre, University College London Queen Square Institute of Neurology, London, UK
| | - A L Boxer
- Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, London, UK
| | - J D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, University College London, London, UK
| | - P A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Informatics, University of Sussex, Brighton, BN1 9RH, UK
| |
Collapse
|
6
|
Xu E, Zhang J, Li J, Song Q, Yang D, Wu G, Chen M. Pathology steered stratification network for subtype identification in Alzheimer's disease. Med Phys 2024; 51:1190-1202. [PMID: 37522278 PMCID: PMC10828102 DOI: 10.1002/mp.16655] [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: 01/11/2023] [Revised: 06/25/2023] [Accepted: 07/19/2023] [Indexed: 08/01/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a heterogeneous, multifactorial neurodegenerative disorder characterized by three neurobiological factors beta-amyloid, pathologic tau, and neurodegeneration. There are no effective treatments for AD at a late stage, urging for early detection and prevention. However, existing statistical inference approaches in neuroimaging studies of AD subtype identification do not take into account the pathological domain knowledge, which could lead to ill-posed results that are sometimes inconsistent with the essential neurological principles. PURPOSE Integrating systems biology modeling with machine learning, the study aims to assist clinical AD prognosis by providing a subpopulation classification in accordance with essential biological principles, neurological patterns, and cognitive symptoms. METHODS We propose a novel pathology steered stratification network (PSSN) that incorporates established domain knowledge in AD pathology through a reaction-diffusion model, where we consider non-linear interactions between major biomarkers and diffusion along the brain structural network. Trained on longitudinal multimodal neuroimaging data, the biological model predicts long-term evolution trajectories that capture individual characteristic progression pattern, filling in the gaps between sparse imaging data available. A deep predictive neural network is then built to exploit spatiotemporal dynamics, link neurological examinations with clinical profiles, and generate subtype assignment probability on an individual basis. We further identify an evolutionary disease graph to quantify subtype transition probabilities through extensive simulations. RESULTS Our stratification achieves superior performance in both inter-cluster heterogeneity and intra-cluster homogeneity of various clinical scores. Applying our approach to enriched samples of aging populations, we identify six subtypes spanning AD spectrum, where each subtype exhibits a distinctive biomarker pattern that is consistent with its clinical outcome. CONCLUSIONS The proposed PSSN (i) reduces neuroimage data to low-dimensional feature vectors, (ii) combines AT[N]-Net based on real pathological pathways, (iii) predicts long-term biomarker trajectories, and (iv) stratifies subjects into fine-grained subtypes with distinct neurological underpinnings. PSSN provides insights into pre-symptomatic diagnosis and practical guidance on clinical treatments, which may be further generalized to other neurodegenerative diseases.
Collapse
Affiliation(s)
- Enze Xu
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Jingwen Zhang
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Jiadi Li
- Department of Psychology, Wake Forest University, Winston-Salem, NC 27109, U.S
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC 27157, U.S
| | - Defu Yang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, U.S
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, U.S
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, U.S
| | - Minghan Chen
- Department of Computer Science, Wake Forest University, Winston-Salem, NC 27109, U.S
| |
Collapse
|
7
|
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
|
8
|
Ding H, Wang B, Hamel AP, Karjadi C, Ang TFA, Au R, Lin H. Exploring cognitive progression subtypes in the Framingham Heart Study. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12574. [PMID: 38515438 PMCID: PMC10955221 DOI: 10.1002/dad2.12574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes. METHODS Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains. RESULTS Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk. DISCUSSION This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention. Highlights We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes.Different subtypes have significant variations in age of onset.Higher stages of progression are associated with increased mortality risk.
