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Yoshioka H, Jin R, Hisaka A, Suzuki H. Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases. Pharmacol Ther 2024; 259:108655. [PMID: 38710372 DOI: 10.1016/j.pharmthera.2024.108655] [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: 01/31/2024] [Revised: 04/22/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.
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Affiliation(s)
- Hideki Yoshioka
- Office of Regulatory Science Research, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Ryota Jin
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Akihiro Hisaka
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan.
| | - Hiroshi Suzuki
- Executive Director, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
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2
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Lin L, Wu Y, Liu L, Sun S, Wu S. Understanding the Temporal Dynamics of Accelerated Brain Aging and Resilient Brain Aging: Insights from Discriminative Event-Based Analysis of UK Biobank Data. Bioengineering (Basel) 2024; 11:647. [PMID: 39061729 PMCID: PMC11273398 DOI: 10.3390/bioengineering11070647] [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: 05/17/2024] [Revised: 06/14/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
The intricate dynamics of brain aging, especially the neurodegenerative mechanisms driving accelerated (ABA) and resilient brain aging (RBA), are pivotal in neuroscience. Understanding the temporal dynamics of these phenotypes is crucial for identifying vulnerabilities to cognitive decline and neurodegenerative diseases. Currently, there is a lack of comprehensive understanding of the temporal dynamics and neuroimaging biomarkers linked to ABA and RBA. This study addressed this gap by utilizing a large-scale UK Biobank (UKB) cohort, with the aim to elucidate brain aging heterogeneity and establish the foundation for targeted interventions. Employing Lasso regression on multimodal neuroimaging data, structural MRI (sMRI), diffusion MRI (dMRI), and resting-state functional MRI (rsfMRI), we predicted the brain age and classified individuals into ABA and RBA cohorts. Our findings identified 1949 subjects (6.2%) as representative of the ABA subpopulation and 3203 subjects (10.1%) as representative of the RBA subpopulation. Additionally, the Discriminative Event-Based Model (DEBM) was applied to estimate the sequence of biomarker changes across aging trajectories. Our analysis unveiled distinct central ordering patterns between the ABA and RBA cohorts, with profound implications for understanding cognitive decline and vulnerability to neurodegenerative disorders. Specifically, the ABA cohort exhibited early degeneration in four functional networks and two cognitive domains, with cortical thinning initially observed in the right hemisphere, followed by the temporal lobe. In contrast, the RBA cohort demonstrated initial degeneration in the three functional networks, with cortical thinning predominantly in the left hemisphere and white matter microstructural degeneration occurring at more advanced stages. The detailed aging progression timeline constructed through our DEBM analysis positioned subjects according to their estimated stage of aging, offering a nuanced view of the aging brain's alterations. This study holds promise for the development of targeted interventions aimed at mitigating age-related cognitive decline.
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Affiliation(s)
- Lan Lin
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Yutong Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Lingyu Liu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Base for Scientific and Technological Cooperation, Beijing University of Technology, Beijing 100124, China
| | - Shuicai Wu
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China; (Y.W.); (L.L.); (S.W.)
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3
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Estarellas M, Oxtoby NP, Schott JM, Alexander DC, Young AL. Multimodal subtypes identified in Alzheimer's Disease Neuroimaging Initiative participants by missing-data-enabled subtype and stage inference. Brain Commun 2024; 6:fcae219. [PMID: 39035417 PMCID: PMC11259979 DOI: 10.1093/braincomms/fcae219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 03/14/2024] [Accepted: 06/22/2024] [Indexed: 07/23/2024] Open
Abstract
Alzheimer's disease is a highly heterogeneous disease in which different biomarkers are dynamic over different windows of the decades-long pathophysiological processes, and potentially have distinct involvement in different subgroups. Subtype and Stage Inference is an unsupervised learning algorithm that disentangles the phenotypic heterogeneity and temporal progression of disease biomarkers, providing disease insight and quantitative estimates of individual subtype and stage. However, a key limitation of Subtype and Stage Inference is that it requires a complete set of biomarkers for each subject, reducing the number of datapoints available for model fitting and limiting applications of Subtype and Stage Inference to modalities that are widely collected, e.g. volumetric biomarkers derived from structural MRI. In this study, we adapted the Subtype and Stage Inference algorithm to handle missing data, enabling the application of Subtype and Stage Inference to multimodal data (magnetic resonance imaging, positron emission tomography, cerebrospinal fluid and cognitive tests) from 789 participants in the Alzheimer's Disease Neuroimaging Initiative. Missing-data Subtype and Stage Inference identified five subtypes having distinct progression patterns, which we describe by the earliest unique abnormality as 'Typical AD with Early Tau', 'Typical AD with Late Tau', 'Cortical', 'Cognitive' and 'Subcortical'. These new multimodal subtypes were differentially associated with age, years of education, Apolipoprotein E (APOE4) status, white matter hyperintensity burden and the rate of conversion from mild cognitive impairment to Alzheimer's disease, with the 'Cognitive' subtype showing the fastest clinical progression, and the 'Subcortical' subtype the slowest. Overall, we demonstrate that missing-data Subtype and Stage Inference reveals a finer landscape of Alzheimer's disease subtypes, each of which are associated with different risk factors. Missing-data Subtype and Stage Inference has broad utility, enabling the prediction of progression in a much wider set of individuals, rather than being restricted to those with complete data.
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Affiliation(s)
- Mar Estarellas
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- School of Biological and Behavioural Sciences, Queen Mary University of London, London, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Alexandra L Young
- 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
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Liu L, Sun S, Kang W, Wu S, Lin L. A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis. Rev Neurosci 2024; 35:121-139. [PMID: 37419866 DOI: 10.1515/revneuro-2023-0033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/18/2023] [Indexed: 07/09/2023]
Abstract
Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.
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Affiliation(s)
- Lingyu Liu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shen Sun
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Wenjie Kang
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Shuicai Wu
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
| | - Lan Lin
- Intelligent Physiological Measurement and Clinical Translation, Beijing International Platform for Scientific and Technological Cooperation, Department of Biomedical Engineering, Faculty of Environment and Life Sciences, Beijing University of Technology, Beijing, 100124, China
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Jia J, Ning Y, Chen M, Wang S, Yang H, Li F, Ding J, Li Y, Zhao B, Lyu J, Yang S, Yan X, Wang Y, Qin W, Wang Q, Li Y, Zhang J, Liang F, Liao Z, Wang S. Biomarker Changes during 20 Years Preceding Alzheimer's Disease. N Engl J Med 2024; 390:712-722. [PMID: 38381674 DOI: 10.1056/nejmoa2310168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
BACKGROUND Biomarker changes that occur in the period between normal cognition and the diagnosis of sporadic Alzheimer's disease have not been extensively investigated in longitudinal studies. METHODS We conducted a multicenter, nested case-control study of Alzheimer's disease biomarkers in cognitively normal participants who were enrolled in the China Cognition and Aging Study from January 2000 through December 2020. A subgroup of these participants underwent testing of cerebrospinal fluid (CSF), cognitive assessments, and brain imaging at 2-year-to-3-year intervals. A total of 648 participants in whom Alzheimer's disease developed were matched with 648 participants who had normal cognition, and the temporal trajectories of CSF biochemical marker concentrations, cognitive testing, and imaging were analyzed in the two groups. RESULTS The median follow-up was 19.9 years (interquartile range, 19.5 to 20.2). CSF and imaging biomarkers in the Alzheimer's disease group diverged from those in the cognitively normal group at the following estimated number of years before diagnosis: amyloid-beta (Aβ)42, 18 years; the ratio of Aβ42 to Aβ40, 14 years; phosphorylated tau 181, 11 years; total tau, 10 years; neurofilament light chain, 9 years; hippocampal volume, 8 years; and cognitive decline, 6 years. As cognitive impairment progressed, the changes in CSF biomarker levels in the Alzheimer's disease group initially accelerated and then slowed. CONCLUSIONS In this study involving Chinese participants during the 20 years preceding clinical diagnosis of sporadic Alzheimer's disease, we observed the time courses of CSF biomarkers, the times before diagnosis at which they diverged from the biomarkers from a matched group of participants who remained cognitively normal, and the temporal order in which the biomarkers became abnormal. (Funded by the Key Project of the National Natural Science Foundation of China and others; ClinicalTrials.gov number, NCT03653156.).
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Affiliation(s)
- Jianping Jia
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Yuye Ning
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Meilin Chen
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Shuheng Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Hao Yang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Fangyu Li
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Jiayi Ding
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Yan Li
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Bote Zhao
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Jihui Lyu
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Shanshan Yang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Xin Yan
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Yue Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Wei Qin
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Qi Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Ying Li
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Jintao Zhang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Furu Liang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Zhengluan Liao
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
| | - Shan Wang
- From the Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital (J.J., Y.N., M.C., Shuheng Wang, H.Y., F. Li, J.D., Yan Li, B.Z., W.Q., Q.W., Ying Li), Beijing Key Laboratory of Geriatric Cognitive Disorders, Clinical Center for Neurodegenerative Disease and Memory Impairment (J.J.), the Center of Alzheimer's Disease, Beijing Institute of Brain Disorders, Collaborative Innovation Center for Brain Disorders (J.J.), and the Department of Neurology, Beijing Anding Hospital (Y.W.), Capital Medical University, Key Laboratory of Neurodegenerative Diseases, Ministry of Education (J.J.), the Center for Cognitive Disorders, Beijing Geriatric Hospital (J.L.), and the Department of Neurology, Beijing Jishuitan Hospital (X.Y.), Beijing, the Department of Neurology, Daqing Oilfield General Hospital, Daqing (S.Y.), the Department of Neurology, the 960th Hospital of the People's Liberation Army, Jinan (J.Z.), the Department of Neurology, Baotou Central Hospital, Baotou (F. Liang), the Department of Psychiatry, Zhejiang Provincial People's Hospital, Hangzhou (Z.L.), and the Department of Neurology, Second Hospital of Hebei Medical University, Shijiazhuang (Shan Wang) - all in China
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6
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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.
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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
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7
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Lin X, Feng T, Cui E, Li Y, Qin Z, Zhao X. A rat model established by simulating genetic-environmental interactions recapitulates human Alzheimer's disease pathology. Brain Res 2024; 1822:148663. [PMID: 37918702 DOI: 10.1016/j.brainres.2023.148663] [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: 08/02/2023] [Revised: 10/16/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND In humans, Alzheimer's disease (AD) is typically sporadic in nature, and its pathology is usually influenced by extensive factors. The study established a rat model based on the genetic-environmental interaction. METHODS A rat model was established by transduction of an adeno-associated virus combined with acrolein treatment. Rats were assigned to the normal control (NC), acrolein group, AAV (-) group, AAV-APP group, and AAV-APP/acrolein group. The success of model construction was verified in multiple ways, including by assessing cognitive function, examining microstructural changes in the brain in vivo, and performing immunohistochemistry. The contribution of genetic (APP mutation) and environmental (acrolein) factors to AD-like phenotypes in the model was explored by factorial analysis. RESULTS 1) The AAV-APP/acrolein group showed a decline in cognitive function, as indicated by a reduced gray matter volume in key cognition-related brain areas, lower FA values in the hippocampus and internal olfactory cortex, and Aβ deposition in the cortex and hippocampus. 2) The AAV-APP group also showed a decline in cognitive function, although the group exhibited atypical brain atrophy in the gray matter and insignificant Aβ deposition. 3) The acrolein group did not show any significant changes in Aβ levels, gray matter volume, or cognitive function. 4) The genetic factor (APP mutation) explained 39.74% of the AD-like phenotypes in the model factors, and the environmental factor (acrolein exposure) explained 33.3%. CONCLUSIONS The genetic-environmental interaction rat model exhibited a phenotype that resembled the features of human AD and will be useful for research on AD.
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Affiliation(s)
- Xiaomei Lin
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Tianyuyi Feng
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Erheng Cui
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Yunfei Li
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Zhang Qin
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Xiaohu Zhao
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China.
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8
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Shen T, Vogel JW, Duda J, Phillips JS, Cook PA, Gee J, Elman L, Quinn C, Amado DA, Baer M, Massimo L, Grossman M, Irwin DJ, McMillan CT. Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum. Transl Neurodegener 2023; 12:57. [PMID: 38062485 PMCID: PMC10701950 DOI: 10.1186/s40035-023-00389-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND TDP-43 proteinopathies represent a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study was aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum. METHODS We used a data-driven procedure to identify 13 anatomic clusters of brain volume for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease. RESULTS SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy in either prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 types B, E and C. In contrast, the prefrontal/somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. The overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology. CONCLUSIONS Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.
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Affiliation(s)
- Ting Shen
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jacob W Vogel
- Department of Clinical Sciences, SciLifeLab, Lund University, 222 42, Lund, Sweden
| | - Jeffrey Duda
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Jeffrey S Phillips
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Philip A Cook
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James Gee
- Penn Image Computing and Science Lab (PICSL), Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Elman
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Colin Quinn
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Defne A Amado
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Michael Baer
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Lauren Massimo
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Murray Grossman
- Penn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, 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
- Digital Neuropathology Laboratory, Department of Neurology, Perelman School of Medicine, University of Pennsylvania, 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.
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9
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Simons M, Levin J, Dichgans M. Tipping points in neurodegeneration. Neuron 2023; 111:2954-2968. [PMID: 37385247 DOI: 10.1016/j.neuron.2023.05.031] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 07/01/2023]
Abstract
In Alzheimer's disease (AD), Aβ deposits form slowly, several decades before further pathological events trigger neurodegeneration and dementia. However, a substantial proportion of affected individuals remains non-demented despite AD pathology, raising questions about the underlying factors that determine the transition to clinical disease. Here, we emphasize the critical function of resilience and resistance factors, which we extend beyond the concept of cognitive reserve to include the glial, immune, and vascular system. We review the evidence and use the metaphor of "tipping points" to illustrate how gradually forming AD neuropathology in the preclinical stage can transition to dementia once adaptive functions of the glial, immune, and vascular system are lost and self-reinforcing pathological cascades are unleashed. Thus, we propose an expanded framework for pathomechanistic research that focuses on tipping points and non-neuronal resilience mechanisms, which may represent previously untapped therapeutic targets in preclinical AD.
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Affiliation(s)
- Mikael Simons
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany; Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany.
