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Qi W, Zhu X, He D, Wang B, Cao S, Dong C, Li Y, Chen Y, Wang B, Shi Y, Jiang G, Liu F, Boots LMM, Li J, Lou X, Yao J, Lu X, Kang J. Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis. J Med Internet Res 2024; 26:e57830. [PMID: 39116438 DOI: 10.2196/57830] [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: 02/27/2024] [Revised: 05/04/2024] [Accepted: 06/25/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. OBJECTIVE The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. METHODS This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. RESULTS To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. CONCLUSIONS The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.
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Affiliation(s)
- Wenhao Qi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaohong Zhu
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Danni He
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Zhejiang Provincial People's Hospital, Hangzhou, China
| | - Bin Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Shihua Cao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Chaoqun Dong
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yunhua Li
- College of Education, Chengdu College of Arts and Sciences, Sichuan, China
| | - Yanfei Chen
- School of Nursing, Hangzhou Normal University, Hangzhou, China
- Nursing Department, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Bingsheng Wang
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Yankai Shi
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Guowei Jiang
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Fang Liu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Lizzy M M Boots
- Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, Netherlands
| | - Jiaqi Li
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiajing Lou
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Jiani Yao
- School of Nursing, Hangzhou Normal University, Hangzhou, China
| | - Xiaodong Lu
- Department of Neurology, Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Junling Kang
- Department of Neurology, The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
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Travaglia A, Hoffmann S. Data sharing as the foundation of discovery: ADNI and breakthroughs in Alzheimer's disease. Alzheimers Dement 2024. [PMID: 39077997 DOI: 10.1002/alz.14129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2024] [Accepted: 06/18/2024] [Indexed: 07/31/2024]
Abstract
The COVID pandemic has shown that when the research community comes together, we can conquer the most complex biomedical challenges. Collaboration and teamwork among federal agencies, private organizations, and researchers have been crucial in the development of vaccines and therapeutics against COVID. Possibly the first example of such cross-functional collaboration is the Alzheimer's Disease Neuroimaging Initiative (ADNI), the largest and longest continually monitored Alzheimer's study. ADNI was designed and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. This article shows how recent successes in the Alzheimer's field are directly a result of ADNI's open and transparent sharing of knowledge, expertise, and resources, which have allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time. ADNI's approach to open-source innovation also served as a model for addressing other complex diseases and led to numerous collaborative research initiatives. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was designed, structured, and operated as a public-private partnership, managed by the Foundation for the National Institutes of Health. The recent successes in the Alzheimer's field are directly a result of ADNI's efforts. Open and transparent sharing of knowledge, expertise, and resources allowed researchers to advance their understanding of Alzheimer's and tackle challenges in a relatively short period of time.
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Affiliation(s)
- Alessio Travaglia
- Translational Science, Neuroscience, Foundation for the National Institutes of Health, North Bethesda, Maryland, USA
| | - Steve Hoffmann
- Science Partnerships, Translational Science, Foundation for the National Institutes of Health, North Bethesda, Maryland, USA
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Pearson MJ, Wagstaff R, Williams RJ. Choroid plexus volumes and auditory verbal learning scores are associated with conversion from mild cognitive impairment to Alzheimer's disease. Brain Behav 2024; 14:e3611. [PMID: 38956818 PMCID: PMC11219301 DOI: 10.1002/brb3.3611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 05/30/2024] [Accepted: 06/01/2024] [Indexed: 07/04/2024] Open
Abstract
PURPOSE Mild cognitive impairment (MCI) can be the prodromal phase of Alzheimer's disease (AD) where appropriate intervention might prevent or delay conversion to AD. Given this, there has been increasing interest in using magnetic resonance imaging (MRI) and neuropsychological testing to predict conversion from MCI to AD. Recent evidence suggests that the choroid plexus (ChP), neural substrates implicated in brain clearance, undergo volumetric changes in MCI and AD. Whether the ChP is involved in memory changes observed in MCI and can be used to predict conversion from MCI to AD has not been explored. METHOD The current study used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to investigate whether later progression from MCI to AD (progressive MCI [pMCI], n = 115) or stable MCI (sMCI, n = 338) was associated with memory scores using the Rey Auditory Verbal Learning Test (RAVLT) and ChP volumes as calculated from MRI. Classification analyses identifying pMCI or sMCI group membership were performed to compare the predictive ability of the RAVLT and ChP volumes. FINDING The results indicated a significant difference between pMCI and sMCI groups for right ChP volume, with the pMCI group showing significantly larger right ChP volume (p = .01, 95% confidence interval [-.116, -.015]). A significant linear relationship between the RAVLT scores and right ChP volume was found across all participants, but not for the two groups separately. Classification analyses showed that a combination of left ChP volume and auditory verbal learning scores resulted in the most accurate classification performance, with group membership accurately predicted for 72% of the testing data. CONCLUSION These results suggest that volumetric ChP changes appear to occur before the onset of AD and might provide value in predicting conversion from MCI to AD.
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Affiliation(s)
- Michael J. Pearson
- Faculty of HealthCharles Darwin UniversityDarwinNorthern TerritoryAustralia
| | - Ruth Wagstaff
- Faculty of HealthCharles Darwin UniversityDarwinNorthern TerritoryAustralia
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İş Ö, Wang X, Reddy JS, Min Y, Yilmaz E, Bhattarai P, Patel T, Bergman J, Quicksall Z, Heckman MG, Tutor-New FQ, Can Demirdogen B, White L, Koga S, Krause V, Inoue Y, Kanekiyo T, Cosacak MI, Nelson N, Lee AJ, Vardarajan B, Mayeux R, Kouri N, Deniz K, Carnwath T, Oatman SR, Lewis-Tuffin LJ, Nguyen T, Carrasquillo MM, Graff-Radford J, Petersen RC, Jr Jack CR, Kantarci K, Murray ME, Nho K, Saykin AJ, Dickson DW, Kizil C, Allen M, Ertekin-Taner N. Gliovascular transcriptional perturbations in Alzheimer's disease reveal molecular mechanisms of blood brain barrier dysfunction. Nat Commun 2024; 15:4758. [PMID: 38902234 PMCID: PMC11190273 DOI: 10.1038/s41467-024-48926-6] [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: 10/09/2023] [Accepted: 05/17/2024] [Indexed: 06/22/2024] Open
Abstract
To uncover molecular changes underlying blood-brain-barrier dysfunction in Alzheimer's disease, we performed single nucleus RNA sequencing in 24 Alzheimer's disease and control brains and focused on vascular and astrocyte clusters as main cell types of blood-brain-barrier gliovascular-unit. The majority of the vascular transcriptional changes were in pericytes. Of the vascular molecular targets predicted to interact with astrocytic ligands, SMAD3, upregulated in Alzheimer's disease pericytes, has the highest number of ligands including VEGFA, downregulated in Alzheimer's disease astrocytes. We validated these findings with external datasets comprising 4,730 pericyte and 150,664 astrocyte nuclei. Blood SMAD3 levels are associated with Alzheimer's disease-related neuroimaging outcomes. We determined inverse relationships between pericytic SMAD3 and astrocytic VEGFA in human iPSC and zebrafish models. Here, we detect vast transcriptome changes in Alzheimer's disease at the gliovascular-unit, prioritize perturbed pericytic SMAD3-astrocytic VEGFA interactions, and validate these in cross-species models to provide a molecular mechanism of blood-brain-barrier disintegrity in Alzheimer's disease.
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Affiliation(s)
- Özkan İş
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Joseph S Reddy
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Yuhao Min
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Elanur Yilmaz
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Prabesh Bhattarai
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Tulsi Patel
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Michael G Heckman
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | | | - Birsen Can Demirdogen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
- Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara, Turkey
| | - Launia White
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Shunsuke Koga
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Vincent Krause
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Yasuteru Inoue
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | - Mehmet Ilyas Cosacak
- German Center for Neurodegenerative Diseases (DZNE) within Helmholtz Association, Dresden, Germany
| | - Nastasia Nelson
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Annie J Lee
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Badri Vardarajan
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Richard Mayeux
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Naomi Kouri
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Kaancan Deniz
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Troy Carnwath
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | - Laura J Lewis-Tuffin
- Mayo Clinic Florida Cytometry and Cell Imaging Laboratory, Mayo Clinic, Jacksonville, FL, USA
| | - Thuy Nguyen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | | | - Ronald C Petersen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic Alzheimer's Disease Research Center, Rochester, MN, USA
| | | | - Kejal Kantarci
- Mayo Clinic Alzheimer's Disease Research Center, Rochester, MN, USA
| | | | - Kwangsik Nho
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Caghan Kizil
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA.
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Lou Y, Ma Y, Du M. A new and unified method for regression analysis of interval-censored failure time data under semiparametric transformation models with missing covariates. Stat Med 2024; 43:2062-2082. [PMID: 38757695 DOI: 10.1002/sim.10035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 01/25/2024] [Accepted: 01/30/2024] [Indexed: 05/18/2024]
Abstract
This paper discusses regression analysis of interval-censored failure time data arising from semiparametric transformation models in the presence of missing covariates. Although some methods have been developed for the problem, they either apply only to limited situations or may have some computational issues. Corresponding to these, we propose a new and unified two-step inference procedure that can be easily implemented using the existing or standard software. The proposed method makes use of a set of working models to extract partial information from incomplete observations and yields a consistent estimator of regression parameters assuming missing at random. An extensive simulation study is conducted and indicates that it performs well in practical situations. Finally, we apply the proposed approach to an Alzheimer's Disease study that motivated this study.