Collapse
Affiliation(s)
- Huitong Ding
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Biqi Wang
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | - Alexander P. Hamel
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | - Cody Karjadi
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Ting F. A. Ang
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Rhoda Au
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
- Departments of Neurology and MedicineBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| |
Collapse
|
9
|
Mastenbroek SE, Vogel JW, Collij LE, Serrano GE, Tremblay C, Young AL, Arce RA, Shill HA, Driver-Dunckley ED, Mehta SH, Belden CM, Atri A, Choudhury P, Barkhof F, Adler CH, Ossenkoppele R, Beach TG, Hansson O. Disease progression modelling reveals heterogeneity in trajectories of Lewy-type α-synuclein pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.569878. [PMID: 38106128 PMCID: PMC10723322 DOI: 10.1101/2023.12.05.569878] [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
Lewy body (LB) disorders, characterized by the aggregation of misfolded α-synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. By applying data-driven disease progression modelling to regional neuropathological LB density scores from 814 brain donors, we describe three inferred trajectories of LB pathology that were characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) showed earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) exhibited the first abnormalities in brainstem regions. Early limbic pathology was associated with Alzheimer's disease-associated characteristics. Meanwhile, brainstem-first pathology was associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in LBDs.
Collapse
Affiliation(s)
- Sophie E. Mastenbroek
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, the Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Jacob W. Vogel
- Department of Clinical Sciences Malmö, Faculty of Medicine, SciLifLab, Lund University, Lund, Sweden
| | - Lyduine E. Collij
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, the Netherlands
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
| | - Geidy E. Serrano
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Cecilia Tremblay
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alexandra L. Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Richard A. Arce
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Holly A. Shill
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, United States of America
| | - Erika D. Driver-Dunckley
- Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Shyamal H. Mehta
- Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Christine M. Belden
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Alireza Atri
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
- Department of Neurology, Center for Mind/Brain Medicine, Brigham & Women’s Hospital & Harvard Medical School, Boston, Massachusetts, United States of America
| | - Parichita Choudhury
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain imaging, Amsterdam, the Netherlands
- Institutes of Neurology & Healthcare Engineering, University College London, London, United Kingdom
| | - Charles H. Adler
- Department of Neurology, Parkinson’s Disease and Movement Disorders Center, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Thomas G. Beach
- Banner Sun Health Research Institute, Sun City, Arizona, United States of America
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| |
Collapse
|
10
|
Aksman LM, Oxtoby NP, Scelsi MA, Wijeratne PA, Young AL, Alves IL, Collij LE, Vogel JW, Barkhof F, Alexander DC, Altmann A. A data-driven study of Alzheimer's disease related amyloid and tau pathology progression. Brain 2023; 146:4935-4948. [PMID: 37433038 PMCID: PMC10690020 DOI: 10.1093/brain/awad232] [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: 09/29/2022] [Revised: 06/12/2023] [Accepted: 06/29/2023] [Indexed: 07/13/2023] Open
Abstract
Amyloid-β is thought to facilitate the spread of tau throughout the neocortex in Alzheimer's disease, though how this occurs is not well understood. This is because of the spatial discordance between amyloid-β, which accumulates in the neocortex, and tau, which accumulates in the medial temporal lobe during ageing. There is evidence that in some cases amyloid-β-independent tau spreads beyond the medial temporal lobe where it may interact with neocortical amyloid-β. This suggests that there may be multiple distinct spatiotemporal subtypes of Alzheimer's-related protein aggregation, with potentially different demographic and genetic risk profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post-mortem neuropathology and in vivo PET-based measures from two large observational studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We consistently identified 'amyloid-first' and 'tau-first' subtypes using cross-sectional information from both studies. In the amyloid-first subtype, extensive neocortical amyloid-β precedes the spread of tau beyond the medial temporal lobe, while in the tau-first subtype, mild tau accumulates in medial temporal and neocortical areas prior to interacting with amyloid-β. As expected, we found a higher prevalence of the amyloid-first subtype among apolipoprotein E (APOE) ε4 allele carriers while the tau-first subtype was more common among APOE ε4 non-carriers. Within tau-first APOE ε4 carriers, we found an increased rate of amyloid-β accumulation (via longitudinal amyloid PET), suggesting that this rare group may belong within the Alzheimer's disease continuum. We also found that tau-first APOE ε4 carriers had several fewer years of education than other groups, suggesting a role for modifiable risk factors in facilitating amyloid-β-independent tau. Tau-first APOE ε4 non-carriers, in contrast, recapitulated many of the features of primary age-related tauopathy. The rate of longitudinal amyloid-β and tau accumulation (both measured via PET) within this group did not differ from normal ageing, supporting the distinction of primary age-related tauopathy from Alzheimer's disease. We also found reduced longitudinal subtype consistency within tau-first APOE ε4 non-carriers, suggesting additional heterogeneity within this group. Our findings support the idea that amyloid-β and tau may begin as independent processes in spatially disconnected regions, with widespread neocortical tau resulting from the local interaction of amyloid-β and tau. The site of this interaction may be subtype-dependent: medial temporal lobe in amyloid-first, neocortex in tau-first. These insights into the dynamics of amyloid-β and tau may inform research and clinical trials that target these pathologies.