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany; Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Martin Dichgans
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany; Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
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10
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Shen T, Vogel JW, Duda J, Phillips JS, Cook PA, Gee J, Elman L, Quinn C, Amado DA, Baer M, Massimo L, Grossman M, Irwin DJ, McMillan CT. Novel data-driven subtypes and stages of brain atrophy in the ALS-FTD spectrum. RESEARCH SQUARE 2023:rs.3.rs-3183113. [PMID: 37609205 PMCID: PMC10441467 DOI: 10.21203/rs.3.rs-3183113/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Background TDP-43 proteinopathies represents a spectrum of neurological disorders, anchored clinically on either end by amyotrophic lateral sclerosis (ALS) and frontotemporal degeneration (FTD). The ALS-FTD spectrum exhibits a diverse range of clinical presentations with overlapping phenotypes, highlighting its heterogeneity. This study aimed to use disease progression modeling to identify novel data-driven spatial and temporal subtypes of brain atrophy and its progression in the ALS-FTD spectrum. Methods We used a data-driven procedure to identify 13 anatomic clusters of brain volumes for 57 behavioral variant FTD (bvFTD; with either autopsy-confirmed TDP-43 or TDP-43 proteinopathy-associated genetic variants), 103 ALS, and 47 ALS-FTD patients with likely TDP-43. A Subtype and Stage Inference (SuStaIn) model was trained to identify subtypes of individuals along the ALS-FTD spectrum with distinct brain atrophy patterns, and we related subtypes and stages to clinical, genetic, and neuropathological features of disease. Results SuStaIn identified three novel subtypes: two disease subtypes with predominant brain atrophy either in prefrontal/somatomotor regions or limbic-related regions, and a normal-appearing group without obvious brain atrophy. The Limbic-predominant subtype tended to present with more impaired cognition, higher frequencies of pathogenic variants in TBK1 and TARDBP genes, and a higher proportion of TDP-43 type B, E and C. In contrast, the Prefrontal/Somatomotor-predominant subtype had higher frequencies of pathogenic variants in C9orf72 and GRN genes and higher proportion of TDP-43 type A. The normal-appearing brain group showed higher frequency of ALS relative to ALS-FTD and bvFTD patients, higher cognitive capacity, higher proportion of lower motor neuron onset, milder motor symptoms, and lower frequencies of genetic pathogenic variants. Overall SuStaIn stages also correlated with evidence for clinical progression including longer disease duration, higher King's stage, and cognitive decline. Additionally, SuStaIn stages differed across clinical phenotypes, genotypes and types of TDP-43 pathology. Conclusions Our findings suggest distinct neurodegenerative subtypes of disease along the ALS-FTD spectrum that can be identified in vivo, each with distinct brain atrophy, clinical, genetic and pathological patterns.
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Affiliation(s)
- Ting Shen
- University of Pennsylvania Perelman School of Medicine
| | | | - Jeffrey Duda
- University of Pennsylvania Perelman School of Medicine
| | | | - Philip A Cook
- University of Pennsylvania Perelman School of Medicine
| | - James Gee
- University of Pennsylvania Perelman School of Medicine
| | - Lauren Elman
- University of Pennsylvania Perelman School of Medicine
| | - Colin Quinn
- University of Pennsylvania Perelman School of Medicine
| | - Defne A Amado
- University of Pennsylvania Perelman School of Medicine
| | - Michael Baer
- University of Pennsylvania Perelman School of Medicine
| | | | | | - David J Irwin
- University of Pennsylvania Perelman School of Medicine
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11
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Quan M, Cao S, Wang Q, Wang S, Jia J. Genetic Phenotypes of Alzheimer's Disease: Mechanisms and Potential Therapy. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:333-349. [PMID: 37589021 PMCID: PMC10425323 DOI: 10.1007/s43657-023-00098-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/28/2023] [Accepted: 02/02/2023] [Indexed: 08/18/2023]
Abstract
Years of intensive research has brought us extensive knowledge on the genetic and molecular factors involved in Alzheimer's disease (AD). In addition to the mutations in the three main causative genes of familial AD (FAD) including presenilins and amyloid precursor protein genes, studies have identified several genes as the most plausible genes for the onset and progression of FAD, such as triggering receptor expressed on myeloid cells 2, sortilin-related receptor 1, and adenosine triphosphate-binding cassette transporter subfamily A member 7. The apolipoprotein E ε4 allele is reported to be the strongest genetic risk factor for sporadic AD (SAD), and it also plays an important role in FAD. Here, we reviewed recent developments in genetic and molecular studies that contributed to the understanding of the genetic phenotypes of FAD and compared them with SAD. We further reviewed the advancements in AD gene therapy and discussed the future perspectives based on the genetic phenotypes.
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Affiliation(s)
- Meina Quan
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
- National Medical Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, 100053 China
| | - Shuman Cao
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
| | - Qi Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
- National Medical Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, 100053 China
| | - Shiyuan Wang
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
| | - Jianping Jia
- Innovation Center for Neurological Disorders and Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
- National Medical Center for Neurological Disorders and National Clinical Research Center for Geriatric Diseases, Beijing, 100053 China
- Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, 100053 China
- Clinical Center for Neurodegenerative Disease and Memory Impairment, Capital Medical University, Beijing, 100053 China
- Center of Alzheimer’s Disease, Collaborative Innovation Center for Brain Disorders, Beijing Institute of Brain Disorders, Capital Medical University, Beijing, 100053 China
- Key Laboratory of Neurodegenerative Diseases, Ministry of Education, Beijing, 100053 China
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12
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Wijeratne PA, Eshaghi A, Scotton WJ, Kohli M, Aksman L, Oxtoby NP, Pustina D, Warner JH, Paulsen JS, Scahill RI, Sampaio C, Tabrizi SJ, Alexander DC. The temporal event-based model: Learning event timelines in progressive diseases. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-19. [PMID: 37719837 PMCID: PMC10503481 DOI: 10.1162/imag_a_00010] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/15/2023] [Indexed: 09/19/2023]
Abstract
Timelines of events, such as symptom appearance or a change in biomarker value, provide powerful signatures that characterise progressive diseases. Understanding and predicting the timing of events is important for clinical trials targeting individuals early in the disease course when putative treatments are likely to have the strongest effect. However, previous models of disease progression cannot estimate the time between events and provide only an ordering in which they change. Here, we introduce the temporal event-based model (TEBM), a new probabilistic model for inferring timelines of biomarker events from sparse and irregularly sampled datasets. We demonstrate the power of the TEBM in two neurodegenerative conditions: Alzheimer's disease (AD) and Huntington's disease (HD). In both diseases, the TEBM not only recapitulates current understanding of event orderings but also provides unique new ranges of timescales between consecutive events. We reproduce and validate these findings using external datasets in both diseases. We also demonstrate that the TEBM improves over current models; provides unique stratification capabilities; and enriches simulated clinical trials to achieve a power of 80 % with less than half the cohort size compared with random selection. The application of the TEBM naturally extends to a wide range of progressive conditions.
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Affiliation(s)
- Peter A. Wijeratne
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
- Department of Informatics, University of Sussex, Brighton, United Kingdom
| | - Arman Eshaghi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London, London, United Kingdom
| | - William J. Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, University College London, London, United Kingdom
| | - Maitrei Kohli
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Leon Aksman
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States
| | - Neil P. Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - John H. Warner
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - Jane S. Paulsen
- Departments of Neurology and Psychiatry, Carver College of Medicine, University of Iowa, Iowa City, Iowa, United States
| | - Rachael I. Scahill
- Huntington’s Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom
| | - Cristina Sampaio
- CHDI Management/CHDI Foundation, Princeton, New Jersey, United States
| | - Sarah J. Tabrizi
- Huntington’s Disease Centre, Department of Neurodegenerative Disease, University College London, Queen Square, London, United Kingdom
| | - Daniel C. Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Tandon R, Kirkpatrick A, Mitchell CS. sEBM: Scaling Event Based Models to Predict Disease Progression via Implicit Biomarker Selection and Clustering. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2023; 13939:208-221. [PMID: 38680427 PMCID: PMC11056195 DOI: 10.1007/978-3-031-34048-2_17] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/01/2024]
Abstract
The Event Based Model (EBM) is a probabilistic generative model to explore biomarker changes occurring as a disease progresses. Disease progression is hypothesized to occur through a sequence of biomarker dysregulation "events". The EBM estimates the biomarker dysregulation event sequence. It computes the data likelihood for a given dysregulation sequence, and subsequently evaluates the posterior distribution on the dysregulation sequence. Since the posterior distribution is intractable, Markov Chain Monte-Carlo is employed to generate samples under the posterior distribution. However, the set of possible sequences increases as N ! where N is the number of biomarkers (data dimension) and quickly becomes prohibitively large for effective sampling via MCMC. This work proposes the "scaled EBM" (sEBM) to enable event based modeling on large biomarker sets (e.g. high-dimensional data). First, sEBM implicitly selects a subset of biomarkers useful for modeling disease progression and infers the event sequence only for that subset. Second, sEBM clusters biomarkers with similar positions in the event sequence and only orders the "clusters", with each successive cluster corresponding to the next stage in disease progression. These two modifications used to construct the sEBM method provably reduces the possible space of event sequences by multiple orders of magnitude. The novel modifications are supported by theory and experiments on synthetic and real clinical data provides validation for sEBM to work in higher dimensional settings. Results on synthetic data with known ground truth shows that sEBM outperforms previous EBM variants as data dimensions increase. sEBM was successfully implemented with up to 300 biomarkers, which is a 6-fold increase over previous EBM applications. A real-world clinical application of sEBM is performed using 119 neuroimaging markers from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data to stratify subjects into 6 stages of disease progression. Subjects included cognitively normal (CN), mild cognitive impairment (MCI), and Alzheimer's Disease (AD). sEBM stage is differentiated for the 3 groups ( χ 2 p - v a l u e < 4.6 e - 32 ) . Increased sEBM stage is a strong predictor of conversion risk to AD ( p - v a l u e < 2.3 e - 14 ) for MCI subjects, as verified with a Cox proportional-hazards model adjusted for age, sex, education and APOE4 status. Like EBM, sEBM does not rely on apriori defined diagnostic labels and only uses cross-sectional data.
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Affiliation(s)
- Raghav Tandon
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Anna Kirkpatrick
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
- School of Mathematics, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Cassie S Mitchell
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Pan F, Huang Y, Cai X, Wang Y, Guan Y, Deng J, Yang D, Zhu J, Zhao Y, Xie F, Fang Z, Guo Q. Integrated algorithm combining plasma biomarkers and cognitive assessments accurately predicts brain β-amyloid pathology. COMMUNICATIONS MEDICINE 2023; 3:65. [PMID: 37165172 PMCID: PMC10172320 DOI: 10.1038/s43856-023-00295-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 04/27/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND Accurate prediction of cerebral amyloidosis with easily available indicators is urgently needed for diagnosis and treatment of Alzheimer's disease (AD). METHODS We examined plasma Aβ42, Aβ40, T-tau, P-tau181, and NfL, with APOE genotypes, cognitive test scores and key demographics in a large Chinese cohort (N = 609, aged 40 to 84 years) covering full AD spectrum. Data-driven integrated computational models were developed to predict brain β-amyloid (Aβ) pathology. RESULTS Our computational models accurately predict brain Aβ positivity (area under the ROC curves (AUC) = 0.94). The results are validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Particularly, the models have the highest prediction power (AUC = 0.97) in mild cognitive impairment (MCI) participants. Three levels of models are designed with different accuracies and complexities. The model which only consists of plasma biomarkers can predict Aβ positivity in amnestic MCI (aMCI) patients with AUC = 0.89. Generally the models perform better in participants without comorbidities or family histories. CONCLUSIONS The innovative integrated models provide opportunity to assess Aβ pathology in a non-invasive and cost-effective way, which might facilitate AD-drug development, early screening, clinical diagnosis and prognosis evaluation.
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Affiliation(s)
- Fengfeng Pan
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yanlu Huang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Xiao Cai
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Ying Wang
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jiale Deng
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Dake Yang
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Jinhang Zhu
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Yike Zhao
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China
| | - Fang Xie
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.
| | - Zhuo Fang
- Department of Data & Analytics, WuXi Diagnostics Innovation Research Institute, Shanghai, China.
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China.
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Whiteside DJ, Malpetti M, Jones PS, Ghosh BCP, Coyle‐Gilchrist I, van Swieten JC, Seelaar H, Jiskoot L, Borroni B, Sanchez‐Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Butler CR, Santana I, Ber IL, Gerhard A, Ducharme S, Levin J, Danek A, Otto M, Sorbi S, Pasquier F, Bouzigues A, Russell LL, Rohrer JD, Rowe JB, Rittman T. Temporal dynamics predict symptom onset and cognitive decline in familial frontotemporal dementia. Alzheimers Dement 2023; 19:1947-1962. [PMID: 36377606 PMCID: PMC7614527 DOI: 10.1002/alz.12824] [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: 07/05/2022] [Revised: 09/13/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022]
Abstract
INTRODUCTION We tested whether changes in functional networks predict cognitive decline and conversion from the presymptomatic prodrome to symptomatic disease in familial frontotemporal dementia (FTD). METHODS For hypothesis generation, 36 participants with behavioral variant FTD (bvFTD) and 34 controls were recruited from one site. For hypothesis testing, we studied 198 symptomatic FTD mutation carriers, 341 presymptomatic mutation carriers, and 329 family members without mutations. We compared functional network dynamics between groups, with clinical severity and with longitudinal clinical progression. RESULTS We identified a characteristic pattern of dynamic network changes in FTD, which correlated with neuropsychological impairment. Among presymptomatic mutation carriers, this pattern of network dynamics was found to a greater extent in those who subsequently converted to the symptomatic phase. Baseline network dynamic changes predicted future cognitive decline in symptomatic participants and older presymptomatic participants. DISCUSSION Dynamic network abnormalities in FTD predict cognitive decline and symptomatic conversion. HIGHLIGHTS We investigated brain network predictors of dementia symptom onset Frontotemporal dementia results in characteristic dynamic network patterns Alterations in network dynamics are associated with neuropsychological impairment Network dynamic changes predict symptomatic conversion in presymptomatic carriers Network dynamic changes are associated with longitudinal cognitive decline.