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Affiliation(s)
- Yichen Lou
- School of Mathematics, Jilin University, Changchun, China
| | - Yuqing Ma
- School of Mathematics, Jilin University, Changchun, China
| | - Mingyue Du
- School of Mathematics, Jilin University, Changchun, China
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Ul Rehman S, Tarek N, Magdy C, Kamel M, Abdelhalim M, Melek A, N. Mahmoud L, Sadek I. AI-based tool for early detection of Alzheimer's disease. Heliyon 2024; 10:e29375. [PMID: 38644855 PMCID: PMC11033128 DOI: 10.1016/j.heliyon.2024.e29375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 04/07/2024] [Accepted: 04/07/2024] [Indexed: 04/23/2024] Open
Abstract
In the context of Alzheimer's disease (AD), timely identification is paramount for effective management, acknowledging its chronic and irreversible nature, where medications can only impede its progression. Our study introduces a holistic solution, leveraging the hippocampus and the VGG16 model with transfer learning for early AD detection. The hippocampus, a pivotal early affected region linked to memory, plays a central role in classifying patients into three categories: cognitively normal (CN), representing individuals without cognitive impairment; mild cognitive impairment (MCI), indicative of a subtle decline in cognitive abilities; and AD, denoting Alzheimer's disease. Employing the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our model undergoes training enriched by advanced image preprocessing techniques, achieving outstanding accuracy (testing 98.17 %, validation 97.52 %, training 99.62 %). The strategic use of transfer learning fortifies our competitive edge, incorporating the hippocampus approach and, notably, a progressive data augmentation technique. This innovative augmentation strategy gradually introduces augmentation factors during training, significantly elevating accuracy and enhancing the model's generalization ability. The study emphasizes practical application with a user-friendly website, empowering radiologists to predict class probabilities, track disease progression, and visualize patient images in both 2D and 3D formats, contributing significantly to the advancement of early AD detection.
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Affiliation(s)
| | - Noha Tarek
- Systems and Biomedical Engineering, Faculty of Engineering, Cairo University, Cairo, Egypt
| | - Caroline Magdy
- Systems and Biomedical Engineering, Faculty of Engineering, Cairo University, Cairo, Egypt
| | - Mohammed Kamel
- Systems and Biomedical Engineering, Faculty of Engineering, Cairo University, Cairo, Egypt
| | - Mohammed Abdelhalim
- Systems and Biomedical Engineering, Faculty of Engineering, Cairo University, Cairo, Egypt
| | - Alaa Melek
- Systems and Biomedical Engineering, Faculty of Engineering, Cairo University, Cairo, Egypt
| | - Lamees N. Mahmoud
- Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt
| | - Ibrahim Sadek
- Biomedical Engineering Dept, Faculty of Engineering, Helwan University, Helwan, Cairo, Egypt
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Lee H, Yong SY, Choi H, Yoon GY, Koh S. Association between loneliness and cognitive function, and brain volume in community-dwelling elderly. Front Aging Neurosci 2024; 16:1389476. [PMID: 38741916 PMCID: PMC11089178 DOI: 10.3389/fnagi.2024.1389476] [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: 02/21/2024] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
Introduction We investigated the relationship between loneliness, cognitive impairment, and regional brain volume among elderly individuals residing in the Korean community. Methods Data from the ARIRANG aging-cognition sub-cohort, collected between 2020 and 2022, were utilized for the present study. Loneliness was assessed using the UCLA-Loneliness Scale (UCLA-LS) questionnaire and the relevant item from Center for Epidemiologic Studies Depression Scale Korean version (CES-D-K). Cognitive impairment was measured through Mini-Mental State Examination (K-MMSE-2) and Seoul Neuropsychological Screening Battery (SNSB-C), with five sub-categories: attention, memory, visuospatial function, language, and executive function. Logistic regression was employed for prevalence ratios related to cognitive impairment, while linear regression was used for regional brain volume including white matter hyperintensity (WMH) and cortical thickness. Results Our analysis involved 785 participants (292 men and 493 women). We observed increased cognitive impairment assessed by K-MMSE-2 [UCLA-LS: odds ratio (OR) 3.133, 95% confidence interval (CI) 1.536-6.393; loneliness from CES-D: OR 2.823, 95% CI 1.426-5.590] and SNSB-C total score (UCLA-LS: OR 2.145, 95% CI 1.304-3.529) in the lonely group compared to the non-lonely group. Specifically, the lonely group identified by UCLA-LS showed an association with declined visuospatial (OR 1.591, 95% CI 1.029-2.460) and executive function (OR 1.971, 95% CI 1.036-3.750). The lonely group identified by CES-D-K was associated with impaired memory (OR 1.577, 95% CI 1.009-2.466) and executive function (OR 1.863, 95% CI 1.036-3.350). In the regional brain volume analysis, loneliness was linked to reduced brain volume in frontal white matter (left: -1.24, 95% CI -2.37 ∼-0.12; right: -1.16, 95% CI -2.31 ∼ -0.00), putamen (left: -0.07, 95% CI -0.12 ∼-0.02; right: -0.06, 95% CI -0.11 ∼-0.01), and globus pallidus (-15.53, 95% CI -30.13 ∼-0.93). There was no observed association in WMH and cortical thickness. Conclusion Loneliness is associated with cognitive decline and volumetric reduction in the frontal white matter, putamen, and globus pallidus.
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Affiliation(s)
- Hunju Lee
- Department of Preventive Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
- Institute of Genomic Cohort, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Sang Yeol Yong
- Department of Rehabilitation Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
- International Olympic Committee Research Centre Korea, Yonsei Institute of Sports Science and Exercise Medicine, Wonju, Republic of Korea
| | - Hyowon Choi
- Department of Preventive Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Ga Young Yoon
- Department of Radiology, Wonju Severance Christian Hospital, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
| | - Sangbaek Koh
- Department of Preventive Medicine, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
- Institute of Genomic Cohort, Wonju College of Medicine, Yonsei University, Wonju, Republic of Korea
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Bergamino M, Burke A, Sabbagh MN, Caselli RJ, Baxter LC, Stokes AM. Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study. Front Aging Neurosci 2024; 16:1362613. [PMID: 38562990 PMCID: PMC10982426 DOI: 10.3389/fnagi.2024.1362613] [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: 12/28/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. Methods In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. Discussion The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, OK, United States
| | - Anna Burke
- Division of Neurology, Barrow Neurological Institute, Phoenix, OK, United States
| | - Marwan N. Sabbagh
- Division of Neurology, Barrow Neurological Institute, Phoenix, OK, United States
| | - Richard J. Caselli
- Department of Neuropsychology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Leslie C. Baxter
- Department of Neurology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, OK, United States
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Shan G, Lu X, Li Z, Caldwell JZ, Bernick C, Cummings J. ADSS: A Composite Score to Detect Disease Progression in Alzheimer's Disease. J Alzheimers Dis Rep 2024; 8:307-316. [PMID: 38405343 PMCID: PMC10894615 DOI: 10.3233/adr-230043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 01/11/2024] [Indexed: 02/27/2024] Open
Abstract
Background Composite scores have been increasingly used in trials for Alzheimer's disease (AD) to detect disease progression, such as the AD Composite Score (ADCOMS) in the lecanemab trial. Objective To develop a new composite score to improve the prediction of outcome change. Methods We proposed to develop a new composite score based on the statistical model in the ADCOMS, by removing duplicated sub-scales and adding the model selection in the partial least squares (PLS) regression. Results The new AD composite Score with variable Selection (ADSS) includes 7 cognitive sub-scales. ADSS can increase the sensitivity to detect disease progression as compared to the existing total scores, which leads to smaller sample sizes using the ADSS in trial designs. Conclusions ADSS can be utilized in AD trials to improve the success rate of drug development with a high sensitivity to detect disease progression in early stages.
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Affiliation(s)
- Guogen Shan
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Xinlin Lu
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | - Zhigang Li
- Department of Biostatistics, University of Florida, Gainesville, FL, USA
| | | | - Charles Bernick
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA
| | - Jeffrey Cummings
- Department of Brain Health, School of Integrated Health Sciences, Chambers-Grundy Center for Transformative Neuroscience, University of Nevada Las Vegas (UNLV) Las Vegas, NV, USA
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Chen Y, Lu P, Wu S, Yang J, Liu W, Zhang Z, Xu Q. CD163-Mediated Small-Vessel Injury in Alzheimer's Disease: An Exploration from Neuroimaging to Transcriptomics. Int J Mol Sci 2024; 25:2293. [PMID: 38396970 PMCID: PMC10888773 DOI: 10.3390/ijms25042293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/11/2024] [Indexed: 02/25/2024] Open
Abstract
Patients with Alzheimer's disease (AD) often present with imaging features indicative of small-vessel injury, among which, white-matter hyperintensities (WMHs) are the most prevalent. However, the underlying mechanism of the association between AD and small-vessel injury is still obscure. The aim of this study is to investigate the mechanism of small-vessel injury in AD. Differential gene expression analyses were conducted to identify the genes related to WMHs separately in mild cognitive impairment (MCI) and cognitively normal (CN) subjects from the ADNI database. The WMH-related genes identified in patients with MCI were considered to be associated with small-vessel injury in early AD. Functional enrichment analyses and a protein-protein interaction (PPI) network were performed to explore the pathway and hub genes related to the mechanism of small-vessel injury in MCI. Subsequently, the Boruta algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm were performed to identify feature-selection genes. Finally, the mechanism of small-vessel injury was analyzed in MCI from the immunological perspectives; the relationship of feature-selection genes with various immune cells and neuroimaging indices were also explored. Furthermore, 5×FAD mice were used to demonstrate the genes related to small-vessel injury. The results of the logistic regression analyses suggested that WMHs significantly contributed to MCI, the early stage of AD. A total of 276 genes were determined as WMH-related genes in patients with MCI, while 203 WMH-related genes were obtained in CN patients. Among them, only 15 genes overlapped and were thus identified as the crosstalk genes. By employing the Boruta and SVM-RFE algorithms, CD163, ALDH3B1, MIR22HG, DTX2, FOLR2, ALDH2, and ZNF23 were recognized as the feature-selection genes linked to small-vessel injury in MCI. After considering the results from the PPI network, CD163 was finally determined as the critical WMH-related gene in MCI. The expression of CD163 was correlated with fractional anisotropy (FA) values in regions that are vulnerable to small-vessel injury in AD. The immunostaining and RT-qPCR results from the verifying experiments demonstrated that the indicators of small-vessel injury presented in the cortical tissue of 5×FAD mice and related to the upregulation of CD163 expression. CD163 may be the most pivotal candidates related to small-vessel injury in early AD.
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Affiliation(s)
- Yuewei Chen
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Peiwen Lu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Shengju Wu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Jie Yang
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Wanwan Liu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
| | - Zhijun Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
| | - Qun Xu
- Health Management Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China; (Y.C.); (P.L.); (W.L.)