Collapse
Affiliation(s)
- Leon M Aksman
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Marzia A Scelsi
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | | | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Jacob W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Informatics and Neuroimaging Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Brain Research Center, Amsterdam 1081 GN, The Netherlands
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam 1007MB, The Netherlands
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
| | | |
Collapse
|
11
|
Hong J, Lu J, Liu F, Wang M, Li X, Clement C, Lopes L, Brendel M, Rominger A, Yen TC, Guan Y, Tian M, Wang J, Zuo C, Shi K. Uncovering distinct progression patterns of tau deposition in progressive supranuclear palsy using [ 18F]Florzolotau PET imaging and subtype/stage inference algorithm. EBioMedicine 2023; 97:104835. [PMID: 37839135 PMCID: PMC10590768 DOI: 10.1016/j.ebiom.2023.104835] [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: 04/13/2023] [Revised: 09/29/2023] [Accepted: 10/02/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND Progressive supranuclear palsy (PSP) is a primary 4-repeat tauopathy with diverse clinical phenotypes. Previous post-mortem studies examined tau deposition sequences in PSP, but in vivo scrutiny is lacking. METHODS We conducted [18F]Florzolotau tau positron emission tomography (PET) scans on 148 patients who were clinically diagnosed with PSP and 20 healthy controls. We employed the Subtype and Stage Inference (SuStaIn) algorithm to identify PSP subtype/stage and related tau patterns, comparing clinical features across subtypes and assessing PSP stage-clinical severity association. We also evaluated functional connectivity differences among subtypes through resting-state functional magnetic resonance imaging. FINDINGS We identified two distinct subtypes of PSP: Subtype1 and Subtype2. Subtype1 typically exhibits a sequential progression of the disease, starting from subcortical and gradually moving to cortical regions. Conversely, Subtype2 is characterized by an early, simultaneous onset in both regions. Interestingly, once the disease is initiated, Subtype1 tends to spread more rapidly within each region compared to Subtype2. Individuals categorized as Subtype2 are generally older and exhibit less severe dysfunctions in areas such as cognition, bulbar, limb motor, and general motor functions compared to those with Subtype1. Moreover, they have a more favorable prognosis in terms of limb motor function. We found significant correlations between several clinical variables and the identified PSP SuStaIn stages. Furthermore, Subtype2 displayed a remarkable reduction in functional connectivity compared to Subtype1. INTERPRETATION We present the evidence of distinct in vivo spatiotemporal tau trajectories in PSP. Our findings can contribute to precision medicine advancements for PSP. FUNDING This work was supported by grants from the National Natural Science Foundation of China (number 82272039, 81971641, 82021002, and 92249302); Swiss National Science Foundation (number 188350); the STI2030-Major Project of China (number 2022ZD0211600); the Clinical Research Plan of Shanghai Hospital Development Center of China (number SHDC2020CR1038B); and the National Key R&D Program of China (number 2022YFC2009902, 2022YFC2009900), the China Scholarship Council (number 202006100181); the Deutsche Forschungsgemeinschaft (DFG) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy, ID 390857198).