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Affiliation(s)
- David J. Whiteside
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
- Cambridge University Hospitals NHS Foundation TrustCambridgeUK
| | - Maura Malpetti
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
| | - P. Simon Jones
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
| | - Boyd C. P. Ghosh
- Wessex Neurological CentreUniversity Hospital SouthamptonSouthamptonUK
| | | | | | - Harro Seelaar
- Department of NeurologyErasmus Medical CentreRotterdamNetherlands
| | - Lize Jiskoot
- Department of NeurologyErasmus Medical CentreRotterdamNetherlands
| | - Barbara Borroni
- Centre for Neurodegenerative DisordersDepartment of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Raquel Sanchez‐Valle
- Alzheimer's Disease and Other Cognitive Disorders UnitNeurology Service, Hospital ClínicInstitut d'Investigacións Biomèdiques August Pi I SunyerUniversity of BarcelonaBarcelonaSpain
| | - Fermin Moreno
- Cognitive Disorders UnitDepartment of NeurologyDonostia University HospitalSan SebastianGipuzkoaSpain
- Neuroscience AreaBiodonostia Health Research InstituteSan SebastianGipuzkoaSpain
| | - Robert Laforce
- CHU de Québec, and Faculté de MédecineDépartement des Sciences NeurologiquesClinique Interdisciplinaire de Mémoire, Université LavalQCCanada
| | - Caroline Graff
- Center for Alzheimer ResearchDivision of NeurogeriatricsDepartment of Neurobiology, Care Sciences and SocietyBioclinicum, Karolinska InstitutetSolnaSweden
- Unit for Hereditary Dementias, Theme AgingKarolinska University HospitalSolnaSweden
| | - Matthis Synofzik
- Department of Neurodegenerative DiseasesHertie‐Institute for Clinical Brain ResearchTübingenGermany
- Center of NeurologyUniversity of TübingenTübingenGermany
| | - Daniela Galimberti
- Fondazione IRCCS Ospedale PoliclinicoMilanItaly
- Department of Biomedical, Surgical and Dental SciencesUniversity of MilanMilanItaly
| | - Mario Masellis
- Sunnybrook Health Sciences CentreSunnybrook Research InstituteUniversity of TorontoTorontoCanada
| | | | - Elizabeth Finger
- Department of Clinical Neurological SciencesUniversity of Western OntarioLondonOntarioCanada
| | - Rik Vandenberghe
- Laboratory for Cognitive NeurologyDepartment of NeurosciencesKU LeuvenLeuvenBelgium
- Neurology ServiceUniversity Hospitals LeuvenBelgium
- Leuven Brain InstituteKU LeuvenLeuvenBelgium
| | | | | | - Chris R. Butler
- Nuffield Department of Clinical NeurosciencesMedical Sciences DivisionUniversity of OxfordOxfordUK
- Department of Brain SciencesImperial College LondonLondonUK
| | - Isabel Santana
- University Hospital of Coimbra (HUC)Neurology Service, Faculty of MedicineUniversity of CoimbraCoimbraPortugal
- Center for Neuroscience and Cell BiologyFaculty of MedicineUniversity of CoimbraCoimbraPortugal
| | - Isabelle Le Ber
- Paris Brain Institute – Institut du Cerveau – ICMInserm U1127, CNRS UMR 7225, AP‐HP ‐ Hôpital Pitié‐SalpêtrièreSorbonne UniversitéParisFrance
- Centre de référence des démences rares ou précoces, IM2ADépartement de NeurologieAP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
- Département de NeurologieAP‐HP ‐ Hôpital Pitié‐SalpêtrièreParisFrance
| | - Alexander Gerhard
- Division of Neuroscience and Experimental PsychologyWolfson Molecular Imaging CentreUniversity of ManchesterManchesterUK
- Departments of Geriatric Medicine and Nuclear MedicineUniversity of Duisburg‐ EssenDuisburgGermany
| | - Simon Ducharme
- Department of PsychiatryMcGill University Health CentreMcGill UniversityMontrealQuébecCanada
- Department of Neurology & NeurosurgeryMcConnell Brain Imaging CentreMontreal Neurological InstituteMcGill UniversityMontrealCanada
| | - Johannes Levin
- Neurologische KlinikLudwig‐Maximilians‐Universität MünchenMunichGermany
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Munich Cluster of Systems NeurologyMunichGermany
| | - Adrian Danek
- Neurologische KlinikLudwig‐Maximilians‐Universität MünchenMunichGermany
| | - Markus Otto
- Department of NeurologyUniversity of UlmUlmGermany
| | - Sandro Sorbi
- Department of NeurofarbaUniversity of FlorenceFlorenceItaly
- IRCCS Fondazione Don Carlo GnocchiFlorenceItaly
| | - Florence Pasquier
- Univ LilleLilleFrance
- Inserm 1172LilleFrance
- CHU, CNR‐MAJ, Labex DistalzLiCEND LilleLilleFrance
| | - Arabella Bouzigues
- Department of Neurodegenerative DiseaseDementia Research Centre UCL Institute of NeurologyQueen SquareLondonUK
| | - Lucy L. Russell
- Department of Neurodegenerative DiseaseDementia Research Centre UCL Institute of NeurologyQueen SquareLondonUK
| | - Jonathan D. Rohrer
- Department of Neurodegenerative DiseaseDementia Research Centre UCL Institute of NeurologyQueen SquareLondonUK
| | - James B. Rowe
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
- Cambridge University Hospitals NHS Foundation TrustCambridgeUK
- MRC Cognition and Brain Sciences UnitUniversity of CambridgeCambridgeUK
| | - Timothy Rittman
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeCambridgeshireUK
- Cambridge University Hospitals NHS Foundation TrustCambridgeUK
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Yu X, Zhang L, Lyu Y, Liu T, Zhu D. SUPERVISED DEEP TREE IN ALZHEIMER'S DISEASE. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2023; 2023:10.1109/isbi53787.2023.10230742. [PMID: 38362508 PMCID: PMC10869122 DOI: 10.1109/isbi53787.2023.10230742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
As a progressive neurodegenerative disorder, the pathological changes of Alzheimer's disease (AD) might begin as much as two decades before the manifestation of clinical symptoms. Since the nature of the irreversible pathology of AD, early diagnosis provides a more tractable way for disease intervention and treatment. Therefore, numerous approaches have been developed for early diagnostic purposes. Although several important biomarkers have been established, most of the existing methods show limitations in describing the continuum of AD progression. However, understanding this continuous development is essential to understand the intrinsic progression mechanism of AD. In this work, we proposed a supervised deep tree model (SDTree) to integrate AD progression and individual prediction. The proposed SDTree method models the progression of AD as a tree embedded in a latent space using nonlinear reversed graph embedding. In this way, the continuum of AD progression is encoded into the locations on the tree structure. The learned tree structure can not only represent the continuum of AD but make predictions for new subjects. We evaluated our method on the classification task and achieved promising results on Alzheimer's Disease Neuroimaging Initiative dataset.
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Affiliation(s)
- Xiaowei Yu
- Computer Science and Engineering, University of Texas at Arlington, TX, USA
| | - Lu Zhang
- Computer Science and Engineering, University of Texas at Arlington, TX, USA
| | - Yanjun Lyu
- Computer Science and Engineering, University of Texas at Arlington, TX, USA
| | - Tianming Liu
- Computer Science, The University of Georgia, Athens, USA
| | - Dajiang Zhu
- Computer Science and Engineering, University of Texas at Arlington, TX, USA
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17
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Fronza MG, Sacramento M, Alves D, Praticò D, Savegnago L. QTC-4-MeOBnE Ameliorated Depressive-Like Behavior and Memory Impairment in 3xTg Mice. Mol Neurobiol 2023; 60:1733-1745. [PMID: 36567360 DOI: 10.1007/s12035-022-03159-w] [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: 08/08/2022] [Accepted: 12/01/2022] [Indexed: 12/27/2022]
Abstract
Growing evidence has associated major depressive disorder (MDD) as a risk factor or prodromal syndrome for the occurrence of Alzheimer's disease (AD). Although this dilemma remains open, it is widely shown that a lifetime history of MDD is correlated with faster progression of AD pathology. Therefore, antidepressant drugs with neuroprotective effects could be an interesting therapeutic conception to target this issue simultaneously. In this sense, 1-(7-chloroquinolin-4-yl)-N-(4-methoxybenzyl)-5-methyl-1H-1,2,3-triazole-4- carboxamide (QTC-4-MeOBnE) was initially conceived as a multi-target ligand with affinity to β-secretase (BACE), glycogen synthase kinase 3β (GSK3β), and acetylcholinesterase but has also shown secondary effects on pathways involved in neuroinflammation and neurogenesis in preclinical models of AD. Herein, we investigated the effect of QTC-4-MeOBnE (1 mg/kg) administration for 45 days on depressive-like behavior and memory impairment in 3xTg mice, before the pathology is completely established. The treatment with QTC-4-MeOBnE prevented memory impairment and depressive-like behavior assessed by the Y-Maze task and forced swimming test. This effect was associated with the modulation of plural pathways involved in the onset and progression of AD, in cerebral structures of the cortex and hippocampus. Among them, the reduction of amyloid beta (Aβ) production mediated by changes in amyloid precursor protein metabolism and hippocampal tau phosphorylation through the inhibition of kinases. Additionally, QTC-4-MeOBnE also exerted beneficial effects on neuroinflammation and synaptic integrity. Overall, our studies suggest that QTC-4-MeOBnE has a moderate effect in a transgenic model of AD, indicating that perhaps studies regarding the neuropsychiatric effects as a neuroprotective molecule are more prone to be feasible.
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Affiliation(s)
- Mariana G Fronza
- Neurobiotechnology Research Group (GPN) - Postgraduate Program of Biotechnology, Federal University of Pelotas (UFPel), Pelotas, RS, Brazil
| | - Manoela Sacramento
- Laboratory of Clean Organic Synthesis (LASOL), Postgraduate Program of Chemistry, UFPel, Pelotas, RS, Brazil
| | - Diego Alves
- Laboratory of Clean Organic Synthesis (LASOL), Postgraduate Program of Chemistry, UFPel, Pelotas, RS, Brazil
| | - Domenico Praticò
- Alzheimer's Center at Temple - ACT, Temple University, Lewis Katz School of Medicine, Philadelphia, PA, USA
| | - Lucielli Savegnago
- Neurobiotechnology Research Group (GPN) - Postgraduate Program of Biotechnology, Federal University of Pelotas (UFPel), Pelotas, RS, Brazil.
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18
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A multidimensional ODE-based model of Alzheimer's disease progression. Sci Rep 2023; 13:3162. [PMID: 36823416 PMCID: PMC9950424 DOI: 10.1038/s41598-023-29383-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
Data-driven Alzheimer's disease (AD) progression models are useful for clinical prediction, disease mechanism understanding, and clinical trial design. Most dynamic models were inspired by the amyloid cascade hypothesis and described AD progression as a linear chain of pathological events. However, the heterogeneity observed in healthy and sporadic AD populations challenged the amyloid hypothesis, and there is a need for more flexible dynamical models that accompany this conceptual shift. We present a statistical model of the temporal evolution of biomarkers and cognitive tests that allows diverse biomarker paths throughout the disease. The model consists of two elements: a multivariate dynamic model of the joint evolution of biomarkers and cognitive tests; and a clinical prediction model. The dynamic model uses a system of ordinary differential equations to jointly model the rate of change of an individual's biomarkers and cognitive tests. The clinical prediction model is an ordinal logistic model of the diagnostic label. Prognosis and time-to-onset predictions are obtained by computing the clinical label probabilities throughout the forecasted biomarker trajectories. The proposed dynamical model is interpretable, free of one-dimensional progression hypotheses or disease staging paradigms, and can account for the heterogeneous dynamics observed in sporadic AD. We developed the model using longitudinal data from the Alzheimer's Disease Neuroimaging Initiative. We illustrate the patterns of biomarker rates of change and the model performance to predict the time to conversion from MCI to dementia.
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19
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The Molecular Effects of Environmental Enrichment on Alzheimer's Disease. Mol Neurobiol 2022; 59:7095-7118. [PMID: 36083518 PMCID: PMC9616781 DOI: 10.1007/s12035-022-03016-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/23/2022] [Indexed: 12/02/2022]
Abstract
Environmental enrichment (EE) is an environmental paradigm encompassing sensory, cognitive, and physical stimulation at a heightened level. Previous studies have reported the beneficial effects of EE in the brain, particularly in the hippocampus. EE improves cognitive function as well as ameliorates depressive and anxiety-like behaviors, making it a potentially effective neuroprotective strategy against neurodegenerative diseases such as Alzheimer's disease (AD). Here, we summarize the current evidence for EE as a neuroprotective strategy as well as the potential molecular pathways that can explain the effects of EE from a biochemical perspective using animal models. The effectiveness of EE in enhancing brain activity against neurodegeneration is explored with a view to differences present in early and late life EE exposure, with its potential application in human being discussed. We discuss EE as one of the non pharmacological approaches in preventing or delaying the onset of AD for future research.
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20
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Ren G, Yu K, Xie Z, Wang P, Zhang W, Huang Y, Wang Y, Wu X. Current Applications of Machine Learning in Spine: From Clinical View. Global Spine J 2022; 12:1827-1840. [PMID: 34628966 PMCID: PMC9609532 DOI: 10.1177/21925682211035363] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
STUDY DESIGN Narrative review. OBJECTIVES This review aims to present current applications of machine learning (ML) in spine domain to clinicians. METHODS We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text. RESULTS Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost. CONCLUSIONS ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.
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Affiliation(s)
- GuanRui Ren
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Kun Yu
- Nanjing Jiangbei Hospital, Nanjing,
Jiangsu, China
| | - ZhiYang Xie
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China
| | - PeiYang Wang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Wei Zhang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - Yong Huang
- Southeast University Medical College,
Nanjing, Jiangsu, China
| | - YunTao Wang
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,YunTao Wang, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
| | - XiaoTao Wu
- Department of Spine Surgery, Zhongda
Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, China,XiaoTao Wu, Department of Spine Surgery,
Zhongda Hospital, School of Medicine, Southeast University, No. 87, Dingjiaqiao
Road, Nanjing, Jiangsu 210009, China.
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21
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Staffaroni AM, Quintana M, Wendelberger B, Heuer HW, Russell LL, Cobigo Y, Wolf A, Goh SYM, Petrucelli L, Gendron TF, Heller C, Clark AL, Taylor JC, Wise A, Ong E, Forsberg L, Brushaber D, Rojas JC, VandeVrede L, Ljubenkov P, Kramer J, Casaletto KB, Appleby B, Bordelon Y, Botha H, Dickerson BC, Domoto-Reilly K, Fields JA, Foroud T, Gavrilova R, Geschwind D, Ghoshal N, Goldman J, Graff-Radford J, Graff-Radford N, Grossman M, Hall MGH, Hsiung GY, Huey ED, Irwin D, Jones DT, Kantarci K, Kaufer D, Knopman D, Kremers W, Lago AL, Lapid MI, Litvan I, Lucente D, Mackenzie IR, Mendez MF, Mester C, Miller BL, Onyike CU, Rademakers R, Ramanan VK, Ramos EM, Rao M, Rascovsky K, Rankin KP, Roberson ED, Savica R, Tartaglia MC, Weintraub S, Wong B, Cash DM, Bouzigues A, Swift IJ, Peakman G, Bocchetta M, Todd EG, Convery RS, Rowe JB, Borroni B, Galimberti D, Tiraboschi P, Masellis M, Finger E, van Swieten JC, Seelaar H, Jiskoot LC, Sorbi S, Butler CR, Graff C, Gerhard A, Langheinrich T, Laforce R, Sanchez-Valle R, de Mendonça A, Moreno F, Synofzik M, Vandenberghe R, Ducharme S, Le Ber I, Levin J, Danek A, Otto M, Pasquier F, Santana I, Kornak J, Boeve BF, Rosen HJ, Rohrer JD, Boxer AL. Temporal order of clinical and biomarker changes in familial frontotemporal dementia. Nat Med 2022; 28:2194-2206. [PMID: 36138153 PMCID: PMC9951811 DOI: 10.1038/s41591-022-01942-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 07/08/2022] [Indexed: 01/17/2023]
Abstract
Unlike familial Alzheimer's disease, we have been unable to accurately predict symptom onset in presymptomatic familial frontotemporal dementia (f-FTD) mutation carriers, which is a major hurdle to designing disease prevention trials. We developed multimodal models for f-FTD disease progression and estimated clinical trial sample sizes in C9orf72, GRN and MAPT mutation carriers. Models included longitudinal clinical and neuropsychological scores, regional brain volumes and plasma neurofilament light chain (NfL) in 796 carriers and 412 noncarrier controls. We found that the temporal ordering of clinical and biomarker progression differed by genotype. In prevention-trial simulations using model-based patient selection, atrophy and NfL were the best endpoints, whereas clinical measures were potential endpoints in early symptomatic trials. f-FTD prevention trials are feasible but will likely require global recruitment efforts. These disease progression models will facilitate the planning of f-FTD clinical trials, including the selection of optimal endpoints and enrollment criteria to maximize power to detect treatment effects.