- Department of Neurology, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
- Renji-UNSW CHeBA (Centre for Healthy Brain Ageing of University of New South Wales) Neurocognitive Center, Renji Hospital of Medical School, Shanghai Jiao Tong University, Shanghai 200127, China
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11
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Erickson CM, Karlawish J, Grill JD, Harkins K, Landau SM, Rivera-Mindt MG, Okonkwo O, Petersen RC, Aisen PS, Weiner MW, Largent EA. A Pragmatic, Investigator-Driven Process for Disclosure of Amyloid PET Scan Results to ADNI-4 Research Participants. J Prev Alzheimers Dis 2024; 11:294-302. [PMID: 38374735 PMCID: PMC10883638 DOI: 10.14283/jpad.2024.33] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
BACKGROUND Prior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples. METHODS The Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included. RESULTS Data collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators. CONCLUSION The pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.
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Affiliation(s)
- C M Erickson
- Emily Largent JD, PhD, RN, 423 Guardian Drive Philadelphia, PA 19104, USA,
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12
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Petrella JR, Jiang J, Sreeram K, Dalziel S, Doraiswamy PM, Hao W. Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade. J Prev Alzheimers Dis 2024; 11:435-444. [PMID: 38374750 PMCID: PMC11082854 DOI: 10.14283/jpad.2023.134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
BACKGROUND Mathematical models of complex diseases, such as Alzheimer's disease, have the potential to play a significant role in personalized medicine. Specifically, models can be personalized by fitting parameters with individual data for the purpose of discovering primary underlying disease drivers, predicting natural history, and assessing the effects of theoretical interventions. Previous work in causal/mechanistic modeling of Alzheimer's Disease progression has modeled the disease at the cellular level and on a short time scale, such as minutes to hours. No previous studies have addressed mechanistic modeling on a personalized level using clinically validated biomarkers in individual subjects. OBJECTIVES This study aimed to investigate the feasibility of personalizing a causal model of Alzheimer's Disease progression using longitudinal biomarker data. DESIGN/SETTING/PARTICIPANTS/MEASUREMENTS We chose the Alzheimer Disease Biomarker Cascade model, a widely-referenced hypothetical model of Alzheimer's Disease based on the amyloid cascade hypothesis, which we had previously implemented mathematically as a mechanistic model. We used available longitudinal demographic and serial biomarker data in over 800 subjects across the cognitive spectrum from the Alzheimer's Disease Neuroimaging Initiative. The data included participants that were cognitively normal, had mild cognitive impairment, or were diagnosed with dementia (probable Alzheimer's Disease). The model consisted of a sparse system of differential equations involving four measurable biomarkers based on cerebrospinal fluid proteins, imaging, and cognitive testing data. RESULTS Personalization of the Alzheimer Disease Biomarker Cascade model with individual serial biomarker data yielded fourteen personalized parameters in each subject reflecting physiologically meaningful characteristics. These included growth rates, latency values, and carrying capacities of the various biomarkers, most of which demonstrated significant differences across clinical diagnostic groups. The model fits to training data across the entire cohort had a root mean squared error (RMSE) of 0.09 (SD 0.081) on a variable scale between zero and one, and were robust, with over 90% of subjects showing an RMSE of < 0.2. Similarly, in a subset of subjects with data on all four biomarkers in at least one test set, performance was high on the test sets, with a mean RMSE of 0.15 (SD 0.117), with 80% of subjects demonstrating an RMSE < 0.2 in the estimation of future biomarker points. Cluster analysis of parameters revealed two distinct endophenotypic groups, with distinct biomarker profiles and disease trajectories. CONCLUSION Results support the feasibility of personalizing mechanistic models based on individual biomarker trajectories and suggest that this approach may be useful for reclassifying subjects on the Alzheimer's clinical spectrum. This computational modeling approach is not limited to the Alzheimer Disease Biomarker Cascade hypothesis, and can be applied to any mechanistic hypothesis of disease progression in the Alzheimer's field that can be monitored with biomarkers. Thus, it offers a computational platform to compare and validate various disease hypotheses, personalize individual biomarker trajectories and predict individual response to theoretical prevention and therapeutic intervention strategies.
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Affiliation(s)
- J R Petrella
- Jeffrey R. Petrella, Department of Radiology, Duke University School of Medicine, DUMC - Box 3808 , 27710-3808, NC, USA
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13
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Chi HC, Ma LZ, Wang ZB, Sheng ZH, Liu JY, Mi YC, Fu Y, Huang YM, Han SL, Gao PY, Tan L, Yu JT. Associations of Frailty with Neuropsychiatric Symptoms of Alzheimer's Disease: A Longitudinal Study. J Alzheimers Dis 2024; 98:629-642. [PMID: 38427482 DOI: 10.3233/jad-231111] [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] [Indexed: 03/03/2024]
Abstract
Background Frailty is a vulnerability state increasing the risk of many adverse health outcomes, but little is known about the effects of frailty on neuropsychiatric health. Objective To explore the associations between frailty and the risk of neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD), especially in its different clinical stages. Methods We included 2,155 individuals assessed using modified frailty index-11 (mFI-11), Neuropsychiatric Inventory (NPI) and Neuropsychiatric Inventory Questionnaire (NPI-Q) in the Alzheimer's Disease Neuroimaging Initiative (ADNI). The relationships between frailty and NPSs were explored with logistic regression models and Cox proportional hazard regression models. Causal mediation analyses were conducted to explore the mediation factors between frailty and NPSs. Results Among mild cognitive impairment (MCI) participants, frailty was cross-sectionally associated with an increased risk of apathy, and longitudinally associated with increased risk of depression and apathy. Among AD participants, frailty was cross-sectionally associated with increased risk of depression and anxiety, and longitudinally associated with an increased risk of apathy. Among participants with cognitive progression, frailty was associated with increased risk of depression and apathy. In MCI participants, the influence of frailty on NPSs was partially mediated by hippocampus volume, whole brain volume, and monocytes, with mediating proportions ranging from 8.40% to 9.29%. Conclusions Frailty was associated with NPSs such as depression, anxiety, and apathy among MCI, AD, and cognitive progression participants. Atrophy of the hippocampus and whole brain, as well as peripheral immunity may be involved in the potential mechanisms underlying the above associations.
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Affiliation(s)
- Hao-Chen Chi
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ling-Zhi Ma
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Zhi-Bo Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Ze-Hu Sheng
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jia-Yao Liu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yin-Chu Mi
- Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, Dalian, China
| | - Yan Fu
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Yi-Ming Huang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Shuang-Ling Han
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Pei-Yang Gao
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Lan Tan
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
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14
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Alwuthaynani MM, Abdallah ZS, Santos-Rodriguez R. A robust class decomposition-based approach for detecting Alzheimer's progression. Exp Biol Med (Maywood) 2023; 248:2514-2525. [PMID: 38059336 PMCID: PMC10854473 DOI: 10.1177/15353702231211880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 10/17/2023] [Accepted: 02/28/2023] [Indexed: 12/08/2023] Open
Abstract
Computer-aided diagnosis of Alzheimer's disease (AD) is a rapidly growing field with the possibility to be utilized in practice. Deep learning has received much attention in detecting AD from structural magnetic resonance imaging (sMRI). However, training a convolutional neural network from scratch is problematic because it requires a lot of annotated data and additional computational time. Transfer learning can offer a promising and practical solution by transferring information learned from other image recognition tasks to medical image classification. Another issue is the dataset distribution's irregularities. A common classification issue in datasets is a class imbalance, where the distribution of samples among the classes is biased. For example, a dataset may contain more instances of some classes than others. Class imbalance is challenging because most machine learning algorithms assume that each class should have an equal number of samples. Models consequently perform poorly in prediction. Class decomposition can address this problem by making learning a dataset's class boundaries easier. Motivated by these approaches, we propose a class decomposition transfer learning (CDTL) approach that employs VGG19, AlexNet, and an entropy-based technique to detect AD from sMRI. This study aims to assess the robustness of the CDTL approach in detecting the cognitive decline of AD using data from various ADNI cohorts to determine whether comparable classification accuracy for the two or more cohorts would be obtained. Furthermore, the proposed model achieved state-of-the-art performance in predicting mild cognitive impairment (MCI)-to-AD conversion with an accuracy of 91.45%.
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Affiliation(s)
- Maha M Alwuthaynani
- University of Bristol, Bristol BS8 1TH, UK
- College of Computer Science & Information Systems, Najran University, Najran 61441, Saudi Arabia
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15
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Lefort-Besnard J, Naveau M, Delcroix N, Decker LM, Cignetti F. Grey matter volume and CSF biomarkers predict neuropsychological subtypes of MCI. Neurobiol Aging 2023; 131:196-208. [PMID: 37689017 DOI: 10.1016/j.neurobiolaging.2023.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 09/11/2023]
Abstract
There is increasing evidence of different subtypes of individuals with mild cognitive impairment (MCI). An important line of research is whether neuropsychologically-defined subtypes have distinct patterns of neurodegeneration and cerebrospinal fluid (CSF) biomarker composition. In our study, we demonstrated that MCI participants of the ADNI database (N = 640) can be discriminated into 3 coherent neuropsychological subgroups. Our clustering approach revealed amnestic MCI, mixed MCI, and cluster-derived normal subgroups. Furthermore, classification modeling revealed that specific predictive features can be used to differentiate amnestic and mixed MCI from cognitively normal (CN) controls: CSF Aβ142 concentration for the former and CSF Aβ1-42 concentration, tau concentration as well as grey matter atrophy (especially in the temporal and occipital lobes) for the latter. In contrast, participants from the cluster-derived normal subgroup exhibited an identical profile to CN controls in terms of cognitive performance, brain structure, and CSF biomarker levels. Our comprehensive data analytics strategy provides further evidence that multimodal neuropsychological subtyping is both clinically and neurobiologically meaningful.
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Affiliation(s)
| | - Mikael Naveau
- Normandie Univ, UNICAEN, CNRS, CEA, INSERM, GIP Cyceron, Caen, France
| | - Nicolas Delcroix
- Normandie Univ, UNICAEN, CNRS, CEA, INSERM, GIP Cyceron, Caen, France
| | - Leslie Marion Decker
- Normandie Univ, UNICAEN, INSERM, COMETE, Caen, France; Normandie Univ, UNICAEN, CIREVE, Caen, France.
| | - Fabien Cignetti
- Univ. Grenoble Alpes, CNRS, VetAgro Sup, Grenoble INP, TIMC, Grenoble, France.