Collapse
Affiliation(s)
- Jimin Hong
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland
| | - Jiaying Lu
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Fengtao Liu
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Science, Shanghai University, Shanghai, China; Department of Informatics, Technical University of Munich, Munich, Germany
| | - Xinyi Li
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Christoph Clement
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland
| | - Leonor Lopes
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Graduate School for Cellular and Biomedical Sciences, University of Bern, Switzerland
| | - Matthias Brendel
- Department of Nuclear Medicine, University of Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland
| | | | - Yihui Guan
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Mei Tian
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China; International Human Phenome Institutes (Shanghai), Shanghai, China
| | - Jian Wang
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
| | - Chuantao Zuo
- Department of Nuclear Medicine & PET Center, Huashan Hospital, Fudan University, Shanghai, China; National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, China; Human Phenome Institute, Fudan University, Shanghai, China.
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, University of Bern, Bern, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
| |
Collapse
|
12
|
Jellinger KA. The Spectrum of Cognitive Dysfunction in Amyotrophic Lateral Sclerosis: An Update. Int J Mol Sci 2023; 24:14647. [PMID: 37834094 PMCID: PMC10572320 DOI: 10.3390/ijms241914647] [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: 08/07/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023] Open
Abstract
Cognitive dysfunction is an important non-motor symptom in amyotrophic lateral sclerosis (ALS) that has a negative impact on survival and caregiver burden. It shows a wide spectrum ranging from subjective cognitive decline to frontotemporal dementia (FTD) and covers various cognitive domains, mainly executive/attention, language and verbal memory deficits. The frequency of cognitive impairment across the different ALS phenotypes ranges from 30% to 75%, with up to 45% fulfilling the criteria of FTD. Significant genetic, clinical, and pathological heterogeneity reflects deficits in various cognitive domains. Modern neuroimaging studies revealed frontotemporal degeneration and widespread involvement of limbic and white matter systems, with hypometabolism of the relevant areas. Morphological substrates are frontotemporal and hippocampal atrophy with synaptic loss, associated with TDP-43 and other co-pathologies, including tau deposition. Widespread functional disruptions of motor and extramotor networks, as well as of frontoparietal, frontostriatal and other connectivities, are markers for cognitive deficits in ALS. Cognitive reserve may moderate the effect of brain damage but is not protective against cognitive decline. The natural history of cognitive dysfunction in ALS and its relationship to FTD are not fully understood, although there is an overlap between the ALS variants and ALS-related frontotemporal syndromes, suggesting a differential vulnerability of motor and non-motor networks. An assessment of risks or the early detection of brain connectivity signatures before structural changes may be helpful in investigating the pathophysiological mechanisms of cognitive impairment in ALS, which might even serve as novel targets for effective disease-modifying therapies.
Collapse
Affiliation(s)
- Kurt A Jellinger
- Institute of Clinical Neurobiology, Alberichgasse 5/13, A-1150 Vienna, Austria
| |
Collapse
|
13
|
Salvadó G, Horie K, Barthélemy NR, Vogel JW, Binette AP, Chen CD, Aschenbrenner AJ, Gordon BA, Benzinger TL, Holtzman DM, Morris JC, Palmqvist S, Stomrud E, Janelidze S, Ossenkoppele R, Schindler SE, Bateman RJ, Hansson O. Novel CSF tau biomarkers can be used for disease staging of sporadic Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.07.14.23292650. [PMID: 37503281 PMCID: PMC10370223 DOI: 10.1101/2023.07.14.23292650] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Biological staging of individuals with Alzheimer's disease (AD) may improve diagnostic and prognostic work-up of dementia in clinical practice and the design of clinical trials. Here, we created a staging model using the Subtype and Stage Inference (SuStaIn) algorithm by evaluating cerebrospinal fluid (CSF) amyloid-β (Aβ) and tau biomarkers in 426 participants from BioFINDER-2, that represent the entire spectrum of AD. The model composition and main analyses were replicated in 222 participants from the Knight ADRC cohort. SuStaIn revealed in the two cohorts that the data was best explained by a single biomarker sequence (one subtype), and that five CSF biomarkers (ordered: Aβ42/40, tau phosphorylation occupancies at the residues 217 and 205 [pT217/T217 and pT205/T205], microtubule-binding region of tau containing the residue 243 [MTBR-tau243], and total tau) were sufficient to create an accurate disease staging model. Increasing CSF stages (0-5) were associated with increased abnormality in other AD-related biomarkers, such as Aβ- and tau-PET, and aligned with different phases of longitudinal biomarker changes consistent with current models of AD progression. Higher CSF stages at baseline were associated with higher hazard ratio of clinical decline. Our findings indicate that a common pathophysiologic molecular pathway develops across all AD patients, and that a single CSF collection is sufficient to reliably indicate the presence of both AD pathologies and the degree and stage of disease progression.