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Affiliation(s)
- Adam M Staffaroni
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | | | | | - Hilary W Heuer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Lucy L Russell
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Yann Cobigo
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Amy Wolf
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Sheng-Yang Matt Goh
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | | | - Tania F Gendron
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Carolin Heller
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Annie L Clark
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jack Carson Taylor
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Amy Wise
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Elise Ong
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Leah Forsberg
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Danielle Brushaber
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Julio C Rojas
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Lawren VandeVrede
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Peter Ljubenkov
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Joel Kramer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Kaitlin B Casaletto
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Brian Appleby
- Department of Neurology, Case Western Reserve University, Cleveland, OH, USA
| | - Yvette Bordelon
- Department of Neurology, University of California, Los Angeles, Los Angeles, USA
| | - Hugo Botha
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Bradford C Dickerson
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Julie A Fields
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Tatiana Foroud
- Indiana University School of Medicine, National Centralized Repository for Alzheimer's, Indianapolis, IN, USA
| | | | - Daniel Geschwind
- Department of Neurology, University of California, Los Angeles, Los Angeles, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Nupur Ghoshal
- Departments of Neurology and Psychiatry, Washington University School of Medicine, Washington University, St. Louis, MO, USA
| | - Jill Goldman
- Department of Neurology, Columbia University, New York, NY, USA
| | | | | | - Murray Grossman
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew G H Hall
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Ging-Yuek Hsiung
- Division of Neurology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Edward D Huey
- Department of Neurology, Columbia University, New York, NY, USA
| | - David Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - David T Jones
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Kejal Kantarci
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Daniel Kaufer
- Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - David Knopman
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Walter Kremers
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Argentina Lario Lago
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Maria I Lapid
- Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA
| | - Irene Litvan
- Department of Neurosciences, University of California, San Diego, La Jolla, CA, USA
| | - Diane Lucente
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ian R Mackenzie
- Department of Pathology, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mario F Mendez
- Department of Neurology, University of California, Los Angeles, Los Angeles, USA
| | - Carly Mester
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Bruce L Miller
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Chiadi U Onyike
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
- Applied and Translational Neurogenomics Group, VIB Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | - Eliana Marisa Ramos
- Department of Neurology, University of California, Los Angeles, Los Angeles, USA
| | - Meghana Rao
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Katya Rascovsky
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - Katherine P Rankin
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Erik D Roberson
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rodolfo Savica
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - M Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, Division of Neurology, University of Toronto, Toronto, Ontario, Canada
| | - Sandra Weintraub
- Department of Neurology, Northwestern University, Chicago, IL, USA
| | - Bonnie Wong
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - David M Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Arabella Bouzigues
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Imogen J Swift
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Georgia Peakman
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Emily G Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Rhian S Convery
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - James B Rowe
- Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust and Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Daniela Galimberti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | | | - Mario Masellis
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre; Hurvitz Brain Sciences Program, Sunnybrook Research Institute, University of Toronto, Toronto, Ontario, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, Canada
| | | | - Harro Seelaar
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Lize C Jiskoot
- Department of Neurology, Erasmus Medical Centre, Rotterdam, Netherlands
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, Florence, Italy
- IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Chris R Butler
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Caroline Graff
- Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, Solna, Sweden
- Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Alexander Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Departments of Geriatric Medicine and Nuclear Medicine, Center for Translational Neuro- and Behavioral Sciences, University Medicine Essen, Essen, Germany
| | - Tobias Langheinrich
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Cerebral Function Unit, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Québec City, Québec, Canada
| | - Raquel Sanchez-Valle
- Alzheimer's disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d'Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | | | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain
- Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
- Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Service, University Hospitals Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Simon Ducharme
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Department of Neurology & Neurosurgery, McGill University, Montreal, Québec, Canada
| | - Isabelle Le Ber
- Sorbonne Université, Paris Brain Institute - Institut du Cerveau - ICM, Inserm U1127, CNRS UMR 7225, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France
- Centre de référence des démences rares ou précoces, IM2A, Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France
- Département de Neurologie, AP-HP - Hôpital Pitié-Salpêtrière, Paris, France
| | - Johannes Levin
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany
- Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology, Munich, Germany
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, Germany
| | - Markus Otto
- Department of Neurology, University of Ulm, Ulm, Germany
| | - Florence Pasquier
- University of Lille, Lille, France
- Inserm, Lille, France
- CHU, CNR-MAJ, Labex Distalz, LiCEND Lille, Lille, France
| | - Isabel Santana
- Neurology Service, Faculty of Medicine, University Hospital of Coimbra (HUC), University of Coimbra, Coimbra, Portugal
- Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - John Kornak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | | | - Howard J Rosen
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square London, London, UK
| | - Adam L Boxer
- Department of Neurology, Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
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Guo X, Chen K, Chen Y, Xiong C, Su Y, Yao L, Reiman EM. A Computational Monte Carlo Simulation Strategy to Determine the Temporal Ordering of Abnormal Age Onset Among Biomarkers of Alzheimer's Disease. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:2613-2622. [PMID: 34428151 PMCID: PMC9588284 DOI: 10.1109/tcbb.2021.3106939] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
To quantitatively determining the temporal ordering of abnormal age onsets (AAO) among various biomarkers for Alzheimer's disease (AD), we introduced a computational Monte-Carlo simulation (CMCS) to statistically examine such ordering of an AAO pair or over all AAOs. The CMCS 1) simulates longitudinal data, estimates AAO for each iteration, and finally assesses the type-I error of an AAO pair or all AAO ordering. Using hippocampus volume (VHC), cerebral glucose hypometabolic convergence index (HCI), plasma neurofilament light (NfL), mini-mental state exam (MMSE), the auditory verbal learning test-long term memory (AVLT-LTM), short term memory (AVLT-STM) and clinical-dementia rating sum of box scale (CDR-SOB) from 382 mild cognitive impairment converters and non-converters, the CMCS estimated type-I error for the earlier AAO of VHC, AVLT_STM and AVLT_LTM each than MMSE was significant (p<0.002). The type-I error for the overall AAO temporal ordering of VHC ≤ AVLT_STM ≤ AVLT_LTM < HCI ≤ MMSE ≤ CDR-SOB ≤ NfL was p = 0.012. These findings showed that our CMCS is capable of providing statistical inferences for quantifying AAO ordering which has important implications in advancing our understanding of AD.
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23
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van der Ende EL, Bron EE, Poos JM, Jiskoot LC, Panman JL, Papma JM, Meeter LH, Dopper EGP, Wilke C, Synofzik M, Heller C, Swift IJ, Sogorb-Esteve A, Bouzigues A, Borroni B, Sanchez-Valle R, Moreno F, Graff C, Laforce R, Galimberti D, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, Rowe JB, de Mendonça A, Tagliavini F, Santana I, Ducharme S, Butler CR, Gerhard A, Levin J, Danek A, Otto M, Pijnenburg YAL, Sorbi S, Zetterberg H, Niessen WJ, Rohrer JD, Klein S, van Swieten JC, Venkatraghavan V, Seelaar H. A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia. Brain 2022; 145:1805-1817. [PMID: 34633446 PMCID: PMC9166533 DOI: 10.1093/brain/awab382] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/22/2021] [Accepted: 09/09/2021] [Indexed: 11/17/2022] Open
Abstract
Several CSF and blood biomarkers for genetic frontotemporal dementia have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain and phosphorylated neurofilament heavy chain), synapse dysfunction [neuronal pentraxin 2 (NPTX2)], astrogliosis (glial fibrillary acidic protein) and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage frontotemporal dementia, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic frontotemporal dementia using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. Two-hundred and seventy-five presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on previous diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF neurofilament light chain, blood phosphorylated neurofilament heavy chain, blood glial fibrillary acidic protein and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The areas under the curve to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic frontotemporal dementia revealed that NPTX2 and neurofilament light chain are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions.
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Affiliation(s)
- Emma L van der Ende
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Esther E Bron
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Jackie M Poos
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Lize C Jiskoot
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Jessica L Panman
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Lieke H Meeter
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Elise G P Dopper
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Carlo Wilke
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 72076 Tübingen, Germany
| | - Matthis Synofzik
- German Center for Neurodegenerative Diseases (DZNE), 72076 Tübingen, Germany
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, 72076 Tübingen, Germany
| | - Carolin Heller
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Imogen J Swift
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Aitana Sogorb-Esteve
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Arabella Bouzigues
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, 25121 Brescia, Italy
| | - Raquel Sanchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clinic, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, 20014 Gipuzkoa, Spain
- Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain
| | - Caroline Graff
- Center for Alzheimer Research, Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Bioclinicum, Karolinska Institutet, 17176 Solna, Sweden
- Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, 17176 Solna, Sweden
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, Université Laval, G1Z 1J4 Québec, Canada
| | - Daniela Galimberti
- Centro Dino Ferrari, University of Milan, 20122 Milan, Italy
- Neurodegenerative Diseases Unit, Fondazione IRCCS, Ospedale Maggiore Policlinico, 20122 Milan, Italy
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, ON M4N 3M5 Toronto, Canada
| | - Maria Carmela Tartaglia
- Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, M5S 1A8 Toronto, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, ON N6A 3K7 London, Ontario, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
| | - James B Rowe
- Cambridge University Centre for Frontotemporal Dementia, University of Cambridge, CB2 0SZ Cambridge, UK
| | | | | | - Isabel Santana
- Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, 3004-504 Coimbra, Portugal
| | - Simon Ducharme
- McConnell Brain Imaging Centre, Montreal Neurological Institute and McGill University Health Centre, McGill University, 3801 Montreal, Québec, Canada
| | - Christopher R Butler
- Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, OX3 9DU Oxford, UK
- Department of Brain Sciences, Imperial College London, SW7 2AZ London, UK
| | - Alexander Gerhard
- Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, University of Manchester, M20 3LJ Manchester, UK
- Department of Nuclear Medicine and Geriatric Medicine, University Hospital Essen, 45 147 Essen, Germany
| | - Johannes Levin
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität München, 81377 Munich, Germany
- German Center for Neurodegenerative Diseases, 81377 Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
| | - Adrian Danek
- Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität München, 81377 Munich, Germany
| | - Markus Otto
- Department of Neurology, University of Ulm, 89081 Ulm, Germany
| | - Yolande A L Pijnenburg
- Department of Neurology, Alzheimer Center, Location VU University Medical Center Amsterdam Neuroscience, Amsterdam University Medical Center, 1105 AZ Amsterdam, The Netherlands
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, 50139 Florence, Italy
| | - Henrik Zetterberg
- UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, 405 30 Mölndal, Sweden
| | - Wiro J Niessen
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Jonathan D Rohrer
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, WC1N 3BG London, UK
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Vikram Venkatraghavan
- Department of Radiology and Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Harro Seelaar
- Department of Neurology and Alzheimer Center, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
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24
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Oxtoby NP, Shand C, Cash DM, Alexander DC, Barkhof F. Targeted Screening for Alzheimer's Disease Clinical Trials Using Data-Driven Disease Progression Models. Front Artif Intell 2022; 5:660581. [PMID: 35719690 PMCID: PMC9204250 DOI: 10.3389/frai.2022.660581] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 04/25/2022] [Indexed: 11/23/2022] Open
Abstract
Heterogeneity in Alzheimer's disease progression contributes to the ongoing failure to demonstrate efficacy of putative disease-modifying therapeutics that have been trialed over the past two decades. Any treatment effect present in a subgroup of trial participants (responders) can be diluted by non-responders who ideally should have been screened out of the trial. How to identify (screen-in) the most likely potential responders is an important question that is still without an answer. Here, we pilot a computational screening tool that leverages recent advances in data-driven disease progression modeling to improve stratification. This aims to increase the sensitivity to treatment effect by screening out non-responders, which will ultimately reduce the size, duration, and cost of a clinical trial. We demonstrate the concept of such a computational screening tool by retrospectively analyzing a completed double-blind clinical trial of donepezil in people with amnestic mild cognitive impairment (clinicaltrials.gov: NCT00000173), identifying a data-driven subgroup having more severe cognitive impairment who showed clearer treatment response than observed for the full cohort.
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Affiliation(s)
- Neil P. Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom,*Correspondence: Neil P. Oxtoby
| | - Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - David M. Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom,Amsterdam University Medical Center, Amsterdam, Netherlands
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25
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Kucikova L, Danso S, Jia L, Su L. Computational Psychiatry and Computational Neurology: Seeking for Mechanistic Modeling in Cognitive Impairment and Dementia. Front Comput Neurosci 2022; 16:865805. [PMID: 35645752 PMCID: PMC9130488 DOI: 10.3389/fncom.2022.865805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 04/25/2022] [Indexed: 12/02/2022] Open
Affiliation(s)
- Ludmila Kucikova
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience, Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
| | - Samuel Danso
- Edinburgh Dementia Prevention and Centre for Clinical Brain Sciences, Edinburgh Medical School, University of Edinburgh, Edinburgh, United Kingdom
| | - Lina Jia
- Beijing Anding Hospital, Capital Medical University, Beijing, China
| | - Li Su
- Department of Neuroscience, Sheffield Institute for Translational Neuroscience, Insigneo Institute for in silico Medicine, University of Sheffield, Sheffield, United Kingdom
- Department of Psychiatry, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- *Correspondence: Li Su
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26
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What contribution can genetics make to predict the risk of Alzheimer's disease? Rev Neurol (Paris) 2022; 178:414-421. [PMID: 35491248 DOI: 10.1016/j.neurol.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 03/08/2022] [Indexed: 11/20/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder. Although its etiology remains incompletely understood, genetic variants are important contributors. The prediction of AD risk through individual genetic variants is an important topic of research that may have individual and societal consequences when preventive treatments will become available. However, the genetic substratum of AD is heterogeneous. In addition to the extremely rare and fully penetrant pathogenic variants of the PSEN1, PSEN2 or APP genes causing autosomal dominant AD, a large spectrum of risk factors have been identified in complex forms, including the common risk factor APOEɛ4, which is associated with a moderate-to-high risk, common polymorphisms associated with a modest individual risk, and a plethora of rare variants in genes like SORL1, TREM2 or ABCA7 with moderate to high-magnitude effect. Understanding how these genetic factors contribute to AD risk in a given individual, in additional to non-genetic factors, remains a challenge. Over the last 10 years, age-related penetrance curves have progressively incorporated advances in the knowledge of AD genetics, from APOE to common polygenic components and, currently, SORL1 rare variants, which represents an important step towards precision medicine in AD. In this review, we present the complex genetic architecture of AD and we expose the prediction of AD risk according to its underlying genetic component.