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16
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Bedford SA, Ortiz-Rosa A, Schabdach JM, Costantino M, Tullo S, Piercy T, Lai MC, Lombardo MV, Di Martino A, Devenyi GA, Chakravarty MM, Alexander-Bloch AF, Seidlitz J, Baron-Cohen S, Bethlehem RA. The impact of quality control on cortical morphometry comparisons in autism. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-21. [PMID: 38495338 PMCID: PMC10938341 DOI: 10.1162/imag_a_00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 08/11/2023] [Accepted: 09/13/2023] [Indexed: 03/19/2024]
Abstract
Structural magnetic resonance imaging (MRI) quality is known to impact and bias neuroanatomical estimates and downstream analysis, including case-control comparisons, and a growing body of work has demonstrated the importance of careful quality control (QC) and evaluated the impact of image and image-processing quality. However, the growing size of typical neuroimaging datasets presents an additional challenge to QC, which is typically extremely time and labour intensive. One of the most important aspects of MRI quality is the accuracy of processed outputs, which have been shown to impact estimated neurodevelopmental trajectories. Here, we evaluate whether the quality of surface reconstructions by FreeSurfer (one of the most widely used MRI processing pipelines) interacts with clinical and demographic factors. We present a tool, FSQC, that enables quick and efficient yet thorough assessment of outputs of the FreeSurfer processing pipeline. We validate our method against other existing QC metrics, including the automated FreeSurfer Euler number, two other manual ratings of raw image quality, and two popular automated QC methods. We show strikingly similar spatial patterns in the relationship between each QC measure and cortical thickness; relationships for cortical volume and surface area are largely consistent across metrics, though with some notable differences. We next demonstrate that thresholding by QC score attenuates but does not eliminate the impact of quality on cortical estimates. Finally, we explore different ways of controlling for quality when examining differences between autistic individuals and neurotypical controls in the Autism Brain Imaging Data Exchange (ABIDE) dataset, demonstrating that inadequate control for quality can alter results of case-control comparisons.
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Affiliation(s)
- Saashi A. Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Alfredo Ortiz-Rosa
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
| | - Jenna M. Schabdach
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
| | - Manuela Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
| | - Stephanie Tullo
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
| | - Tom Piercy
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | | | - Meng-Chuan Lai
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- The Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health and Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry and Autism Research Unit, The Hospital for Sick Children, Toronto, Canada
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Michael V. Lombardo
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems, Istituto Italiano di Tecnologia, Rovereto, Italy
| | | | - Gabriel A. Devenyi
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Canada
- Department of Psychiatry, McGill University, Montreal, Canada
| | - M. Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Canada
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, McGill University, Montreal, Canada
- Department of Biomedical Engineering, McGill University, Montreal, Canada
| | - Aaron F. Alexander-Bloch
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Jakob Seidlitz
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, United States
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children’s Hospital of Philadelphia, Philadelphia, PA, United States
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, United States
| | - Simon Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough, United Kingdom
| | - Richard A.I. Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
- Department of Psychology, University of Cambridge, Cambridge, United Kingdom
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Kurucu MC, Rekik I. Graph neural network based unsupervised influential sample selection for brain multigraph population fusion. Comput Med Imaging Graph 2023; 108:102274. [PMID: 37531812 DOI: 10.1016/j.compmedimag.2023.102274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/17/2023] [Accepted: 07/17/2023] [Indexed: 08/04/2023]
Abstract
Graph neural networks (GNNs) have witnessed remarkable proliferation due to the increasing number of applications where data is represented as graphs. GNN-based multigraph population fusion methods for estimating population representative connectional brain templates (CBT) have recently led to improvements, especially in network neuroscience. However, prior studies do not consider how an individual training brain multigraph influences the quality of GNN training for brain multigraph population fusion. To address this issue, we propose two major sample selection methods to quantify the influence of a training brain multigraph on the brain multigraph population fusion task using GNNs, in a fully unsupervised manner: (1) GraphGradIn, in which we use gradients w.r.t GNN weights to trace changes in the centeredness loss of connectional brain template during the training phase; (2) GraphTestIn, in which we exclude a training brain multigraph of interest during the refinement process in the test phase to infer its influence on the CBT centeredness loss. Next, we select the most influential multigraphs to build the training set for brain multigraph population fusion into a CBT. We conducted extensive experiments on brain multigraph datasets to show that using a dataset of influential training samples improves the learned connectional brain template in terms of centeredness, discriminativeness, and topological soundness. Finally, we demonstrate the use of our methods by discovering the connectional fingerprints of healthy and neurologically disordered brain multigraph populations including Alzheimer's disease and Autism spectrum disorder patients. Our source code is available at https://github.com/basiralab/GraphGradIn.
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Affiliation(s)
- Mert Can Kurucu
- BASIRA Lab, Imperial-X and Computing Department, Imperial College London, London, UK; Istanbul Technical University, Istanbul, Turkey
| | - Islem Rekik
- BASIRA Lab, Imperial-X and Computing Department, Imperial College London, London, UK.
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Martin SA, Townend FJ, Barkhof F, Cole JH. Interpretable machine learning for dementia: A systematic review. Alzheimers Dement 2023; 19:2135-2149. [PMID: 36735865 PMCID: PMC10955773 DOI: 10.1002/alz.12948] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/05/2022] [Accepted: 12/20/2022] [Indexed: 02/05/2023]
Abstract
INTRODUCTION Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently "interpretable," whereas post hoc "explainability" methods can be used for other models. METHODS Here we sought to summarize the state-of-the-art of interpretable machine learning for dementia. RESULTS We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets. DISCUSSION Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia-related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient-specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.
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Affiliation(s)
- Sophie A. Martin
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
- Dementia Research CentreQueen Square Institute of NeurologyUniversity College LondonLondonUK
| | - Florence J. Townend
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
| | - Frederik Barkhof
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
- Dementia Research CentreQueen Square Institute of NeurologyUniversity College LondonLondonUK
- Amsterdam UMC, Department of Radiology & Nuclear MedicineVrije UniversiteitAmsterdamNetherlands
| | - James H. Cole
- Centre for Medical Image ComputingDepartment of Computer ScienceUniversity College LondonLondonUK
- Dementia Research CentreQueen Square Institute of NeurologyUniversity College LondonLondonUK
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Neu SC, Crawford KL, Toga AW. The image and data archive at the laboratory of neuro imaging. Front Neuroinform 2023; 17:1173623. [PMID: 37181736 PMCID: PMC10169596 DOI: 10.3389/fninf.2023.1173623] [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: 02/24/2023] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The Image and Data Archive (IDA) is a secure online resource for archiving, exploring, and sharing neuroscience data run by the Laboratory of Neuro Imaging (LONI). The laboratory first started managing neuroimaging data for multi-centered research studies in the late 1990's and since has become a nexus for many multi-site collaborations. By providing management and informatics tools and resources for de-identifying, integrating, searching, visualizing, and sharing a diverse range of neuroscience data, study investigators maintain complete control over data stored in the IDA while benefiting from a robust and reliable infrastructure that protects and preserves research data to maximize data collection investment.
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Affiliation(s)
| | | | - Arthur W. Toga
- Laboratory of Neuro Imaging, Department of Neurology, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States
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20
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Chaari N, Akdağ HC, Rekik I. Comparative survey of multigraph integration methods for holistic brain connectivity mapping. Med Image Anal 2023; 85:102741. [PMID: 36638747 DOI: 10.1016/j.media.2023.102741] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.
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Affiliation(s)
- Nada Chaari
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Faculty of Management, Istanbul Technical University, Istanbul, Turkey
| | | | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Computing, Imperial-X Translation and Innovation Hub, Imperial College London, London, UK.
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21
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Tang X, Mo Z, Chang C, Qian X. Group-shrinkage feature selection with a spatial network for mining DNA methylation data. Comput Biol Med 2023; 154:106573. [PMID: 36706568 DOI: 10.1016/j.compbiomed.2023.106573] [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: 11/09/2022] [Revised: 01/05/2023] [Accepted: 01/22/2023] [Indexed: 01/25/2023]
Abstract
Identifying disease-related biomarkers from high-dimensional DNA methylation data helps in reducing early screening costs and inferring pathogenesis mechanisms. Good discovery results have been achieved through spatial correlation methods of methylation sites, group-based regularization, and network constraints. However, these methods still have some key limitations as they cannot exclude isolated differential sites and only consider adjacent site ordering. Therefore, we propose a group-shrinkage feature selection algorithm to encourage the selection of clustered sites and discourage the selection of isolated differential sites. Specifically, a network-guided group-shrinkage strategy is developed to penalize weakly-correlated isolated methylation sites through a network structure constraint. The spatial network is constructed based on spatial correlation information of DNA methylation sites, where this information accounts for the uneven site distribution. The experimental simulations and applications demonstrated that the proposed method outperforms the advanced regularization methods, especially in rejecting isolated methylation sites; hence this study provides an efficient and clinical-valuable method for biomarker candidate discovery in DNA methylation data. Additionally, the proposed method exhibits enhanced reliability due to introducing biological prior knowledge into a regularization-based feature selection framework and could promote more research in the integration between biological prior knowledge and classical feature selection methods, thus facilitating their clinical application. Our source codes will be released at https://github.com/SJTUBME-QianLab/Group-shrinkage-Spatial-Network once this manuscript is accepted for publication.
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Affiliation(s)
- Xinlu Tang
- Medical Image and Health Informatics Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Zhanfeng Mo
- School of Computer Science and Engineering, Nanyang Technological University, Singapore.
| | - Cheng Chang
- Department of Nuclear Medicine, Shanghai, Chest Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, China.
| | - Xiaohua Qian
- Medical Image and Health Informatics Lab, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
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22
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Haddad E, Pizzagalli F, Zhu AH, Bhatt RR, Islam T, Ba Gari I, Dixon D, Thomopoulos SI, Thompson PM, Jahanshad N. Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3. Hum Brain Mapp 2023; 44:1515-1532. [PMID: 36437735 PMCID: PMC9921222 DOI: 10.1002/hbm.26147] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 10/19/2022] [Accepted: 10/19/2022] [Indexed: 11/29/2022] Open
Abstract
Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.