Collapse
Affiliation(s)
- Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Kanta Horie
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Eisai Inc., Nutley, NJ, United States
| | - Nicolas R. Barthélemy
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
| | - Jacob W. Vogel
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Clinical Science, Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Charles D. Chen
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Andrew J Aschenbrenner
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian A. Gordon
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L.S. Benzinger
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M. Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J. Bateman
- The Tracy Family SILQ Center, Washington University School of Medicine, St Louis, MO, United States
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States
- Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| |
Collapse
|
14
|
Zhou C, Wang L, Cheng W, Lv J, Guan X, Guo T, Wu J, Zhang W, Gao T, Liu X, Bai X, Wu H, Cao Z, Gu L, Chen J, Wen J, Huang P, Xu X, Zhang B, Feng J, Zhang M. Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease. NPJ Parkinsons Dis 2023; 9:111. [PMID: 37443179 PMCID: PMC10344958 DOI: 10.1038/s41531-023-00556-3] [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: 01/22/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Increasing evidence suggests that Parkinson's disease (PD) exhibits disparate spatial and temporal patterns of progression. Here we used a machine-learning technique-Subtype and Stage Inference (SuStaIn) - to uncover PD subtypes with distinct trajectories of clinical and neurodegeneration events. We enrolled 228 PD patients and 119 healthy controls with comprehensive assessments of olfactory, autonomic, cognitive, sleep, and emotional function. The integrity of substantia nigra (SN), locus coeruleus (LC), amygdala, hippocampus, entorhinal cortex, and basal forebrain were assessed using diffusion and neuromelanin-sensitive MRI. SuStaIn model with above clinical and neuroimaging variables as input was conducted to identify PD subtypes. An independent dataset consisting of 153 PD patients and 67 healthy controls was utilized to validate our findings. We identified two distinct PD subtypes: subtype 1 with rapid eye movement sleep behavior disorder (RBD), autonomic dysfunction, and degeneration of the SN and LC as early manifestations, and cognitive impairment and limbic degeneration as advanced manifestations, while subtype 2 with hyposmia, cognitive impairment, and limbic degeneration as early manifestations, followed later by RBD and degeneration of the LC in advanced disease. Similar subtypes were shown in the validation dataset. Moreover, we found that subtype 1 had weaker levodopa response, more GBA mutations, and poorer prognosis than subtype 2. These findings provide new insights into the underlying disease biology and might be useful for personalized treatment for patients based on their subtype.
Collapse
Affiliation(s)
- Cheng Zhou
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Linbo Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - JinChao Lv
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
| | - Xiaojun Guan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Tao Guo
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jingjing Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Ting Gao
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xiaocao Liu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xueqin Bai
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Haoting Wu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Zhengye Cao
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Luyan Gu
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jingwen Chen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jiaqi Wen
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Xiaojun Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Baorong Zhang
- Department of Neurology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, 200433, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, United Kingdom.
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China.