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Golriz Khatami S, Salimi Y, Hofmann-Apitius M, Oxtoby NP, Birkenbihl C. Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer's disease. Alzheimers Res Ther 2022; 14:55. [PMID: 35443691 PMCID: PMC9020023 DOI: 10.1186/s13195-022-01001-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/06/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND Previous models of Alzheimer's disease (AD) progression were primarily hypothetical or based on data originating from single cohort studies. However, cohort datasets are subject to specific inclusion and exclusion criteria that influence the signals observed in their collected data. Furthermore, each study measures only a subset of AD-relevant variables. To gain a comprehensive understanding of AD progression, the heterogeneity and robustness of estimated progression patterns must be understood, and complementary information contained in cohort datasets be leveraged. METHODS We compared ten event-based models that we fit to ten independent AD cohort datasets. Additionally, we designed and applied a novel rank aggregation algorithm that combines partially overlapping, individual event sequences into a meta-sequence containing the complementary information from each cohort. RESULTS We observed overall consistency across the ten event-based model sequences (average pairwise Kendall's tau correlation coefficient of 0.69 ± 0.28), despite variance in the positioning of mainly imaging variables. The changes described in the aggregated meta-sequence are broadly consistent with the current understanding of AD progression, starting with cerebrospinal fluid amyloid beta, followed by tauopathy, memory impairment, FDG-PET, and ultimately brain deterioration and impairment of visual memory. CONCLUSION Overall, the event-based models demonstrated similar and robust disease cascades across independent AD cohorts. Aggregation of data-driven results can combine complementary strengths and information of patient-level datasets. Accordingly, the derived meta-sequence draws a more complete picture of AD pathology compared to models relying on single cohorts.
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Affiliation(s)
- Sepehr Golriz Khatami
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany.
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany.
| | - Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
| | - Neil P Oxtoby
- Centre for Medical Image Computing and Department of Computer Science, University College London, Gower St, London, WC1E 6BT, UK
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53757, Sankt Augustin, Germany
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, 53115, Bonn, Germany
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Levitis E, Vogel JW, Funck T, Hachinski V, Gauthier S, Vöglein J, Levin J, Gordon BA, Benzinger T, Iturria-Medina Y, Evans AC. Differentiating amyloid beta spread in autosomal dominant and sporadic Alzheimer's disease. Brain Commun 2022; 4:fcac085. [PMID: 35602652 PMCID: PMC9116976 DOI: 10.1093/braincomms/fcac085] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 12/05/2021] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
Amyloid-beta deposition is one of the hallmark pathologies in both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, the latter of which is caused by mutations in genes involved in amyloid-beta processing. Despite amyloid-beta deposition being a centrepiece to both sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease, some differences between these Alzheimer's disease subtypes have been observed with respect to the spatial pattern of amyloid-beta. Previous work has shown that the spatial pattern of amyloid-beta in individuals spanning the sporadic Alzheimer's disease spectrum can be reproduced with high accuracy using an epidemic spreading model which simulates the diffusion of amyloid-beta across neuronal connections and is constrained by individual rates of amyloid-beta production and clearance. However, it has not been investigated whether amyloid-beta deposition in the rarer autosomal-dominant Alzheimer's disease can be modelled in the same way, and if so, how congruent the spreading patterns of amyloid-beta across sporadic Alzheimer's disease and autosomal-dominant Alzheimer's disease are. We leverage the epidemic spreading model as a data-driven approach to probe individual-level variation in the spreading patterns of amyloid-beta across three different large-scale imaging datasets (2 sporadic Alzheimer's disease, 1 autosomal-dominant Alzheimer's disease). We applied the epidemic spreading model separately to the Alzheimer's Disease Neuroimaging initiative (n = 737), the Open Access Series of Imaging Studies (n = 510) and the Dominantly Inherited Alzheimer's Network (n = 249), the latter two of which were processed using an identical pipeline. We assessed inter- and intra-individual model performance in each dataset separately and further identified the most likely subject-specific epicentre of amyloid-beta spread. Using epicentres defined in previous work in sporadic Alzheimer's disease, the epidemic spreading model provided moderate prediction of the regional pattern of amyloid-beta deposition across all three datasets. We further find that, whilst the most likely epicentre for most amyloid-beta-positive subjects overlaps with the default mode network, 13% of autosomal-dominant Alzheimer's disease individuals were best characterized by a striatal origin of amyloid-beta spread. These subjects were also distinguished by being younger than autosomal-dominant Alzheimer's disease subjects with a default mode network amyloid-beta origin, despite having a similar estimated age of symptom onset. Together, our results suggest that most autosomal-dominant Alzheimer's disease patients express amyloid-beta spreading patterns similar to those of sporadic Alzheimer's disease, but that there may be a subset of autosomal-dominant Alzheimer's disease patients with a separate, striatal phenotype.
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Affiliation(s)
- Elizabeth Levitis
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Correspondence to: Elizabeth Levitis Magnuson Clinical Center Room 4N244, MSC 1367 Bethesda, MD 20814, USA E-mail:
| | - Jacob W Vogel
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Thomas Funck
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | | | - Serge Gauthier
- McGill Centre for Studies in Aging, McGill University, Montreal, QC, Canada
| | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases, Munich, Germany,Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases, Munich, Germany
| | - Brian A Gordon
- Department of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
| | - Tammie Benzinger
- Department of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, USA
| | - Yasser Iturria-Medina
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Correspondence may also be addressed to: Alan C. Evans Montreal Neurological Institute Montreal, H3A 2B4, Quebec Canada E-mail:
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
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Grangeon L, Paquet C, Guey S, Zarea A, Martinaud O, Rotharmel M, Maltête D, Quillard-Muraine M, Nicolas G, Charbonnier C, Chabriat H, Wallon D. Cerebrospinal Fluid Profile of Tau, Phosphorylated Tau, Aβ42, and Aβ40 in Probable Cerebral Amyloid Angiopathy. J Alzheimers Dis 2022; 87:791-802. [DOI: 10.3233/jad-215208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background: There is no consensus regarding the diagnostic value of cerebrospinal fluid (CSF) Alzheimer’s disease (AD) biomarkers in cerebral amyloid angiopathy (CAA). Objective: To describe the CSF levels of Aβ 42, Aβ 40, total protein Tau, and phosphorylated-Tau (p-Tau) in a large series of probable CAA patients and to compare with AD patients in order to identify a specific pattern in CAA but also to look for correlations with the neuroimaging profile. Methods: We retrospectively included from 2 French centers probable CAA patients according to modified Boston criteria who underwent lumbar puncture (LP) with CSF AD biomarker quantifications. Two neurologists independently analyzed all MRI sequences. A logistic regression and Spearman’s correlation coefficient were used to identify correlation between MRI and CSF biomarkers in CAA. Results: We included 63 probable CAA and 27 AD patients. Among CAA 50.8% presented with decreased Aβ 42 level associated with elevated p-Tau and/or Tau, 34.9% with isolated decreased Aβ 42 level and 14.3% patients with normal Aβ 42 level. Compared to AD, CAA showed lower levels of Tau (p = 0.008), p-Tau (p = 0.004), and Aβ 40 (p = 0.001) but similar Aβ 42 level (p = 0.07). No correlation between Aβ 42 or Aβ 40 levels and neuroimaging was found. Conclusion: CSF biomarkers may improve the accuracy of the modified Boston criteria with altered profile in 85% of the patients fulfilling revised Boston criteria for probable CAA. Aβ 40 appears as an interesting selective biomarker in differential diagnosis.
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Affiliation(s)
- Lou Grangeon
- Normandie Univ, UNIROUEN, Inserm U1245 and CHURouen, Department of Neurology and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, Rouen, France
| | - Claire Paquet
- CMRR Paris Nord AP-HP, Groupe Hospitalier Lariboisière Fernand-Widal Saint-Louis, INSERM, U942, Université Paris Diderot, Sorbonne Paris Cité, UMRS 942, France
| | - Stéphanie Guey
- Department of Neurology, AP-HP, Groupe Hospitalier Lariboisière Fernand-Widal Saint-Louis, Paris, France
| | - Aline Zarea
- Normandie Univ, UNIROUEN, Inserm U1245 and CHURouen, Department of Neurology and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, Rouen, France
| | | | - Maud Rotharmel
- Rouvray Hospital of Rouen, University Department of Psychiatry, France
| | - David Maltête
- Normandie Univ, UNIROUEN, Inserm U1245 and CHURouen, Department of Neurology and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, Rouen, France
| | | | - Gael Nicolas
- Normandie Univ, UNIROUEN, Inserm U1245 and CHU Rouen, Department of Genetics and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, Rouen, France
| | - Camille Charbonnier
- Normandie Univ, UNIROUEN, Inserm U1245 and CHU Rouen, Department of Genetics and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, Rouen, France
| | - Hugues Chabriat
- Department of Neurology, AP-HP, Groupe Hospitalier Lariboisière Fernand-Widal Saint-Louis, Paris, France
| | - David Wallon
- Normandie Univ, UNIROUEN, Inserm U1245 and CHURouen, Department of Neurology and CNR-MAJ, Normandy Center for Genomic and Personalized Medicine, Rouen, France
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30
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Golde TE. Alzheimer’s disease – the journey of a healthy brain into organ failure. Mol Neurodegener 2022; 17:18. [PMID: 35248124 PMCID: PMC8898417 DOI: 10.1186/s13024-022-00523-1] [Citation(s) in RCA: 39] [Impact Index Per Article: 19.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 02/17/2022] [Indexed: 12/19/2022] Open
Abstract
As the most common dementia, Alzheimer’s disease (AD) exacts an immense personal, societal, and economic toll. AD was first described at the neuropathological level in the early 1900s. Today, we have mechanistic insight into select aspects of AD pathogenesis and have the ability to clinically detect and diagnose AD and underlying AD pathologies in living patients. These insights demonstrate that AD is a complex, insidious, degenerative proteinopathy triggered by Aβ aggregate formation. Over time Aβ pathology drives neurofibrillary tangle (NFT) pathology, dysfunction of virtually all cell types in the brain, and ultimately, overt neurodegeneration. Yet, large gaps in our knowledge of AD pathophysiology and huge unmet medical need remain. Though we largely conceptualize AD as a disease of aging, heritable and non-heritable factors impact brain physiology, either continuously or at specific time points during the lifespan, and thereby alter risk for devolvement of AD. Herein, I describe the lifelong journey of a healthy brain from birth to death with AD, while acknowledging the many knowledge gaps that remain regarding our understanding of AD pathogenesis. To ensure the current lexicon surrounding AD changes from inevitable, incurable, and poorly manageable to a lexicon of preventable, curable, and manageable we must address these knowledge gaps, develop therapies that have a bigger impact on clinical symptoms or progression of disease and use these interventions at the appropriate stage of disease.
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Predicting progression of Alzheimer’s disease using forward-to-backward bi-directional network with integrative imputation. Neural Netw 2022; 150:422-439. [DOI: 10.1016/j.neunet.2022.03.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 02/23/2022] [Accepted: 03/10/2022] [Indexed: 11/20/2022]
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Saito Y, Kamagata K, Wijeratne PA, Andica C, Uchida W, Takabayashi K, Fujita S, Akashi T, Wada A, Shimoji K, Hori M, Masutani Y, Alexander DC, Aoki S. Temporal Progression Patterns of Brain Atrophy in Corticobasal Syndrome and Progressive Supranuclear Palsy Revealed by Subtype and Stage Inference (SuStaIn). Front Neurol 2022; 13:814768. [PMID: 35280291 PMCID: PMC8914081 DOI: 10.3389/fneur.2022.814768] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Differentiating corticobasal degeneration presenting with corticobasal syndrome (CBD-CBS) from progressive supranuclear palsy with Richardson's syndrome (PSP-RS), particularly in early stages, is often challenging because the neurodegenerative conditions closely overlap in terms of clinical presentation and pathology. Although volumetry using brain magnetic resonance imaging (MRI) has been studied in patients with CBS and PSP-RS, studies assessing the progression of brain atrophy are limited. Therefore, we aimed to reveal the difference in the temporal progression patterns of brain atrophy between patients with CBS and those with PSP-RS purely based on cross-sectional data using Subtype and Stage Inference (SuStaIn)—a novel, unsupervised machine learning technique that integrates clustering and disease progression modeling. We applied SuStaIn to the cross-sectional regional brain volumes of 25 patients with CBS, 39 patients with typical PSP-RS, and 50 healthy controls to estimate the two disease subtypes and trajectories of CBS and PSP-RS, which have distinct atrophy patterns. The progression model and classification accuracy of CBS and PSP-RS were compared with those of previous studies to evaluate the performance of SuStaIn. SuStaIn identified distinct temporal progression patterns of brain atrophy for CBS and PSP-RS, which were largely consistent with previous evidence, with high reproducibility (99.7%) under cross-validation. We classified these diseases with high accuracy (0.875) and sensitivity (0.680 and 1.000, respectively) based on cross-sectional structural brain MRI data; the accuracy was higher than that reported in previous studies. Moreover, SuStaIn stage correctly reflected disease severity without the label of disease stage, such as disease duration. Furthermore, SuStaIn also showed the genialized performance of differentiation and reflection for CBS and PSP-RS. Thus, SuStaIn has potential for improving our understanding of disease mechanisms, accurately stratifying patients, and providing prognoses for patients with CBS and PSP-RS.
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Affiliation(s)
- Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- *Correspondence: Koji Kamagata
| | - Peter A. Wijeratne
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Keigo Shimoji
- Department of Radiology, Tokyo Metropolitan Geriatric Hospital and Institute of Gerontology, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Yoshitaka Masutani
- Department of Biomedical Information Sciences, Hiroshima City University Graduate School of Information Sciences, Hiroshima, Japan
| | - Daniel C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
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33
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Scotton WJ, Bocchetta M, Todd E, Cash DM, Oxtoby N, VandeVrede L, Heuer H, Alexander DC, Rowe JB, Morris HR, Boxer A, Rohrer JD, Wijeratne PA. A data-driven model of brain volume changes in progressive supranuclear palsy. Brain Commun 2022; 4:fcac098. [PMID: 35602649 PMCID: PMC9118104 DOI: 10.1093/braincomms/fcac098] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/08/2021] [Accepted: 04/11/2022] [Indexed: 11/13/2022] Open
Abstract
The most common clinical phenotype of progressive supranuclear palsy is Richardson syndrome, characterized by levodopa unresponsive symmetric parkinsonism, with a vertical supranuclear gaze palsy, early falls and cognitive impairment. There is currently no detailed understanding of the full sequence of disease pathophysiology in progressive supranuclear palsy. Determining the sequence of brain atrophy in progressive supranuclear palsy could provide important insights into the mechanisms of disease progression, as well as guide patient stratification and monitoring for clinical trials. We used a probabilistic event-based model applied to cross-sectional structural MRI scans in a large international cohort, to determine the sequence of brain atrophy in clinically diagnosed progressive supranuclear palsy Richardson syndrome. A total of 341 people with Richardson syndrome (of whom 255 had 12-month follow-up imaging) and 260 controls were included in the study. We used a combination of 12-month follow-up MRI scans, and a validated clinical rating score (progressive supranuclear palsy rating scale) to demonstrate the longitudinal consistency and utility of the event-based model's staging system. The event-based model estimated that the earliest atrophy occurs in the brainstem and subcortical regions followed by progression caudally into the superior cerebellar peduncle and deep cerebellar nuclei, and rostrally to the cortex. The sequence of cortical atrophy progresses in an anterior to posterior direction, beginning in the insula and then the frontal lobe before spreading to the temporal, parietal and finally the occipital lobe. This in vivo ordering accords with the post-mortem neuropathological staging of progressive supranuclear palsy and was robust under cross-validation. Using longitudinal information from 12-month follow-up scans, we demonstrate that subjects consistently move to later stages over this time interval, supporting the validity of the model. In addition, both clinical severity (progressive supranuclear palsy rating scale) and disease duration were significantly correlated with the predicted subject event-based model stage (P < 0.01). Our results provide new insights into the sequence of atrophy progression in progressive supranuclear palsy and offer potential utility to stratify people with this disease on entry into clinical trials based on disease stage, as well as track disease progression.