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Affiliation(s)
- Elizabeth Haddad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Fabrizio Pizzagalli
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA.,Department of Neurosciences, University of Turin, Turin, Italy
| | - Alyssa H Zhu
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Ravi R Bhatt
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Tasfiya Islam
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Iyad Ba Gari
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Daniel Dixon
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, California, USA
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23
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Vizcarra JC, Teich AF, Dugger BN, Gutman DA. Survey of Neuroanatomic Sampling and Staining Procedures in Alzheimer Disease Research Center Brain Banks. FREE NEUROPATHOLOGY 2023; 4:4-6. [PMID: 37347036 PMCID: PMC10280272 DOI: 10.17879/freeneuropathology-2023-4696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 03/28/2023] [Indexed: 06/23/2023]
Abstract
The collection of post-mortem brain tissue has been a core function of the Alzheimer Disease Research Center's (ADRCs) network located within the United States since its inception. Individual brain banks and centers follow detailed protocols to record, store, and manage complex datasets that include clinical data, demographics, and when post-mortem tissue is available, a detailed neuropathological assessment. Since each institution often has specific research foci, there can be variability in tissue collection and processing workflows. While published guidelines exist for select diseases, such as those put forth by the National Institute on Aging and Alzheimer Association (NIA-AA), it is of importance to denote the current practices across institutions. To this end a survey was developed and sent to United States based brain bank leaders, collecting data on brain region sampling, including anatomic landmarks used, staining (including antibodies used), as well as whole-slide-image scanning hardware. We distributed this survey to 40 brain banks and obtained a response rate of 95% (38 / 40). Most brain banks followed guidelines defined by the NIA-AA, having H&E staining in all recommended regions and targeted region-based amyloid beta, tau, and alpha-synuclein immunohistochemical staining. However, sampling consistency varied related to key anatomic landmarks/locations in select regions, such as the striatum, periventricular white matter, and parietal cortex. This study highlights the diversity and similarities amongst brain banks and discusses considerations when amalgamating data/samples across multiple centers. This survey aids in establishing benchmarks to enhance dialogues on divergent workflows in a feasible way.
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Affiliation(s)
- Juan C. Vizcarra
- Department of Biomedical Engineering, Emory University & Georgia Institute of Technology, Atlanta, USA
| | - Andrew F. Teich
- Department of Pathology and Cell Biology, Department of Neurology, The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York, USA
| | - Brittany N. Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - David A. Gutman
- Department of Neuropathology, Emory University, Atlanta, Georgia, USA
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24
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Wang T, Huynh K, Giles C, Mellett NA, Duong T, Nguyen A, Lim WLF, Smith AAT, Olshansky G, Cadby G, Hung J, Hui J, Beilby J, Watts GF, Chatterjee P, Martins I, Laws SM, Bush AI, Rowe CC, Villemagne VL, Ames D, Masters CL, Taddei K, Doré V, Fripp J, Arnold M, Kastenmüller G, Nho K, Saykin AJ, Baillie R, Han X, Martins RN, Moses EK, Kaddurah‐Daouk R, Meikle PJ. APOE ε2 resilience for Alzheimer's disease is mediated by plasma lipid species: Analysis of three independent cohort studies. Alzheimers Dement 2022; 18:2151-2166. [PMID: 35077012 PMCID: PMC9787288 DOI: 10.1002/alz.12538] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/19/2021] [Accepted: 10/25/2021] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The apolipoprotein E (APOE) genotype is the strongest genetic risk factor for late-onset Alzheimer's disease. However, its effect on lipid metabolic pathways, and their mediating effect on disease risk, is poorly understood. METHODS We performed lipidomic analysis on three independent cohorts (the Australian Imaging, Biomarkers and Lifestyle [AIBL] flagship study, n = 1087; the Alzheimer's Disease Neuroimaging Initiative [ADNI] 1 study, n = 819; and the Busselton Health Study [BHS], n = 4384), and we defined associations between APOE ε2 and ε4 and 569 plasma/serum lipid species. Mediation analysis defined the proportion of the treatment effect of the APOE genotype mediated by plasma/serum lipid species. RESULTS A total of 237 and 104 lipid species were associated with APOE ε2 and ε4, respectively. Of these 68 (ε2) and 24 (ε4) were associated with prevalent Alzheimer's disease. Individual lipid species or lipidomic models of APOE genotypes mediated up to 30% and 10% of APOE ε2 and ε4 treatment effect, respectively. DISCUSSION Plasma lipid species mediate the treatment effect of APOE genotypes on Alzheimer's disease and as such represent a potential therapeutic target.
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25
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Shan G. Monte Carlo cross-validation for a study with binary outcome and limited sample size. BMC Med Inform Decis Mak 2022; 22:270. [PMID: 36253749 PMCID: PMC9578204 DOI: 10.1186/s12911-022-02016-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 10/10/2022] [Indexed: 11/26/2022] Open
Abstract
Cross-validation (CV) is a resampling approach to evaluate machine learning models when sample size is limited. The number of all possible combinations of folds for the training data, known as CV rounds, are often very small in leave-one-out CV. Alternatively, Monte Carlo cross-validation (MCCV) can be performed with a flexible number of simulations when computational resources are feasible for a study with limited sample size. We conduct extensive simulation studies to compare accuracy between MCCV and CV with the same number of simulations for a study with binary outcome (e.g., disease progression or not). Accuracy of MCCV is generally higher than CV although the gain is small. They have similar performance when sample size is large. Meanwhile, MCCV is going to provide reliable performance metrics as the number of simulations increases. Two real examples are used to illustrate the comparison between MCCV and CV.
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Affiliation(s)
- Guogen Shan
- Department of Biostatistics, University of Florida, Gainesville, FL, 32610, USA.
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26
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Ramamoorthy D, Severson K, Ghosh S, Sachs K, Glass JD, Fournier CN, Herrington TM, Berry JD, Ng K, Fraenkel E. Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data. NATURE COMPUTATIONAL SCIENCE 2022; 2:605-616. [PMID: 38177466 PMCID: PMC10766562 DOI: 10.1038/s43588-022-00299-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Accepted: 07/14/2022] [Indexed: 01/06/2024]
Abstract
The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer's and Parkinson's diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.
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Affiliation(s)
| | - Kristen Severson
- Center for Computational Health and MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA
| | - Soumya Ghosh
- Center for Computational Health and MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA
| | - Karen Sachs
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Next Generation Analytics, Palo Alto, CA, USA
| | - Jonathan D Glass
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | | | - Todd M Herrington
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - James D Berry
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Kenney Ng
- Center for Computational Health and MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA
| | - Ernest Fraenkel
- Department of Biological Engineering, MIT, Cambridge, MA, USA.
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27
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Cobbinah BM, Sorg C, Yang Q, Ternblom A, Zheng C, Han W, Che L, Shao J. Reducing variations in multi-center Alzheimer's disease classification with convolutional adversarial autoencoder. Med Image Anal 2022; 82:102585. [PMID: 36057187 DOI: 10.1016/j.media.2022.102585] [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: 12/11/2021] [Revised: 07/22/2022] [Accepted: 08/15/2022] [Indexed: 11/29/2022]
Abstract
Based on brain magnetic resonance imaging (MRI), multiple variations ranging from MRI scanners to center-specific parameter settings, imaging protocols, and brain region-of-interest (ROI) definitions pose a big challenge for multi-center Alzheimer's disease characterization and classification. Existing approaches to reduce such variations require intricate multi-step, often manual preprocessing pipelines, including skull stripping, segmentation, registration, cortical reconstruction, and ROI outlining. Such procedures are time-consuming, and more importantly, tend to be user biased. Contrasting costly and biased preprocessing pipelines, the question arises whether we can design a deep learning model to automatically reduce these variations from multiple centers for Alzheimer's disease classification? In this study, we used T1 and T2-weighted structural MRI from Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on three groups with 375 subjects, respectively: patients with Alzheimer's disease (AD) dementia, with mild cognitive impairment (MCI), and healthy controls (HC); to test our approach, we defined AD classification as classifying an individual's structural image to one of the three group labels. We first introduced a convolutional adversarial autoencoder (CAAE) to reduce the variations existing in multi-center raw MRI scans by automatically registering them into a common aligned space. Afterward, a convolutional residual soft attention network (CRAT) was further proposed for AD classification. Canonical classification procedures demonstrated that our model achieved classification accuracies of 91.8%, 90.05%, and 88.10% for the 2-way classification tasks using the RAW aligned MRI scans, including AD vs. HC, AD vs. MCI, and MCI vs. HC, respectively. Thus, our automated approach achieves comparable or even better classification performance by comparing it with many baselines with dedicated conventional preprocessing pipelines. Furthermore, the uncovered brain hotpots, i.e., hippocampus, amygdala, and temporal pole, are consistent with previous studies.
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Affiliation(s)
- Bernard M Cobbinah
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Christian Sorg
- Department of Neuroradiology, TUM-NIC Neuroimaging Center of Technical University Munich, Germany
| | - Qinli Yang
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Arvid Ternblom
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Changgang Zheng
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Wei Han
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Liwei Che
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China
| | - Junming Shao
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731 Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, 611731 Chengdu, China; Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China.