| |
Collapse
|
15
|
Young AL, Vogel JW, Robinson JL, McMillan CT, Ossenkoppele R, Wolk DA, Irwin DJ, Elman L, Grossman M, Lee VMY, Lee EB, Hansson O. Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies. Brain 2023; 146:2975-2988. [PMID: 37150879 PMCID: PMC10317181 DOI: 10.1093/brain/awad145] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 03/27/2023] [Accepted: 04/16/2023] [Indexed: 05/09/2023] Open
Abstract
TAR DNA-binding protein-43 (TDP-43) accumulation is the primary pathology underlying several neurodegenerative diseases. Charting the progression and heterogeneity of TDP-43 accumulation is necessary to better characterize TDP-43 proteinopathies, but current TDP-43 staging systems are heuristic and assume each syndrome is homogeneous. Here, we use data-driven disease progression modelling to derive a fine-grained empirical staging system for the classification and differentiation of frontotemporal lobar degeneration due to TDP-43 (FTLD-TDP, n = 126), amyotrophic lateral sclerosis (ALS, n = 141) and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) with and without Alzheimer's disease (n = 304). The data-driven staging of ALS and FTLD-TDP complement and extend previously described human-defined staging schema for ALS and behavioural variant frontotemporal dementia. In LATE-NC individuals, progression along data-driven stages was positively associated with age, but negatively associated with age in individuals with FTLD-TDP. Using only regional TDP-43 severity, our data driven model distinguished individuals diagnosed with ALS, FTLD-TDP or LATE-NC with a cross-validated accuracy of 85.9%, with misclassifications associated with mixed pathological diagnosis, age and genetic mutations. Adding age and SuStaIn stage to this model increased accuracy to 92.3%. Our model differentiates LATE-NC from FTLD-TDP, though some overlap was observed between late-stage LATE-NC and early-stage FTLD-TDP. We further tested for the presence of subtypes with distinct regional TDP-43 progression patterns within each diagnostic group, identifying two distinct cortical-predominant and brainstem-predominant subtypes within FTLD-TDP and a further two subcortical-predominant and corticolimbic-predominant subtypes within ALS. The FTLD-TDP subtypes exhibited differing proportions of TDP-43 type, while there was a trend for age differing between ALS subtypes. Interestingly, a negative relationship between age and SuStaIn stage was seen in the brainstem/subcortical-predominant subtype of each proteinopathy. No subtypes were observed for the LATE-NC group, despite aggregating individuals with and without Alzheimer's disease and a larger sample size for this group. Overall, we provide an empirical pathological TDP-43 staging system for ALS, FTLD-TDP and LATE-NC, which yielded accurate classification. We further demonstrate that there is substantial heterogeneity amongst ALS and FTLD-TDP progression patterns that warrants further investigation in larger cross-cohort studies.
Collapse
Affiliation(s)
- Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, WC1V 6LJ, UK
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, SE-222 42 Lund, Sweden
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
| | - John L Robinson
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Corey T McMillan
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, 1081 HZ Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, 1081 HV Amsterdam, The Netherlands
| | - David A Wolk
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - David J Irwin
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lauren Elman
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Virginia M Y Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA 19104, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, SE-222 42 Lund, Sweden
- Memory Clinic, Skåne University Hospital, SE-205 02 Malmö, Sweden
| |
Collapse
|
16
|
Ahmadi K, Pereira JB, Berron D, Vogel J, Ingala S, Strandberg OT, Janelidze S, Barkhof F, Pfeuffer J, Knutsson L, van Westen D, Palmqvist S, Mutsaerts HJ, Hansson O. Gray matter hypoperfusion is a late pathological event in the course of Alzheimer's disease. J Cereb Blood Flow Metab 2023; 43:565-580. [PMID: 36412244 PMCID: PMC10063832 DOI: 10.1177/0271678x221141139] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Several studies have shown decreased cerebral blood flow (CBF) in Alzheimer's disease (AD). However, the role of hypoperfusion in the disease pathogenesis remains unclear. Combining arterial spin labeling MRI, PET, and CSF biomarkers, we investigated the associations between gray matter (GM)-CBF and the key mechanisms in AD including amyloid-β (Aβ) and tau pathology, synaptic and axonal degeneration. Further, we applied a disease progression modeling to characterize the temporal sequence of different AD biomarkers. Lower perfusion was observed in temporo-occipito-parietal cortex in the Aβ-positive cognitively impaired compared to both Aβ-negative and Aβ-positive cognitively unimpaired individuals. In participants along the AD spectrum, GM-CBF was associated with tau, synaptic and axonal dysfunction, but not Aβ in similar cortical regions. Axonal degeneration was further associated with hypoperfusion in cognitively unimpaired individuals. Disease progression modeling revealed that GM-CBF disruption Followed the abnormality of biomarkers of Aβ, tau and brain atrophy. These findings indicate that tau tangles and neurodegeneration are more closely connected with GM-CBF changes than Aβ pathology. Although subjected to the sensitivity of the employed neuroimaging techniques and the modeling approach, these findings suggest that hypoperfusion might not be an early event associated with the build-up of Aβ in preclinical phase of AD.