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Affiliation(s)
- W. J. Scotton
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
- Correspondence to: William J. Scotton UCL Institute of Neurology
Department of Neurodegeneration Dementia Research Centre First Floor, 8-11 Queen Square,
WC1N 3AR London, UK E-mail:
| | - M. Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
| | - E. Todd
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
| | - D. M. Cash
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen
Square Institute of Neurology, University College London, London, UK
| | - N. Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University
College London, London, UK
| | - L. VandeVrede
- Department of Neurology, Memory and Aging Center, University of
California, San Francisco, CA, USA
| | - H. Heuer
- Department of Neurology, Memory and Aging Center, University of
California, San Francisco, CA, USA
| | | | - D. C. Alexander
- Centre for Medical Image Computing, Department of Computer Science, University
College London, London, UK
| | - J. B. Rowe
- Department of Clinical Neurosciences, Cambridge University, Cambridge
University Hospitals NHS Trust, Cambridge, UK
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge
University, 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. Boxer
- Department of Neurology, Memory and Aging Center, University of
California, San Francisco, CA, USA
| | - J. D. Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL 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
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Verdi S, Marquand AF, Schott JM, Cole JH. Beyond the average patient: how neuroimaging models can address heterogeneity in dementia. Brain 2021; 144:2946-2953. [PMID: 33892488 PMCID: PMC8634113 DOI: 10.1093/brain/awab165] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 02/24/2021] [Accepted: 04/08/2021] [Indexed: 11/25/2022] Open
Abstract
Dementia is a highly heterogeneous condition, with pronounced individual differences in age of onset, clinical presentation, progression rates and neuropathological hallmarks, even within a specific diagnostic group. However, the most common statistical designs used in dementia research studies and clinical trials overlook this heterogeneity, instead relying on comparisons of group average differences (e.g. patient versus control or treatment versus placebo), implicitly assuming within-group homogeneity. This one-size-fits-all approach potentially limits our understanding of dementia aetiology, hindering the identification of effective treatments. Neuroimaging has enabled the characterization of the average neuroanatomical substrates of dementias; however, the increasing availability of large open neuroimaging datasets provides the opportunity to examine patterns of neuroanatomical variability in individual patients. In this update, we outline the causes and consequences of heterogeneity in dementia and discuss recent research that aims to tackle heterogeneity directly, rather than assuming that dementia affects everyone in the same way. We introduce spatial normative modelling as an emerging data-driven technique, which can be applied to dementia data to model neuroanatomical variation, capturing individualized neurobiological 'fingerprints'. Such methods have the potential to detect clinically relevant subtypes, track an individual's disease progression or evaluate treatment responses, with the goal of moving towards precision medicine for dementia.
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Affiliation(s)
- Serena Verdi
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, 6525EN, The Netherlands
- Department of Cognitive Neuroscience, Radboud University Medical Centre, Nijmegen, 6525EN, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - James H Cole
- Centre for Medical Image Computing, Medical Physics and Biomedical Engineering, University College London, London WC1V 6LJ, UK
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
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Ravi D, Blumberg SB, Ingala S, Barkhof F, Alexander DC, Oxtoby NP. Degenerative adversarial neuroimage nets for brain scan simulations: Application in ageing and dementia. Med Image Anal 2021; 75:102257. [PMID: 34731771 PMCID: PMC8907865 DOI: 10.1016/j.media.2021.102257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 06/23/2021] [Accepted: 09/27/2021] [Indexed: 11/21/2022]
Abstract
We implemented a new deep learning framework capable of synthesising realistic and accurate 4D brain MRI in ageing and Alzheimer’s disease. We proposed a sequence of memory-efficient techniques designed to improve model stability, reduce artefacts, and improve individualization. Synthesised T1w MRI scans contain only minor structural differences with real data, and have minimal noise/texture artefacts. Synthesised MRI scans were diagnostically indistinguishable from real scans. Synthetic MRI can be used for: i) data augmentation, ii) model validation and iii) understanding biological/disease mechanisms in the brain.
Accurate and realistic simulation of high-dimensional medical images has become an important research area relevant to many AI-enabled healthcare applications. However, current state-of-the-art approaches lack the ability to produce satisfactory high-resolution and accurate subject-specific images. In this work, we present a deep learning framework, namely 4D-Degenerative Adversarial NeuroImage Net (4D-DANI-Net), to generate high-resolution, longitudinal MRI scans that mimic subject-specific neurodegeneration in ageing and dementia. 4D-DANI-Net is a modular framework based on adversarial training and a set of novel spatiotemporal, biologically-informed constraints. To ensure efficient training and overcome memory limitations affecting such high-dimensional problems, we rely on three key technological advances: i) a new 3D training consistency mechanism called Profile Weight Functions (PWFs), ii) a 3D super-resolution module and iii) a transfer learning strategy to fine-tune the system for a given individual. To evaluate our approach, we trained the framework on 9852 T1-weighted MRI scans from 876 participants in the Alzheimer’s Disease Neuroimaging Initiative dataset and held out a separate test set of 1283 MRI scans from 170 participants for quantitative and qualitative assessment of the personalised time series of synthetic images. We performed three evaluations: i) image quality assessment; ii) quantifying the accuracy of regional brain volumes over and above benchmark models; and iii) quantifying visual perception of the synthetic images by medical experts. Overall, both quantitative and qualitative results show that 4D-DANI-Net produces realistic, low-artefact, personalised time series of synthetic T1 MRI that outperforms benchmark models.
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Affiliation(s)
- Daniele Ravi
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK.
| | - Stefano B Blumberg
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK
| | - Silvia Ingala
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, the Netherlands; Insititutes of Neurology and Healthcare Engineering, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK
| | - Neil P Oxtoby
- Centre for Medical Image Computing (CMIC), Department of Computer Science, University College London, UK
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Wijeratne PA, Garbarino S, Gregory S, Johnson EB, Scahill RI, Paulsen JS, Tabrizi SJ, Lorenzi M, Alexander DC. Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease. Neurol Genet 2021; 7:e617. [PMID: 34660889 PMCID: PMC8515202 DOI: 10.1212/nxg.0000000000000617] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/06/2021] [Indexed: 01/18/2023]
Abstract
BACKGROUND AND OBJECTIVES Longitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD). METHODS We use data from the 2 largest cohort imaging studies in HD-284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)-to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control. RESULTS Our model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years). DISCUSSION Our findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.
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Affiliation(s)
- Peter A. Wijeratne
- From the Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d’Azur (M.L.), Inria, Epione Research Project, Valbonne, France
| | - Sara Garbarino
- From the Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d’Azur (M.L.), Inria, Epione Research Project, Valbonne, France
| | - Sarah Gregory
- From the Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d’Azur (M.L.), Inria, Epione Research Project, Valbonne, France
| | - Eileanoir B. Johnson
- From the Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d’Azur (M.L.), Inria, Epione Research Project, Valbonne, France
| | - Rachael I. Scahill
- From the Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d’Azur (M.L.), Inria, Epione Research Project, Valbonne, France
| | - Jane S. Paulsen
- From the Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d’Azur (M.L.), Inria, Epione Research Project, Valbonne, France
| | - Sarah J. Tabrizi
- From the Centre for Medical Image Computing (P.A.W., D.C.A.), Department of Computer Science, University College London, Gower Street; Huntington's Disease Research Centre (P.A.W., S. Gregory, E.B.J., R.I.S., S.J.T.), Department of Neurodegenerative Disease, University College London, Queen Square Institute of Neurology, London, United Kingdom; Dipartimento di Matematica (S. Garbarino), UNIGE, DIMA, Genova, Italy; Departments of Neurology and Psychiatry (J.S.P.), Carver College of Medicine, University of Iowa; and Université Côte d’Azur (M.L.), Inria, Epione Research Project, Valbonne, France
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Zhang L, Yang C, Li Y, Niu S, Liang X, Zhang Z, Luo Q, Luo H. Dynamic Changes in the Levels of Amyloid-β 42 Species in the Brain and Periphery of APP/PS1 Mice and Their Significance for Alzheimer's Disease. Front Mol Neurosci 2021; 14:723317. [PMID: 34512259 PMCID: PMC8430227 DOI: 10.3389/fnmol.2021.723317] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Accepted: 08/09/2021] [Indexed: 11/13/2022] Open
Abstract
Although amyloid-β42 (Aβ42) has been used as one of the core biomarkers for Alzheimer’s disease (AD) diagnosis, the dynamic changes of its different forms in the brain, blood, and even intestines and its correlation with the progression of AD disease remain obscure. Herein, we screened Aβ42-specific preferred antibody pairs 1F12/1F12 and 1F12/2C6 to accurately detect Aβ42 types using sandwich ELISA, including total Aβ42, Aβ42 oligomers (Aβ42Os), and Aβ42 monomers (Aβ42Ms). The levels of Aβ42 species in the brain, blood, and intestines of different aged APP/PS1 mice were quantified to study their correlation with AD progression. Total Aβ42 levels in the blood were not correlated with AD progression, but Aβ42Ms level in the blood of 9-month-old APP/PS1 mice was significantly reduced, and Aβ42Os level in the brain was significantly elevated compared to 3-month-old APP/PS1, demonstrating that the levels of Aβ42Ms and Aβ42Os in the blood and brain were correlated with AD progression. Interestingly, in 9-month-old APP/PS1 mice, the level of Aβ42 in the intestine was higher than that in 3-month-old APP/PS1 mice, indicating that the increased level of Aβ42 in the gastrointestinal organs may also be related to the progression of AD. Meanwhile, changes in the gut microbiota composition of APP/PS1 mice with age were also observed. Therefore, the increase in Aβ derived from intestinal tissues and changes in microbiome composition can be used as a potential early diagnosis tool for AD, and further used as an indicator of drug intervention to reduce brain amyloid.
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Affiliation(s)
- Liding Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Changwen Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yanqing Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shiqi Niu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaohan Liang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Zhihong Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,School of Biomedical Engineering, Hainan University, Haikou, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,School of Biomedical Engineering, Hainan University, Haikou, China
| | - Haiming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
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38
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Howlett J, Hill SM, Ritchie CW, Tom BDM. Disease Modelling of Cognitive Outcomes and Biomarkers in the European Prevention of Alzheimer's Dementia Longitudinal Cohort. Front Big Data 2021; 4:676168. [PMID: 34490422 PMCID: PMC8417903 DOI: 10.3389/fdata.2021.676168] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 07/30/2021] [Indexed: 12/04/2022] Open
Abstract
A key challenge for the secondary prevention of Alzheimer’s dementia is the need to identify individuals early on in the disease process through sensitive cognitive tests and biomarkers. The European Prevention of Alzheimer’s Dementia (EPAD) consortium recruited participants into a longitudinal cohort study with the aim of building a readiness cohort for a proof-of-concept clinical trial and also to generate a rich longitudinal data-set for disease modelling. Data have been collected on a wide range of measurements including cognitive outcomes, neuroimaging, cerebrospinal fluid biomarkers, genetics and other clinical and environmental risk factors, and are available for 1,828 eligible participants at baseline, 1,567 at 6 months, 1,188 at one-year follow-up, 383 at 2 years, and 89 participants at three-year follow-up visit. We novelly apply state-of-the-art longitudinal modelling and risk stratification approaches to these data in order to characterise disease progression and biological heterogeneity within the cohort. Specifically, we use longitudinal class-specific mixed effects models to characterise the different clinical disease trajectories and a semi-supervised Bayesian clustering approach to explore whether participants can be stratified into homogeneous subgroups that have different patterns of cognitive functioning evolution, while also having subgroup-specific profiles in terms of baseline biomarkers and longitudinal rate of change in biomarkers.
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Affiliation(s)
- James Howlett
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Steven M Hill
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
| | - Craig W Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Brian D M Tom
- MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom
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Bejanin A, Iulita MF, Vilaplana E, Carmona-Iragui M, Benejam B, Videla L, Barroeta I, Fernandez S, Altuna M, Pegueroles J, Montal V, Valldeneu S, Giménez S, González-Ortiz S, Muñoz L, Padilla C, Aranha MR, Estellés T, Illán-Gala I, Belbin O, Camacho V, Wilson LR, Annus T, Osorio RS, Videla S, Lehmann S, Holland AJ, Zetterberg H, Blennow K, Alcolea D, Clarimon J, Zaman SH, Blesa R, Lleó A, Fortea J. Association of Apolipoprotein E ɛ4 Allele With Clinical and Multimodal Biomarker Changes of Alzheimer Disease in Adults With Down Syndrome. JAMA Neurol 2021; 78:937-947. [PMID: 34228042 PMCID: PMC8261691 DOI: 10.1001/jamaneurol.2021.1893] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Importance Alzheimer disease (AD) is the leading cause of death in individuals with Down syndrome (DS). Previous studies have suggested that the APOE ɛ4 allele plays a role in the risk and age at onset of dementia in DS; however, data on in vivo biomarkers remain scarce. Objective To investigate the association of the APOE ɛ4 allele with clinical and multimodal biomarkers of AD in adults with DS. Design, Setting, and Participants This dual-center cohort study recruited adults with DS in Barcelona, Spain, and in Cambridge, UK, between June 1, 2009, and February 28, 2020. Included individuals had been genotyped for APOE and had at least 1 clinical or AD biomarker measurement; 2 individuals were excluded because of the absence of trisomy 21. Participants were either APOE ɛ4 allele carriers or noncarriers. Main Outcomes and Measures Participants underwent a neurological and neuropsychological assessment. A subset of participants had biomarker measurements: Aβ1-42, Aβ1-40, phosphorylated tau 181 (pTau181) and neurofilament light chain (NfL) in cerebrospinal fluid (CSF), pTau181, and NfL in plasma; amyloid positron emission tomography (PET); fluorine 18-labeled-fluorodeoxyglucose PET; and/or magnetic resonance imaging. Age at symptom onset was compared between APOE ɛ4 allele carriers and noncarriers, and within-group local regression models were used to compare the association of biomarkers with age. Voxelwise analyses were performed to assess topographical differences in gray matter metabolism and volume. Results Of the 464 adults with DS included in the study, 97 (20.9%) were APOE ɛ4 allele carriers and 367 (79.1%) were noncarriers. No differences between the 2 groups were found by age (median [interquartile range], 45.9 [36.4-50.2] years vs 43.7 [34.9-50.2] years; P = .56) or sex (51 male carriers [52.6%] vs 199 male noncarriers [54.2%]). APOE ɛ4 allele carriers compared with noncarriers presented with AD symptoms at a younger age (mean [SD] age, 50.7 [4.4] years vs 52.7 [5.8] years; P = .02) and showed earlier cognitive decline. Locally estimated scatterplot smoothing curves further showed between-group differences in biomarker trajectories with age as reflected by nonoverlapping CIs. Specifically, carriers showed lower levels of the CSF Aβ1-42 to Aβ1-40 ratio until age 40 years, earlier increases in amyloid PET and plasma pTau181, and earlier loss of cortical metabolism and hippocampal volume. No differences were found in NfL biomarkers or CSF total tau and pTau181. Voxelwise analyses showed lower metabolism in subcortical and parieto-occipital structures and lower medial temporal volume in APOE ɛ4 allele carriers. Conclusions and Relevance In this study, the APOE ɛ4 allele was associated with earlier clinical and biomarker changes of AD in DS. These results provide insights into the mechanisms by which APOE increases the risk of AD, emphasizing the importance of APOE genotype for future clinical trials in DS.