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28
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Lane HY, Lin CH. Diagnosing Alzheimer's Disease Specifically and Sensitively With pLG72 and Cystine/Glutamate Antiporter SLC7A11 AS Blood Biomarkers. Int J Neuropsychopharmacol 2022; 26:1-8. [PMID: 35986919 PMCID: PMC9850657 DOI: 10.1093/ijnp/pyac053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 07/08/2022] [Accepted: 08/19/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Reliable blood biomarkers for Alzheimer's disease (AD) have been lacking. The D-amino acids oxidase modulator (named pLG72) modulates glutamate N-methyl-D-aspartate receptor activity. The cystine/glutamate antiporter contains a SLC7A11 subunit, which mediates glutamate release. This study aimed to determine the accuracy of pLG72 protein and SLC7A11 mRNA in diagnosing AD. METHODS This study enrolled 130 healthy controls and 109 unmatched AD patients; among them, 40 controls and 70 patients were selected to match by age. We measured their pLG72 protein in plasma and SLC7A11 mRNA in white blood cells. RESULTS AD patients had markedly higher pLG72 levels and SLC7A11 mRNA ΔCT values than healthy controls (in both unmatched and matched cohorts; all 4 P values <.001). The receiver operating characteristics analysis in the unmatched cohorts demonstrated that the pLG72 level had a high specificity (0.900) at the optimal cutoff value of 2.3285, the ΔCT of SLC7A11 mRNA displayed an excellent sensitivity (0.954) at the cutoff of 12.185, and the combined value of pLG72 and SLC7A11 ΔCT determined a favorable area under the curve (AUC) (0.882) at the cutoff of 21.721. The AUC of the combined value surpassed that of either biomarker. The specificity, sensitivity, and AUC of the matched cohort were like those of the unmatched cohort. CONCLUSIONS The findings suggest that pLG72 protein and SLC7A11 mRNA can distinguish AD patients from healthy controls with excellent specificity and sensitivity, respectively. The combination of pLG72 and SLC7A11 yields better AUC than either, suggesting the superiority of simultaneously measuring both biomarkers in identifying AD patients.
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Affiliation(s)
- Hsien-Yuan Lane
- Department of Psychiatry and Brain Disease Research Center, China Medical University Hospital, Taichung, Taiwan,Graduate Institute of Biomedical Sciences, China Medical University, Taichung, Taiwan,Department of Psychology, College of Medical and Health Sciences, Asia University, Taichung, Taiwan
| | - Chieh-Hsin Lin
- Correspondence: Chieh-Hsin Lin, MD, PhD, Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, No. 123, Da-Pi Rd, Kaohsiung 833, Taiwan ()
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29
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Dumurgier J, Sabia S, Zetterberg H, Teunissen CE, Hanseeuw B, Orellana A, Schraen S, Gabelle A, Boada M, Lebouvier T, Willemse EAJ, Cognat E, Ruiz A, Hourregue C, Lilamand M, Bouaziz-Amar E, Laplanche JL, Lehmann S, Pasquier F, Scheltens P, Blennow K, Singh-Manoux A, Paquet C. A Pragmatic, Data-Driven Method to Determine Cutoffs for CSF Biomarkers of Alzheimer Disease Based on Validation Against PET Imaging. Neurology 2022; 99:e669-e678. [PMID: 35970577 PMCID: PMC9484605 DOI: 10.1212/wnl.0000000000200735] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 03/30/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND AND OBJECTIVES To elaborate a new algorithm to establish a standardized method to define cutoffs for CSF biomarkers of Alzheimer disease (AD) by validating the algorithm against CSF classification derived from PET imaging. METHODS Low and high levels of CSF phosphorylated tau were first identified to establish optimal cutoffs for CSF β-amyloid (Aβ) peptide biomarkers. These Aβ cutoffs were then used to determine cutoffs for CSF tau and phosphorylated tau markers. We compared this algorithm to a reference method, based on tau and amyloid PET imaging status (ADNI study), and then applied the algorithm to 10 large clinical cohorts of patients. RESULTS A total of 6,922 patients with CSF biomarker data were included (mean [SD] age: 70.6 [8.5] years, 51.0% women). In the ADNI study population (n = 497), the agreement between classification based on our algorithm and the one based on amyloid/tau PET imaging was high, with Cohen's kappa coefficient between 0.87 and 0.99. Applying the algorithm to 10 large cohorts of patients (n = 6,425), the proportion of persons with AD ranged from 25.9% to 43.5%. DISCUSSION The proposed novel, pragmatic method to determine CSF biomarker cutoffs for AD does not require assessment of other biomarkers or assumptions concerning the clinical diagnosis of patients. Use of this standardized algorithm is likely to reduce heterogeneity in AD classification.
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Affiliation(s)
- Julien Dumurgier
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom.
| | - Séverine Sabia
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Henrik Zetterberg
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Charlotte E Teunissen
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Bernard Hanseeuw
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Adelina Orellana
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Susanna Schraen
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Audrey Gabelle
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Mercè Boada
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Thibaud Lebouvier
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Eline A J Willemse
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Emmanuel Cognat
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Agustin Ruiz
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Claire Hourregue
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Matthieu Lilamand
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Elodie Bouaziz-Amar
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Jean-Louis Laplanche
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Sylvain Lehmann
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Florence Pasquier
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Philip Scheltens
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Kaj Blennow
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Archana Singh-Manoux
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
| | - Claire Paquet
- From the Université de Paris (J.D., S. Sabia, A.S.-M.), Inserm U1153, Epidemiology of Ageing and Neurodegenerative Diseases; Cognitive Neurology Center (J.D., E.C., C.H., M.L., C.P.), Lariboisiere-Fernand Widal Hospital, AP-HP, Université de Paris, France; Department of Psychiatry and Neurochemistry (H.Z., K.B.), University of Gothenburg; Clinical Neurochemistry Laboratory (H.Z., K.B.), Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease (H.Z.), Institute of Neurology, University College London; Dementia Research Institute (H.Z.), London, United Kingdom; Neurochemistry Laboratory (C.E.T., E.A.J.W.), Clinical Chemistry Department, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, the Netherlands; Department of Neurology (B.H.), Cliniques Universitaires Saint-Luc, and Institute of Neuroscience (B.H.), Université Catholique de Louvain, Brussels, Belgium; Gordon Center for Medical Imaging (B.H.), Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston; Research Center and Memory Clinic (A.O., M.B., A.R.), Fundació ACE, Institut Català de Neurciències Aplicades, Universitat International de Catalunya, Barcelona; Centro de Investigación biomédica en Red de Enfermedades Neurodegerenativas (CIBERNED) (A.O., A.R.), Madrid, Spain; Univ. Lille (S. Schraen, T.L., F.P.), CHU Lille, Inserm UMR-S 1172, LilNCog (JPARC)-Lille Neurosciences & Cognition, DISTAlz, LiCEND; Department of Neurology (A.G.), Memory Research and Resources Centre, University of Montpellier; Department of Biochemistry and Molecular Biology (E.B.-A., J.-L.L.), Lariboisière Hospital, APHP, Paris; Department of Biochemistry (S.L.), University of Montpellier, France; Alzheimer Center (P.S.), Department of Neurology, VU University Medical Center, Neuroscience Campus Amsterdam, the Netherlands; and Department of Epidemiology and Public Health (A.S.-M.), University College London, United Kingdom
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Proteomic Discovery and Validation of Novel Fluid Biomarkers for Improved Patient Selection and Prediction of Clinical Outcomes in Alzheimer’s Disease Patient Cohorts. Proteomes 2022; 10:proteomes10030026. [PMID: 35997438 PMCID: PMC9397030 DOI: 10.3390/proteomes10030026] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 07/13/2022] [Accepted: 07/23/2022] [Indexed: 01/25/2023] Open
Abstract
Alzheimer’s disease (AD) is an irreversible neurodegenerative disease characterized by progressive cognitive decline. The two cardinal neuropathological hallmarks of AD include the buildup of cerebral β amyloid (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau. The current disease-modifying treatments are still not effective enough to lower the rate of cognitive decline. There is an urgent need to identify early detection and disease progression biomarkers that can facilitate AD drug development. The current established readouts based on the expression levels of amyloid beta, tau, and phospho-tau have shown many discrepancies in patient samples when linked to disease progression. There is an urgent need to identify diagnostic and disease progression biomarkers from blood, cerebrospinal fluid (CSF), or other biofluids that can facilitate the early detection of the disease and provide pharmacodynamic readouts for new drugs being tested in clinical trials. Advances in proteomic approaches using state-of-the-art mass spectrometry are now being increasingly applied to study AD disease mechanisms and identify drug targets and novel disease biomarkers. In this report, we describe the application of quantitative proteomic approaches for understanding AD pathophysiology, summarize the current knowledge gained from proteomic investigations of AD, and discuss the development and validation of new predictive and diagnostic disease biomarkers.
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Salimi Y, Domingo-Fernández D, Bobis-Álvarez C, Hofmann-Apitius M, Birkenbihl C. ADataViewer: exploring semantically harmonized Alzheimer's disease cohort datasets. Alzheimers Res Ther 2022; 14:69. [PMID: 35598021 PMCID: PMC9123725 DOI: 10.1186/s13195-022-01009-4] [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: 11/26/2021] [Accepted: 04/27/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Currently, Alzheimer's disease (AD) cohort datasets are difficult to find and lack across-cohort interoperability, and the actual content of publicly available datasets often only becomes clear to third-party researchers once data access has been granted. These aspects severely hinder the advancement of AD research through emerging data-driven approaches such as machine learning and artificial intelligence and bias current data-driven findings towards the few commonly used, well-explored AD cohorts. To achieve robust and generalizable results, validation across multiple datasets is crucial. METHODS We accessed and systematically investigated the content of 20 major AD cohort datasets at the data level. Both, a medical professional and a data specialist, manually curated and semantically harmonized the acquired datasets. Finally, we developed a platform that displays vital information about the available datasets. RESULTS Here, we present ADataViewer, an interactive platform that facilitates the exploration of 20 cohort datasets with respect to longitudinal follow-up, demographics, ethnoracial diversity, measured modalities, and statistical properties of individual variables. It allows researchers to quickly identify AD cohorts that meet user-specified requirements for discovery and validation studies regarding available variables, sample sizes, and longitudinal follow-up. Additionally, we publish the underlying variable mapping catalog that harmonizes 1196 unique variables across the 20 cohorts and paves the way for interoperable AD datasets. CONCLUSIONS In conclusion, ADataViewer facilitates fast, robust data-driven research by transparently displaying cohort dataset content and supporting researchers in selecting datasets that are suited for their envisioned study. The platform is available at https://adata.scai.fraunhofer.de/ .
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Affiliation(s)
- Yasamin Salimi
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany.
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany.