Collapse
Affiliation(s)
- Khazar Ahmadi
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Joana B Pereira
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden
| | - David Berron
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Jacob Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Olof T Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Queen's Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Josef Pfeuffer
- Application Development, Siemens Healthcare, Erlangen, Germany
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden.,The Russell H. Morgan Department of Radiology and Radiological Science, Division of MR Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Danielle van Westen
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Diagnostic Radiology, Lund University, Lund, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Henk Jmm Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location VUmc, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Queen's Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Lund University, Lund, Sweden.,Memory Clinic, Skåne University Hospital, Malmö, Sweden
| |
Collapse
|
17
|
Scotton WJ, Shand C, Todd E, Bocchetta M, Cash DM, VandeVrede L, Heuer H, Young AL, Oxtoby N, Alexander DC, Rowe JB, Morris HR, Boxer AL, Rohrer JD, Wijeratne PA. Uncovering spatiotemporal patterns of atrophy in progressive supranuclear palsy using unsupervised machine learning. Brain Commun 2023; 5:fcad048. [PMID: 36938523 PMCID: PMC10016410 DOI: 10.1093/braincomms/fcad048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 11/22/2022] [Accepted: 02/27/2023] [Indexed: 03/06/2023] Open
Abstract
To better understand the pathological and phenotypic heterogeneity of progressive supranuclear palsy and the links between the two, we applied a novel unsupervised machine learning algorithm (Subtype and Stage Inference) to the largest MRI data set to date of people with clinically diagnosed progressive supranuclear palsy (including progressive supranuclear palsy-Richardson and variant progressive supranuclear palsy syndromes). Our cohort is comprised of 426 progressive supranuclear palsy cases, of which 367 had at least one follow-up scan, and 290 controls. Of the progressive supranuclear palsy cases, 357 were clinically diagnosed with progressive supranuclear palsy-Richardson, 52 with a progressive supranuclear palsy-cortical variant (progressive supranuclear palsy-frontal, progressive supranuclear palsy-speech/language, or progressive supranuclear palsy-corticobasal), and 17 with a progressive supranuclear palsy-subcortical variant (progressive supranuclear palsy-parkinsonism or progressive supranuclear palsy-progressive gait freezing). Subtype and Stage Inference was applied to volumetric MRI features extracted from baseline structural (T1-weighted) MRI scans and then used to subtype and stage follow-up scans. The subtypes and stages at follow-up were used to validate the longitudinal consistency of subtype and stage assignments. We further compared the clinical phenotypes of each subtype to gain insight into the relationship between progressive supranuclear palsy pathology, atrophy patterns, and clinical presentation. The data supported two subtypes, each with a distinct progression of atrophy: a 'subcortical' subtype, in which early atrophy was most prominent in the brainstem, ventral diencephalon, superior cerebellar peduncles, and the dentate nucleus, and a 'cortical' subtype, in which there was early atrophy in the frontal lobes and the insula alongside brainstem atrophy. There was a strong association between clinical diagnosis and the Subtype and Stage Inference subtype with 82% of progressive supranuclear palsy-subcortical cases and 81% of progressive supranuclear palsy-Richardson cases assigned to the subcortical subtype and 82% of progressive supranuclear palsy-cortical cases assigned to the cortical subtype. The increasing stage was associated with worsening clinical scores, whilst the 'subcortical' subtype was associated with worse clinical severity scores compared to the 'cortical subtype' (progressive supranuclear palsy rating scale and Unified Parkinson's Disease Rating Scale). Validation experiments showed that subtype assignment was longitudinally stable (95% of scans were assigned to the same subtype at follow-up) and individual staging was longitudinally consistent with 90% remaining at the same stage or progressing to a later stage at follow-up. In summary, we applied Subtype and Stage Inference to structural MRI data and empirically identified two distinct subtypes of spatiotemporal atrophy in progressive supranuclear palsy. These image-based subtypes were differentially enriched for progressive supranuclear palsy clinical syndromes and showed different clinical characteristics. Being able to accurately subtype and stage progressive supranuclear palsy patients at baseline has important implications for screening patients on entry to clinical trials, as well as tracking disease progression.