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Affiliation(s)
- Alexandre Bejanin
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Maria Florencia Iulita
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Eduard Vilaplana
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Maria Carmona-Iragui
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain.,Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain
| | - Bessy Benejam
- Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain
| | - Laura Videla
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain.,Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain
| | - Isabel Barroeta
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Susana Fernandez
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Miren Altuna
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Jordi Pegueroles
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Victor Montal
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Silvia Valldeneu
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Sandra Giménez
- Multidisciplinary Sleep Unit, Respiratory Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | | | - Laia Muñoz
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Concepción Padilla
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Mateus Rozalem Aranha
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Teresa Estellés
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Ignacio Illán-Gala
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Olivia Belbin
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Valle Camacho
- Nuclear Medicine Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Liam Reese Wilson
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Douglas House, Cambridge, United Kingdom
| | - Tiina Annus
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Douglas House, Cambridge, United Kingdom
| | - Ricardo S Osorio
- Healthy Brain Aging and Sleep Center, Department of Psychiatry, New York University Grossman School of Medicine, New York
| | - Sebastián Videla
- Clinical Research Support Unit, Bellvitge Biomedical Research Institute Department of Clinical Pharmacology, University of Barcelona, Barcelona, Spain
| | - Sylvain Lehmann
- Le Laboratoire de Biochimie et Protéomique Clinique, Université Montpellier, Centre Hospitalier Universitaire Montpellier, Institut National de la Santé et de la Recherche Médicale, Montpellier, France
| | - Anthony J Holland
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Douglas House, Cambridge, United Kingdom
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mönldal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,UK Dementia Research Institute at University College London (UCL), London, United Kingdom.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mönldal, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Daniel Alcolea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Jordi Clarimon
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Shahid H Zaman
- Cambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Douglas House, Cambridge, United Kingdom.,Cambridgeshire and Peterborough National Health Service (NHS) Foundation Trust, Fulbourn Hospital, Elizabeth House, Cambridge, United Kingdom
| | - Rafael Blesa
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Alberto Lleó
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain
| | - Juan Fortea
- Sant Pau Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain.,Center of Biomedical Investigation Network for Neurodegenerative Diseases (CIBERNED), Madrid, Spain.,Barcelona Down Medical Center, Fundació Catalana Síndrome de Down, Barcelona, Spain
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Dong Z, Gu H, Guo Q, Liang S, Xue J, Yao F, Liu X, Li F, Liu H, Sun L, Zhao K. Profiling of Serum Exosome MiRNA Reveals the Potential of a MiRNA Panel as Diagnostic Biomarker for Alzheimer's Disease. Mol Neurobiol 2021; 58:3084-3094. [PMID: 33629272 DOI: 10.1007/s12035-021-02323-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Accepted: 02/05/2021] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease in the older adults. Although much effort has been made in the analyses of diagnostic biomarkers, such as amyloid-β, tau, and neurofilament light chain, identifying peripheral blood-based biomarkers is in extremely urgent need for their minimal invasiveness and more convenience. Here we characterized the miRNA profile by RNA sequencing in human serum exosomes from AD patients and healthy controls (HC) to investigate its potential for AD diagnosis. Subsequently, Gene Ontology analysis and pathway analysis were performed for the targeted genes from the differentially expressed miRNAs. These basic functions were differentially enriched, including cell adhesion, regulation of transcription, and the ubiquitin system. Functional network analysis highlighted the pathways of proteoglycans in cancer, viral carcinogenesis, signaling pathways regulating pluripotency of stem cells, and cellular senescence in AD. A total of 24 miRNAs showed significantly differential expression between AD and HC with more than ± 2.0-fold change at p value < 0.05 and at least 50 reads for each sample. Logistic regression analysis established a model for AD prediction by serum exosomal miR-30b-5p, miR-22-3p, and miR-378a-3p. Sequencing results were validated using quantitative reverse transcription PCR. The data showed that miR-30b-5p, miR-22-3p, and miR-378a-3p were significantly deregulated in AD, with area under the curve (AUC) of 0.668, 0.637, and 0.718, respectively. The combination of the three miRs gained a better diagnostic capability with AUC of 0.880. This finding revealed a miR panel as potential biomarker in the peripheral blood to distinguish AD from HC.
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Affiliation(s)
- Zhiwu Dong
- Department of Laboratory Medicine, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, 147 Jiankang Road, Jinshan District, Shanghai, 201599, People's Republic of China.
| | - Hongjun Gu
- Shanghai Jinshan Zhongren Aged Care Hospital, Shanghai, 201501, China
| | - Qiang Guo
- Department of Ultrasound Medicine, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, Shanghai, 201599, China
| | - Shuang Liang
- Department of Laboratory Medicine, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, 147 Jiankang Road, Jinshan District, Shanghai, 201599, People's Republic of China
| | - Jian Xue
- Shanghai Jinshan Zhongren Aged Care Hospital, Shanghai, 201501, China
| | - Feng Yao
- Shanghai Jinshan Zhongren Aged Care Hospital, Shanghai, 201501, China
| | - Xianglu Liu
- Department of Laboratory Medicine, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, 147 Jiankang Road, Jinshan District, Shanghai, 201599, People's Republic of China
| | - Feifei Li
- Department of Laboratory Medicine, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, 147 Jiankang Road, Jinshan District, Shanghai, 201599, People's Republic of China
| | - Huiling Liu
- Department of Laboratory Medicine, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, 147 Jiankang Road, Jinshan District, Shanghai, 201599, People's Republic of China
| | - Li Sun
- Department of Laboratory Medicine, Jinshan Branch of Shanghai Sixth People's Hospital Affiliated to Shanghai Jiaotong University, 147 Jiankang Road, Jinshan District, Shanghai, 201599, People's Republic of China
| | - Kewen Zhao
- Department of Pathophysiology, Shanghai Jiaotong University School of Medicine, Shanghai, 200025, People's Republic of China
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Luckett PH, McCullough A, Gordon BA, Strain J, Flores S, Dincer A, McCarthy J, Kuffner T, Stern A, Meeker KL, Berman SB, Chhatwal JP, Cruchaga C, Fagan AM, Farlow MR, Fox NC, Jucker M, Levin J, Masters CL, Mori H, Noble JM, Salloway S, Schofield PR, Brickman AM, Brooks WS, Cash DM, Fulham MJ, Ghetti B, Jack CR, Vöglein J, Klunk W, Koeppe R, Oh H, Su Y, Weiner M, Wang Q, Swisher L, Marcus D, Koudelis D, Joseph-Mathurin N, Cash L, Hornbeck R, Xiong C, Perrin RJ, Karch CM, Hassenstab J, McDade E, Morris JC, Benzinger TLS, Bateman RJ, Ances BM. Modeling autosomal dominant Alzheimer's disease with machine learning. Alzheimers Dement 2021; 17:1005-1016. [PMID: 33480178 PMCID: PMC8195816 DOI: 10.1002/alz.12259] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Revised: 11/06/2020] [Accepted: 11/08/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers. DISCUSSION Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.
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Affiliation(s)
| | | | - Brian A Gordon
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Jeremy Strain
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Shaney Flores
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Aylin Dincer
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - John McCarthy
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Todd Kuffner
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ari Stern
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Karin L Meeker
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Jasmeer P Chhatwal
- Brigham and Women's Hospital, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Carlos Cruchaga
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Anne M Fagan
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Mathias Jucker
- German Center for Neurodegenerative Disease, Tübingen, Germany
| | - Johannes Levin
- Ludwig Maximilian University of Munich, Munich, Germany
- German Center for Neurodegenerative Diseases, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Colin L Masters
- Florey Institute, The University of Melbourne, Parkville, VIC, Australia
| | - Hiroshi Mori
- Osaka City University, Sumiyoshi Ward, Osaka, Japan
| | - James M Noble
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, G.H. Sergievsky Center and Department of Neurology, Columbia University Irving Medical Center, New York, New York, USA
| | | | - Peter R Schofield
- Neuroscience Research Australia, Randwick, NSW, Australia
- University of New South Wales, Sydney, NSW, Australia
| | | | - William S Brooks
- Neuroscience Research Australia, Randwick, NSW, Australia
- University of New South Wales, Sydney, NSW, Australia
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, London, UK
| | - Michael J Fulham
- Department of Molecular Imaging, Royal Prince Alfred Hospital, Missenden Road, Camperdown, NSW, Australia
- University of Sydney, Sydney, NSW, Australia
| | | | | | - Jonathan Vöglein
- German Center for Neurodegenerative Diseases, Munich, Germany
- Department of Neurology, Ludwig-Maximilians-Universität München, München, Germany
| | - William Klunk
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Hwamee Oh
- Brown University, Providence, Rhode Island, USA
| | - Yi Su
- Banner Alzheimer Institute, Phoenix, Arizona, USA
| | | | - Qing Wang
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Laura Swisher
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Dan Marcus
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | | | - Lisa Cash
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Russ Hornbeck
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - Chengjie Xiong
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Celeste M Karch
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | - Eric McDade
- Washington University in St. Louis, St. Louis, Missouri, USA
| | - John C Morris
- Washington University in St. Louis, St. Louis, Missouri, USA
| | | | | | - Beau M Ances
- Washington University in St. Louis, St. Louis, Missouri, USA
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Jung W, Jun E, Suk HI. Deep recurrent model for individualized prediction of Alzheimer's disease progression. Neuroimage 2021; 237:118143. [PMID: 33991694 DOI: 10.1016/j.neuroimage.2021.118143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 03/15/2021] [Accepted: 04/13/2021] [Indexed: 01/27/2023] Open
Abstract
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling. Under the same problem settings, in this work, we propose a novel computational framework that can predict the phenotypic measurements of MRI biomarkers and trajectories of clinical status along with cognitive scores at multiple future time points. However, in handling time series data, it generally faces many unexpected missing observations. In regard to such an unfavorable situation, we define a secondary problem of estimating those missing values and tackle it in a systematic way by taking account of temporal and multivariate relations inherent in time series data. Concretely, we propose a deep recurrent network that jointly tackles the four problems of (i) missing value imputation, (ii) phenotypic measurements forecasting, (iii) trajectory estimation of a cognitive score, and (iv) clinical status prediction of a subject based on his/her longitudinal imaging biomarkers. Notably, the learnable parameters of all the modules in our predictive models are trained in an end-to-end manner by taking the morphological features and cognitive scores as input, with our circumspectly defined loss function. In our experiments over The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge cohort, we measured performance for various metrics and compared our method to competing methods in the literature. Exhaustive analyses and ablation studies were also conducted to better confirm the effectiveness of our method.
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Affiliation(s)
- Wonsik Jung
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Eunji Jun
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea.
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43
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Abi Nader C, Ayache N, Frisoni GB, Robert P, Lorenzi M. Simulating the outcome of amyloid treatments in Alzheimer's disease from imaging and clinical data. Brain Commun 2021; 3:fcab091. [PMID: 34085040 PMCID: PMC8168944 DOI: 10.1093/braincomms/fcab091] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 01/25/2021] [Accepted: 02/23/2021] [Indexed: 11/14/2022] Open
Abstract
In this study, we investigate SimulAD, a novel quantitative instrument for the development of intervention strategies for disease-modifying drugs in Alzheimer's disease. SimulAD is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers' progression. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative the method enables to generate hypothetical scenarios of amyloid lowering interventions. The results quantify the crucial role of intervention time, and provide a theoretical justification for testing amyloid modifying drugs in the pre-clinical stage. Our experimental simulations are compatible with the outcomes observed in past clinical trials, and suggest that anti-amyloid treatments should be administered at least 7 years earlier than what is currently being done in order to obtain statistically powered improvement of clinical endpoints.
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Affiliation(s)
- Clément Abi Nader
- Université Côte d'Azur, INRIA Sophia Antipolis, EPIONE Research Project, 06902, Sophia-Antipolis, France
| | - Nicholas Ayache
- Université Côte d'Azur, INRIA Sophia Antipolis, EPIONE Research Project, 06902, Sophia-Antipolis, France
| | - Giovanni B Frisoni
- Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, Hospitals and University of Geneva, 1205, Geneva, Switzerland
| | - Philippe Robert
- Université Côte d'Azur, CoBTeK Lab, MNC3 Program, 06103, Nice, France
| | - Marco Lorenzi
- Université Côte d'Azur, INRIA Sophia Antipolis, EPIONE Research Project, 06902, Sophia-Antipolis, France
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44
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Johnson EB, Ziegler G, Penny W, Rees G, Tabrizi SJ, Scahill RI, Gregory S. Dynamics of Cortical Degeneration Over a Decade in Huntington's Disease. Biol Psychiatry 2021; 89:807-816. [PMID: 33500176 PMCID: PMC7986936 DOI: 10.1016/j.biopsych.2020.11.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 10/14/2020] [Accepted: 11/08/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Characterizing changing brain structure in neurodegeneration is fundamental to understanding long-term effects of pathology and ultimately providing therapeutic targets. It is well established that Huntington's disease (HD) gene carriers undergo progressive brain changes during the course of disease, yet the long-term trajectory of cortical atrophy is not well defined. Given that genetic therapies currently tested in HD are primarily expected to target the cortex, understanding atrophy across this region is essential. METHODS Capitalizing on a unique longitudinal dataset with a minimum of 3 and maximum of 7 brain scans from 49 HD gene carriers and 49 age-matched control subjects, we implemented a novel dynamical systems approach to infer patterns of regional neurodegeneration over 10 years. We use Bayesian hierarchical modeling to map participant- and group-level trajectories of atrophy spatially and temporally, additionally relating atrophy to the genetic marker of HD (CAG-repeat length) and motor and cognitive symptoms. RESULTS We show, for the first time, that neurodegenerative changes exhibit complex temporal dynamics with substantial regional variation around the point of clinical diagnosis. Although widespread group differences were seen across the cortex, the occipital and parietal regions undergo the greatest rate of cortical atrophy. We have established links between atrophy and genetic markers of HD while demonstrating that specific cortical changes predict decline in motor and cognitive performance. CONCLUSIONS HD gene carriers display regional variability in the spatial pattern of cortical atrophy, which relates to genetic factors and motor and cognitive symptoms. Our findings indicate a complex pattern of neuronal loss, which enables greater characterization of HD progression.