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
| | - Carlos Bobis-Álvarez
- University Hospital Ntra. Sra. de Candelaria, Santa Cruz de Tenerife, 38010, Spain
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), 53754, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115, Bonn, Germany
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El-Sappagh S, Saleh H, Ali F, Amer E, Abuhmed T. Two-stage deep learning model for Alzheimer’s disease detection and prediction of the mild cognitive impairment time. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07263-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Deoni SCL, Medeiros P, Deoni AT, Burton P, Beauchemin J, D'Sa V, Boskamp E, By S, McNulty C, Mileski W, Welch BE, Huentelman M. Development of a mobile low-field MRI scanner. Sci Rep 2022; 12:5690. [PMID: 35383255 PMCID: PMC8982311 DOI: 10.1038/s41598-022-09760-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 03/15/2022] [Indexed: 12/23/2022] Open
Abstract
Magnetic resonance imaging (MRI) allows important visualization of the brain and central nervous system anatomy and organization. However, unlike electroencephalography (EEG) or functional near infrared spectroscopy, which can be brought to a patient or study participant, MRI remains a hospital or center-based modality. Low magnetic field strength MRI systems, however, offer the potential to extend beyond these traditional hospital and imaging center boundaries. Here we describe the development of a modified cargo van that incorporates a removable low-field permanent magnet MRI system and demonstrate its proof-of-concept. Using phantom scans and in vivo T2-weighted neuroimaging data, we show no significant differences with respect to geometric distortion, signal-to-noise ratio, or tissue segmentation outcomes in data acquired in the mobile system compared to a similar static system in a laboratory setting. These encouraging results show, for the first time, MRI that can be performed at a participant’s home, community center, school, etc. Breaking traditional barriers of access, this mobile approach may enable imaging of patients and participants who have mobility challenges, live long distances from imaging centers, or are otherwise unable to travel to an imaging center or hospital.
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Affiliation(s)
- Sean C L Deoni
- Advanced Baby Imaging Lab, Rhode Island Hospital, Warren Alpert Medical School at Brown University, Providence, RI, USA. .,Department of Diagnostic Radiology, Warren Alpert Medical School at Brown University, Providence, RI, USA. .,Department of Pediatrics, Warren Alpert Medical School at Brown University, Providence, RI, USA.
| | | | - Alexandra T Deoni
- Advanced Baby Imaging Lab, Rhode Island Hospital, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Phoebe Burton
- Advanced Baby Imaging Lab, Rhode Island Hospital, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Jennifer Beauchemin
- Advanced Baby Imaging Lab, Rhode Island Hospital, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | - Viren D'Sa
- Advanced Baby Imaging Lab, Rhode Island Hospital, Warren Alpert Medical School at Brown University, Providence, RI, USA.,Department of Diagnostic Radiology, Warren Alpert Medical School at Brown University, Providence, RI, USA
| | | | | | | | | | - Brian E Welch
- Hyperfine, Guilford, CT, USA.,Philips North America, Cambridge, MA, USA
| | - Matthew Huentelman
- Neurogenomics Division, Translational Genomics Research Institute, Phoenix, AZ, USA
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Veitch DP, Weiner MW, Aisen PS, Beckett LA, DeCarli C, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Okonkwo O, Perrin RJ, Petersen RC, Rivera‐Mindt M, Saykin AJ, Shaw LM, Toga AW, Tosun D, Trojanowski JQ. Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease. Alzheimers Dement 2022; 18:824-857. [PMID: 34581485 PMCID: PMC9158456 DOI: 10.1002/alz.12422] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 02/06/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020. METHODS We identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion. RESULTS Disease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity. DISCUSSION ADNI has had a profound impact in improving clinical trials for AD.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA,Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of MedicineUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of PsychiatryUniversity of California, San FranciscoSan FranciscoCaliforniaUSA,Department of NeurologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Laurel A. Beckett
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | - Charles DeCarli
- Department of Neurology and Center for NeuroscienceUniversity of California DavisDavisCaliforniaUSA
| | - Robert C. Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Broad Institute, Ariadne Labsand Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health SciencesUniversity of California DavisDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA,Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA,Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA,Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuroimaging, USC Stevens Institute of Neuroimaging and Informatics, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of RadiologyUniversity of California, San FranciscoSan FranciscoCaliforniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Center for Neurodegenerative Research, School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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Sun W, Chang C, Long Q. Graph-guided Bayesian SVM with Adaptive Structured Shrinkage Prior for High-dimensional Data. PROCEEDINGS : ... IEEE INTERNATIONAL CONFERENCE ON BIG DATA. IEEE INTERNATIONAL CONFERENCE ON BIG DATA 2021; 2021:4472-4479. [PMID: 35187547 PMCID: PMC8855458 DOI: 10.1109/bigdata52589.2021.9671712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Support vector machine (SVM) is a popular classification method for the analysis of a wide range of data including big biomedical data. Many SVM methods with feature selection have been developed under the frequentist regularization or Bayesian shrinkage frameworks. On the other hand, the value of incorporating a priori known biological knowledge, such as those from functional genomics and functional proteomics, into statistical analysis of -omic data has been recognized in recent years. Such biological information is often represented by graphs. We propose a novel method that assigns Laplace priors to the regression coefficients and incorporates the underlying graph information via a hyper-prior for the shrinkage parameters in the Laplace priors. This enables smoothing of shrinkage parameters for connected variables in the graph and conditional independence between shrinkage parameters for disconnected variables. Extensive simulations demonstrate that our proposed methods achieve the best performance compared to the other existing SVM methods in terms of prediction accuracy. The proposed method are also illustrated in analysis of genomic data from cancer studies, demonstrating its advantage in generating biologically meaningful results and identifying potentially important features.
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Affiliation(s)
- Wenli Sun
- Dept of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Changgee Chang
- Dept of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, USA
| | - Qi Long
- Dept of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, USA
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Large, open datasets for human connectomics research: Considerations for reproducible and responsible data use. Neuroimage 2021; 244:118579. [PMID: 34536537 DOI: 10.1016/j.neuroimage.2021.118579] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 12/19/2022] Open
Abstract
Large, open datasets have emerged as important resources in the field of human connectomics. In this review, the evolution of data sharing involving magnetic resonance imaging is described. A summary of the challenges and progress in conducting reproducible data analyses is provided, including description of recent progress made in the development of community guidelines and recommendations, software and data management tools, and initiatives to enhance training and education. Finally, this review concludes with a discussion of ethical conduct relevant to analyses of large, open datasets and a researcher's responsibility to prevent further stigmatization of historically marginalized racial and ethnic groups. Moving forward, future work should include an enhanced emphasis on the social determinants of health, which may further contextualize findings among diverse population-based samples. Leveraging the progress to date and guided by interdisciplinary collaborations, the future of connectomics promises to be an impressive era of innovative research, yielding a more inclusive understanding of brain structure and function.
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37
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Kinnunen KM, Mullin AP, Pustina D, Turner EC, Burton J, Gordon MF, Scahill RI, Gantman EC, Noble S, Romero K, Georgiou-Karistianis N, Schwarz AJ. Recommendations to Optimize the Use of Volumetric MRI in Huntington's Disease Clinical Trials. Front Neurol 2021; 12:712565. [PMID: 34744964 PMCID: PMC8569234 DOI: 10.3389/fneur.2021.712565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 09/21/2021] [Indexed: 12/12/2022] Open
Abstract
Volumetric magnetic resonance imaging (vMRI) has been widely studied in Huntington's disease (HD) and is commonly used to assess treatment effects on brain atrophy in interventional trials. Global and regional trajectories of brain atrophy in HD, with early involvement of striatal regions, are becoming increasingly understood. However, there remains heterogeneity in the methods used and a lack of widely-accessible multisite, longitudinal, normative datasets in HD. Consensus for standardized practices for data acquisition, analysis, sharing, and reporting will strengthen the interpretation of vMRI results and facilitate their adoption as part of a pathobiological disease staging system. The Huntington's Disease Regulatory Science Consortium (HD-RSC) currently comprises 37 member organizations and is dedicated to building a regulatory science strategy to expedite the approval of HD therapeutics. Here, we propose four recommendations to address vMRI standardization in HD research: (1) a checklist of standardized practices for the use of vMRI in clinical research and for reporting results; (2) targeted research projects to evaluate advanced vMRI methodologies in HD; (3) the definition of standard MRI-based anatomical boundaries for key brain structures in HD, plus the creation of a standard reference dataset to benchmark vMRI data analysis methods; and (4) broad access to raw images and derived data from both observational studies and interventional trials, coded to protect participant identity. In concert, these recommendations will enable a better understanding of disease progression and increase confidence in the use of vMRI for drug development.
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Affiliation(s)
| | - Ariana P Mullin
- Critical Path Institute, Tucson, AZ, United States.,Wave Life Sciences, Ltd., Cambridge, MA, United States
| | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | | | | | - Mark F Gordon
- Teva Pharmaceuticals, West Chester, PA, United States
| | - Rachael I Scahill
- Huntington's Disease Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - Emily C Gantman
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Simon Noble
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Klaus Romero
- Critical Path Institute, Tucson, AZ, United States
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
| | - Adam J Schwarz
- Takeda Pharmaceuticals, Ltd., Cambridge, MA, United States
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Sinha S, Thomopoulos SI, Lam P, Muir A, Thompson PM. Alzheimer's Disease Classification Accuracy is Improved by MRI Harmonization based on Attention-Guided Generative Adversarial Networks. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2021; 12088:120880L. [PMID: 35340753 PMCID: PMC8952312 DOI: 10.1117/12.2606155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Alzheimer's disease (AD) accounts for 60% of dementia cases worldwide; patients with the disease typically suffer from irreversible memory loss and progressive decline in multiple cognitive domains. With brain imaging techniques such as magnetic resonance imaging (MRI), microscopic brain changes are detectable even before abnormal memory loss is detected clinically. Patterns of brain atrophy can be measured using MRI, which gives us an opportunity to facilitate AD detection using image classification techniques. Even so, MRI scanning protocols and scanners differ across studies. The resulting differences in image contrast and signal to noise make it important to train and test classification models on multiple datasets, and to handle shifts in image characteristics across protocols (also known as domain transfer or domain adaptation). Here, we examined whether adversarial domain adaptation can boost the performance of a Convolutional Neural Network (CNN) model designed to classify AD. To test this, we used an Attention-Guided Generative Adversarial Network (GAN) to harmonize images from three publicly available brain MRI datasets - ADNI, AIBL and OASIS - adjusting for scanner-dependent effects. Our AG-GAN optimized a joint objective function that included attention loss, pixel loss, cycle-consistency loss and adversarial loss; the model was trained bidirectionally in an end-to-end fashion. For AD classification, we adapted the popular 2D AlexNet CNN to handle 3D images. Classification based on harmonized MR images significantly outperformed classification based on the three datasets in non-harmonized form, motivating further work on image harmonization using adversarial techniques.