Collapse
Affiliation(s)
- William J Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Emily Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Lawren VandeVrede
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA 94158, USA
| | - Hilary Heuer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA 94158, USA
| | - Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Neil Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| | - James B Rowe
- Cambridge University Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, Medical Research Council Cognition and Brain Sciences Unit, Cambridge CB2 0QQ, UK
| | - Huw R Morris
- Department of Clinical and Movement Neurosciences, University College London Queen Square Institute of Neurology, London WC1N 3BG, UK
- Movement Disorders Centre, University College London Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Adam L Boxer
- Department of Neurology, Memory and Aging Center, University of California, San Francisco, CA 94158, USA
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3AR, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London WC1V 6LJ, UK
| |
Collapse
|
18
|
Young AL, Vogel JW, Robinson JL, McMillan CT, Ossenkoppele R, Wolk DA, Irwin DJ, Elman L, Grossman M, Lee VMY, Lee EB, Hansson O. Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.31.23285242. [PMID: 36778217 PMCID: PMC9915837 DOI: 10.1101/2023.01.31.23285242] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
TAR DNA-binding protein-43 (TDP-43) accumulation is the primary pathology underlying several neurodegenerative diseases. Charting the progression and heterogeneity of TDP-43 accumulation is necessary to better characterise TDP-43 proteinopathies, but current TDP-43 staging systems are heuristic and assume each syndrome is homogeneous. Here, we use data-driven disease progression modelling to derive a fine-grained empirical staging system for the classification and differentiation of frontotemporal lobar degeneration due to TDP-43 (FTLD-TDP, n=126), amyotrophic lateral sclerosis (ALS, n=141) and limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) with and without Alzheimer’s disease (n=304). The data-driven staging of ALS and FTLD-TDP complement and extend previously described human-defined staging schema for ALS and behavioural variant frontotemporal dementia. In LATE-NC individuals, progression along data-driven stages was positively associated with age, but negatively associated with age in individuals with FTLD-TDP. Using only regional TDP-43 severity, our data driven model distinguished individuals diagnosed with ALS, FTLD-TDP or LATE-NC with a cross-validated accuracy of 85.9%, with misclassifications associated with mixed pathological diagnosis, age and genetic mutations. Adding age and SuStaIn stage to this model increased accuracy to 92.3%. Our model differentiates LATE-NC from FTLD-TDP, though some overlap was observed between late-stage LATE-NC and early-stage FTLD-TDP. We further tested for the presence of subtypes with distinct regional TDP-43 progression patterns within each diagnostic group, identifying two distinct cortical-predominant and brainstem-predominant subtypes within FTLD-TDP and a further two subcortical-predominant and corticolimbic-predominant subtypes within ALS. The FTLD-TDP subtypes exhibited differing proportions of TDP-43 type, while there was a trend for age differing between ALS subtypes. Interestingly, a negative relationship between age and SuStaIn stage was seen in the brainstem/subcortical-predominant subtype of each proteinopathy. No subtypes were observed for the LATE-NC group, despite aggregating AD+ and AD-individuals and a larger sample size for this group. Overall, we provide an empirical pathological TDP-43 staging system for ALS, FTLD-TDP and LATE-NC, which yielded accurate classification. We further demonstrate that there is substantial heterogeneity amongst ALS and FTLD-TDP progression patterns that warrants further investigation in larger cross-cohort studies.
Collapse
Affiliation(s)
- Alexandra L Young
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, Lund, Sweden
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - John L Robinson
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Corey T McMillan
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rik Ossenkoppele
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - David A Wolk
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - David J Irwin
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Elman
- Department of Neurology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Murray Grossman
- Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Virginia M-Y Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Edward B Lee
- Penn Center for Neurodegenerative Disease Research, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Oskar Hansson
- Clinical Memory Research Unit, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| |
Collapse
|