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Affiliation(s)
- Eileanoir B Johnson
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom.
| | - Gabriel Ziegler
- Institute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; German Center for Neurodegenerative Diseases, Magdeburg, Germany.
| | - William Penny
- School of Psychology, University of East Anglia, Norwich, United Kingdom
| | - Geraint Rees
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah J Tabrizi
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Dementia Research Institute at University College London, London, United Kingdom
| | - Rachael I Scahill
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Sarah Gregory
- Huntington's Disease Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom
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Oxtoby NP, Leyland LA, Aksman LM, Thomas GEC, Bunting EL, Wijeratne PA, Young AL, Zarkali A, Tan MMX, Bremner FD, Keane PA, Morris HR, Schrag AE, Alexander DC, Weil RS. Sequence of clinical and neurodegeneration events in Parkinson's disease progression. Brain 2021; 144:975-988. [PMID: 33543247 PMCID: PMC8041043 DOI: 10.1093/brain/awaa461] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 10/05/2020] [Accepted: 10/24/2020] [Indexed: 02/07/2023] Open
Abstract
Dementia is one of the most debilitating aspects of Parkinson's disease. There are no validated biomarkers that can track Parkinson's disease progression, nor accurately identify patients who will develop dementia and when. Understanding the sequence of observable changes in Parkinson's disease in people at elevated risk for developing dementia could provide an integrated biomarker for identifying and managing individuals who will develop Parkinson's dementia. We aimed to estimate the sequence of clinical and neurodegeneration events, and variability in this sequence, using data-driven statistical modelling in two separate Parkinson's cohorts, focusing on patients at elevated risk for dementia due to their age at symptom onset. We updated a novel version of an event-based model that has only recently been extended to cope naturally with clinical data, enabling its application in Parkinson's disease for the first time. The observational cohorts included healthy control subjects and patients with Parkinson's disease, of whom those diagnosed at age 65 or older were classified as having high risk of dementia. The model estimates that Parkinson's progression in patients at elevated risk for dementia starts with classic prodromal features of Parkinson's disease (olfaction, sleep), followed by early deficits in visual cognition and increased brain iron content, followed later by a less certain ordering of neurodegeneration in the substantia nigra and cortex, neuropsychological cognitive deficits, retinal thinning in dopamine layers, and further deficits in visual cognition. Importantly, we also characterize variation in the sequence. We found consistent, cross-validated results within cohorts, and agreement between cohorts on the subset of features available in both cohorts. Our sequencing results add powerful support to the increasing body of evidence suggesting that visual processing specifically is affected early in patients with Parkinson's disease at elevated risk of dementia. This opens a route to earlier and more precise detection, as well as a more detailed understanding of the pathological mechanisms underpinning Parkinson's dementia.
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Affiliation(s)
- Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | | | - Leon M Aksman
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - George E C Thomas
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Emma L Bunting
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Peter A Wijeratne
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Alexandra L Young
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Angelika Zarkali
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Manuela M X Tan
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Fion D Bremner
- Neuro-ophthalmology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
| | - Pearse A Keane
- Institute of Ophthalmology, UCL, London, UK
- Moorfields Eye Hospital, London, UK
| | - Huw R Morris
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Anette E Schrag
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science and Department of Medical Physics and Biomedical Engineering, UCL, London, UK
| | - Rimona S Weil
- Dementia Research Centre, UCL Institute of Neurology, UCL, London, UK
- Movement Disorders Consortium, UCL, London, UK
- The Wellcome Centre for Human Neuroimaging, UCL Institute of Neurology, UCL, London, UK
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Buckley RF. Recent Advances in Imaging of Preclinical, Sporadic, and Autosomal Dominant Alzheimer's Disease. Neurotherapeutics 2021; 18:709-727. [PMID: 33782864 PMCID: PMC8423933 DOI: 10.1007/s13311-021-01026-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/25/2022] Open
Abstract
Observing Alzheimer's disease (AD) pathological changes in vivo with neuroimaging provides invaluable opportunities to understand and predict the course of disease. Neuroimaging AD biomarkers also allow for real-time tracking of disease-modifying treatment in clinical trials. With recent neuroimaging advances, along with the burgeoning availability of longitudinal neuroimaging data and big-data harmonization approaches, a more comprehensive evaluation of the disease has shed light on the topographical staging and temporal sequencing of the disease. Multimodal imaging approaches have also promoted the development of data-driven models of AD-associated pathological propagation of tau proteinopathies. Studies of autosomal dominant, early sporadic, and late sporadic courses of the disease have shed unique insights into the AD pathological cascade, particularly with regard to genetic vulnerabilities and the identification of potential drug targets. Further, neuroimaging markers of b-amyloid, tau, and neurodegeneration have provided a powerful tool for validation of novel fluid cerebrospinal and plasma markers. This review highlights some of the latest advances in the field of human neuroimaging in AD across these topics, particularly with respect to positron emission tomography and structural and functional magnetic resonance imaging.
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Affiliation(s)
- Rachel F Buckley
- Department of Neurology, Massachusetts General Hospital & Brigham and Women's, Harvard Medical School, Boston, MA, USA.
- Melbourne School of Psychological Sciences and Florey Institutes, University of Melbourne, Melbourne, VIC, Australia.
- Department of Neurology, Massachusetts General Hospital, 149 13th St, Charlestown, MA, 02129, USA.
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Liu N, Xu J, Liu H, Zhang S, Li M, Zhou Y, Qin W, Li MJ, Yu C. Hippocampal transcriptome-wide association study and neurobiological pathway analysis for Alzheimer's disease. PLoS Genet 2021; 17:e1009363. [PMID: 33630843 PMCID: PMC7906391 DOI: 10.1371/journal.pgen.1009363] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 01/12/2021] [Indexed: 01/22/2023] Open
Abstract
Genome-wide association studies (GWASs) have identified multiple susceptibility loci for Alzheimer’s disease (AD), which is characterized by early and progressive damage to the hippocampus. However, the association of hippocampal gene expression with AD and the underlying neurobiological pathways remain largely unknown. Based on the genomic and transcriptomic data of 111 hippocampal samples and the summary data of two large-scale meta-analyses of GWASs, a transcriptome-wide association study (TWAS) was performed to identify genes with significant associations between hippocampal expression and AD. We identified 54 significantly associated genes using an AD-GWAS meta-analysis of 455,258 individuals; 36 of the genes were confirmed in another AD-GWAS meta-analysis of 63,926 individuals. Fine-mapping models further prioritized 24 AD-related genes whose effects on AD were mediated by hippocampal expression, including APOE and two novel genes (PTPN9 and PCDHA4). These genes are functionally related to amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. By integrating the predicted hippocampal expression and neuroimaging data, we found that the hippocampal expression of QPCTL and ERCC2 showed significant difference between AD patients and cognitively normal elderly individuals as well as correlated with hippocampal volume. Mediation analysis further demonstrated that hippocampal volume mediated the effect of hippocampal gene expression (QPCTL and ERCC2) on AD. This study identifies two novel genes associated with AD by integrating hippocampal gene expression and genome-wide association data and reveals candidate hippocampus-mediated neurobiological pathways from gene expression to AD. The hippocampus is a potential neuroimaging endophenotype for Alzheimer’s disease (AD). This study identifies genes whose expression in hippocampal tissue is associated with AD and establishes the pathways from hippocampal gene expression to hippocampal volume to AD. We demonstrate that 24 genes are associated with AD in hippocampal tissue, and these genes are enriched for AD-related biological processes of amyloid-beta formation, phosphorylation/dephosphorylation, neuronal apoptosis, neurogenesis and telomerase-related processes. We further show that hippocampal volume mediates the effects of the hippocampal gene expression of QPCTL and ERCC2 on AD. These findings improve our understanding of the genetic and neural mechanisms of AD.
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Affiliation(s)
- Nana Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Huaigui Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shijie Zhang
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Department of Pharmacology, Tianjin Medical University, Tianjin, China
| | - Miaoxin Li
- Department of Medical Genetics, Center for Genome Research, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Centre for Genomic Sciences, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Department of Psychiatry, The University of Hong Kong, Hong Kong Special Administrative Region, China
- Centre for Reproduction, Development and Growth, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Yao Zhou
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Department of Pharmacology, Tianjin Medical University, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mulin Jun Li
- The Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Department of Pharmacology, Tianjin Medical University, Tianjin, China
- * E-mail: (MJL); (CY)
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Chinese Academy of Sciences (CAS) Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- * E-mail: (MJL); (CY)
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48
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Zhou Y, Tinaz S, Tagare HD. Robust Bayesian Analysis of Early-Stage Parkinson's Disease Progression Using DaTscan Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:549-561. [PMID: 33055025 PMCID: PMC7899013 DOI: 10.1109/tmi.2020.3031478] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper proposes a mixture of linear dynamical systems model for quantifying the heterogeneous progress of Parkinson's disease from DaTscan Images. The model is fitted to longitudinal DaTscans from the Parkinson's Progression Marker Initiative. Fitting is accomplished using robust Bayesian inference with collapsed Gibbs sampling. Bayesian inference reveals three image-based progression subtypes which differ in progression speeds as well as progression trajectories. The model reveals characteristic spatial progression patterns in the brain, each pattern associated with a time constant. These patterns can serve as disease progression markers. The subtypes also have different progression rates of clinical symptoms measured by MDS-UPDRS Part III scores.
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49
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Weigand AJ, Thomas KR, Bangen KJ, Eglit GML, Delano-Wood L, Gilbert PE, Brickman AM, Bondi MW. APOE interacts with tau PET to influence memory independently of amyloid PET in older adults without dementia. Alzheimers Dement 2021; 17:61-69. [PMID: 32886451 DOI: 10.1002/alz.12173] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Revised: 07/11/2020] [Accepted: 07/14/2020] [Indexed: 02/03/2023]
Abstract
INTRODUCTION Apolipoprotein E (APOE) interacts with Alzheimer's disease pathology to promote disease progression. We investigated the moderating effect of APOE on independent associations of amyloid and tau positron emission tomography (PET) with cognition. METHODS For 297 nondemented older adults from the Alzheimer's Disease Neuroimaging Initiative, regression equations modeled associations between cognition and (1) cortical amyloid beta (Aβ) PET levels adjusting for tau and (2) medial temporal lobe (MTL) tau PET levels adjusting for Aβ, including interactions with APOE ε4-carrier status. RESULTS Adjusting for tau PET, Aβ was not associated with cognition and did not interact with APOE. In contrast, adjusting for Aβ PET, MTL tau was associated with all cognitive domains. Further, there was a stronger moderating effect of APOE on MTL tau and memory associations in ε4-carriers, even among Aβ-negative individuals. DISCUSSION Findings suggest that APOE may interact with tau independently of Aβ and that elevated MTL tau confers negative cognitive consequences in Aβ-negative ε4 carriers.
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Affiliation(s)
- Alexandra J Weigand
- San Diego State University/University of California, San Diego Joint Doctoral Program, San Diego
| | - Kelsey R Thomas
- Veterans Affairs San Diego Healthcare System, San Diego, California, USA
- Department of Psychiatry, University of California, San Diego, California, USA
| | - Katherine J Bangen
- Veterans Affairs San Diego Healthcare System, San Diego, California, USA
- Department of Psychiatry, University of California, San Diego, California, USA
| | - Graham M L Eglit
- Veterans Affairs San Diego Healthcare System, San Diego, California, USA
| | - Lisa Delano-Wood
- Veterans Affairs San Diego Healthcare System, San Diego, California, USA
- Department of Psychiatry, University of California, San Diego, California, USA
| | - Paul E Gilbert
- Department of Psychology, San Diego State University, California, USA
| | - Adam M Brickman
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Mark W Bondi
- Veterans Affairs San Diego Healthcare System, San Diego, California, USA
- Department of Psychiatry, University of California, San Diego, California, USA
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50
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Dekker I, Schoonheim MM, Venkatraghavan V, Eijlers AJC, Brouwer I, Bron EE, Klein S, Wattjes MP, Wink AM, Geurts JJG, Uitdehaag BMJ, Oxtoby NP, Alexander DC, Vrenken H, Killestein J, Barkhof F, Wottschel V. The sequence of structural, functional and cognitive changes in multiple sclerosis. Neuroimage Clin 2020; 29:102550. [PMID: 33418173 PMCID: PMC7804841 DOI: 10.1016/j.nicl.2020.102550] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/09/2020] [Accepted: 12/20/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND As disease progression remains poorly understood in multiple sclerosis (MS), we aim to investigate the sequence in which different disease milestones occur using a novel data-driven approach. METHODS We analysed a cohort of 295 relapse-onset MS patients and 96 healthy controls, and considered 28 features, capturing information on T2-lesion load, regional brain and spinal cord volumes, resting-state functional centrality ("hubness"), microstructural tissue integrity of major white matter (WM) tracts and performance on multiple cognitive tests. We used a discriminative event-based model to estimate the sequence of biomarker abnormality in MS progression in general, as well as specific models for worsening physical disability and cognitive impairment. RESULTS We demonstrated that grey matter (GM) atrophy of the cerebellum, thalamus, and changes in corticospinal tracts are early events in MS pathology, whereas other WM tracts as well as the cognitive domains of working memory, attention, and executive function are consistently late events. The models for disability and cognition show early functional changes of the default-mode network and earlier changes in spinal cord volume compared to the general MS population. Overall, GM atrophy seems crucial due to its early involvement in the disease course, whereas WM tract integrity appears to be affected relatively late despite the early onset of WM lesions. CONCLUSION Data-driven modelling revealed the relative occurrence of both imaging and non-imaging events as MS progresses, providing insights into disease propagation mechanisms, and allowing fine-grained staging of patients for monitoring purposes.
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Affiliation(s)
- Iris Dekker
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands; Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Menno M Schoonheim
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Vikram Venkatraghavan
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Anand J C Eijlers
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Iman Brouwer
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Esther E Bron
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology & Nuclear Medicine, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Mike P Wattjes
- Dept. of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Alle Meije Wink
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Jeroen J G Geurts
- Anatomy and Neurosciences, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Bernard M J Uitdehaag
- Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK
| | - Hugo Vrenken
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Joep Killestein
- Neurology, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands; Centre for Medical Image Computing, Department of Computer Science, UCL, London, UK; Institute of Neurology, UCL, London, UK
| | - Viktor Wottschel
- Amsterdam UMC, Location VUmc, Departments of Radiology and Nuclear Medicine, MS Center Amsterdam, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands.
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