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Affiliation(s)
- Surabhi Sinha
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Pradeep Lam
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alexandra Muir
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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Sapkota S, Ramirez J, Yhap V, Masellis M, Black SE. Brain atrophy trajectories predict differential functional performance in Alzheimer's disease: Moderations with apolipoprotein E and sex. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12244. [PMID: 34692981 PMCID: PMC8515221 DOI: 10.1002/dad2.12244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 08/09/2021] [Indexed: 12/30/2022]
Abstract
INTRODUCTION We examine whether distinct brain atrophy patterns (using brain parenchymal fraction [BPF]) differentially predict functional performance and decline in Alzheimer's disease (AD), and are independently moderated by (1) a key AD genetic risk marker (apolipoprotein E [APOE]), (2) sex, and (3) high-risk group (women APOE ɛ4 carriers). METHODS We used a 2-year longitudinal sample of AD patients (baseline N = 170; mean age = 71.3 [9.1] years) from the Sunnybrook Dementia Study. We applied latent class analysis, latent growth modeling, and path analysis. We aimed to replicate our findings (N = 184) in the Alzheimer's Disease Neuroimaging Initiative. RESULTS We observed that high brain atrophy class predicted lower functional performance and steeper decline. This association was moderated by APOE, sex, and high-risk group. Baseline findings as moderated by APOE and high-risk group were replicated. DISCUSSION Women APOE ɛ4 carriers may selectively be at a greater risk of functional impairment with higher brain atrophy.
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Affiliation(s)
- Shraddha Sapkota
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Joel Ramirez
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Vanessa Yhap
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
| | - Mario Masellis
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
- Department of Medicine (Neurology)University of TorontoTorontoOntarioCanada
| | - Sandra E. Black
- Hurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreTorontoOntarioCanada
- Department of Medicine (Neurology)University of TorontoTorontoOntarioCanada
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Nezhadmoghadam F, Martinez-Torteya A, Treviño V, Martínez E, Santos A, Tamez-Peña J, Alzheimer's Disease Neuroimaging Initiative. Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling. Curr Alzheimer Res 2021; 18:595-606. [PMID: 34488612 DOI: 10.2174/1567205018666210831145825] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 05/30/2021] [Accepted: 06/30/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Alzheimer's Disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills. The ability to correctly predict the diagnosis of Alzheimer's disease in its earliest stages can help physicians make more informed clinical decisions on therapy plans. OBJECTIVE This study aimed to determine whether the unsupervised discovering of latent classes of subjects with Mild Cognitive Impairment (MCI) may be useful in finding different prodromal AD stages and/or subjects with a low MCI to AD conversion risk. METHODS Total 18 features relevant to the MCI to AD conversion process led to the identification of 681 subjects with early MCI. Subjects were divided into training (70%) and validation (30%) sets. Subjects from the training set were analyzed using consensus clustering, and Gaussian Mixture Models (GMM) were used to describe the latent classes. The discovered GMM predicted the latent class of the validation set. Finally, descriptive statistics, rates of conversion, and Odds Ratios (OR) were computed for each discovered class. RESULTS Through consensus clustering, we discovered three different clusters among MCI subjects. The three clusters were associated with low-risk (OR = 0.12, 95%CI = 0.04 to 0.3|), medium-risk (OR = 1.33, 95%CI = 0.75 to 2.37), and high-risk (OR = 3.02, 95%CI = 1.64 to 5.57) of converting from MCI to AD, with the high-risk and low-risk groups highly contrasting. Hence, prodromal AD subjects were present in only two clusters. CONCLUSION We successfully discovered three different latent classes among MCI subjects with varied risks of MCI-to-AD conversion through consensus clustering. Two of the discovered classes may represent two different prodromal presentations of Alzheimer´s disease.
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Affiliation(s)
- Fahimeh Nezhadmoghadam
- Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey, N.L., 64849, Mexico
| | - Antonio Martinez-Torteya
- Universidad de Monterrey, School of Engineering and Technologies, Av. Ignacio Morones Prieto 4500, San Pedro Garza García 66238, Mexico
| | - Victor Treviño
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Emmanuel Martínez
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Alejandro Santos
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
| | - Jose Tamez-Peña
- Tecnologico de Monterrey, School of Medicine and Health Sciences, Ave. Ignacio Morones Prieto 3000, Sertoma, Monterrey, N.L, 64710, Mexico
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Lin FV, Heffner K, Gevirtz R, Zhang Z, Tadin D, Porsteinsson A. Targeting autonomic flexibility to enhance cognitive training outcomes in older adults with mild cognitive impairment: study protocol for a randomized controlled trial. Trials 2021; 22:560. [PMID: 34425878 PMCID: PMC8381519 DOI: 10.1186/s13063-021-05530-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 08/09/2021] [Indexed: 11/23/2022] Open
Abstract
Importance Cognitive training with components that can further enhance the transferred and long-term effects and slow the progress of dementia is needed for preventing dementia. Objective The goal of the study is to test whether improving autonomic nervous system (ANS) flexibility via a resonance frequency breathing (RFB) training will strengthen the effects of a visual speed of processing (VSOP) cognitive training on cognitive and brain function, and slow the progress of dementia in older adults with mild cognitive impairment (MCI). Design Stage II double-blinded randomized controlled trial. The study was prospectively registered at ClinicalTrials.gov, with registration approved on 21 August 2020 (No. NCT04522791). Setting Study-related appointments will be conducted on-site at University of Rochester Medical Center locations. Data collection will be conducted from August 2020 to February 2025. Participants Older adults with MCI (n = 114) will be randomly assigned to an 8-week combined intervention (RFB+VSOP), VSOP with guided imagery relaxation (IR) control, and a IR-only control, with periodical booster training sessions at follow-ups. Mechanistic and distal outcomes include ANS flexibility, measured by heart rate variability, and multiple markers of dementia progress. Data will be collected across a 14-month period. Discussion This will be among the first RCTs to examine in older persons with MCI a novel, combined intervention targeting ANS flexibility, an important contributor to overall environmental adaptation, with an ultimate goal for slowing neurodegeneration. Trial registration ClinicalTrials.gov NCT04522791. Registered on 21 August 2020 Protocol version: STUDY00004727; IRB protocol version 2, approved on 30 July 2020.
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Affiliation(s)
- Feng V Lin
- Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, USA.,Wu Tsai Neuroscience Institute, Stanford University, Stanford, USA.,Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, Rochester, USA.,Department of Brain and Cognitive Sciences, University of Rochester, Rochester, USA
| | - Kathi Heffner
- Elaine C. Hubbard Center for Nursing Research on Aging, School of Nursing, University of Rochester Medical Center, Rochester, USA. .,Department of Medicine, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, USA. .,Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, USA.
| | | | - Zhengwu Zhang
- University of North Carolina-Chapel Hill, Chapel Hill, USA
| | - Duje Tadin
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, USA.,Department of Neuroscience, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, USA
| | - Anton Porsteinsson
- Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, Rochester, USA
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Kaur H, Felix MR, Liang C, Mukherjee J. Development and evaluation of [ 18F]Flotaza for Aβ plaque imaging in postmortem human Alzheimer's disease brain. Bioorg Med Chem Lett 2021; 46:128164. [PMID: 34082067 PMCID: PMC8379902 DOI: 10.1016/j.bmcl.2021.128164] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 05/20/2021] [Accepted: 05/27/2021] [Indexed: 11/30/2022]
Abstract
Positron emission tomographic (PET) studies of amyloid β (Aβ) accumulation in Alzheimer's disease (AD) have shown clinical utility. The aim of this study was to develop and evaluate the effectiveness of a new fluorine-18 radiotracer [18F]Flotaza (2-{2-[2-[18F]fluoroethoxy]ethoxy}ethoxy)-4'-N,N-dimethylaminoazobenzene), for Aβ plaque imaging. Nucleophilic [18F]fluoride was used in a one-step radiosynthesis for [18F]flotaza. Using post mortem human AD brain tissues consisting of anterior cingulate (AC) and corpus callosum (CC), binding affinity of Flotaza, Ki = 1.68 nM for human Aβ plaques and weak (>10-5 M) for Tau protein. Radiosynthesis of [18F]Flotaza was very efficient in high radiochemical yields (>25%) with specific activities >74 GBq/μmol. Brain slices from all AD subjects were positively immunostained with anti-Aβ. Ratio of [18F]Flotaza in gray matter AC to white matter CC was >100 in all the 6 subjects. Very little white matter binding was seen. [18F]Flotaza binding in AC strongly correlated with anti-Aβ immunostains. [18F]Flotaza is therefore a suitable fluorine-18 PET radiotracer for PET imaging studies of human Aβ plaques.
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Affiliation(s)
- Harsimran Kaur
- Preclinical Imaging, Department of Radiological Sciences, University of California, Irvine 92697, USA
| | - Megan R Felix
- Preclinical Imaging, Department of Radiological Sciences, University of California, Irvine 92697, USA
| | - Christopher Liang
- Preclinical Imaging, Department of Radiological Sciences, University of California, Irvine 92697, USA
| | - Jogeshwar Mukherjee
- Preclinical Imaging, Department of Radiological Sciences, University of California, Irvine 92697, USA.
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Prakash J, Wang V, Quinn RE, Mitchell CS. Unsupervised Machine Learning to Identify Separable Clinical Alzheimer's Disease Sub-Populations. Brain Sci 2021; 11:977. [PMID: 34439596 PMCID: PMC8392842 DOI: 10.3390/brainsci11080977] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/10/2021] [Accepted: 07/20/2021] [Indexed: 11/20/2022] Open
Abstract
Heterogeneity among Alzheimer's disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and association rule mining (ARM) was performed on the ADNIMERGE dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Patient sociodemographics, brain imaging, biomarkers, cognitive tests, and medication usage were included for analysis. Four AD clinical sub-populations were identified using between-cluster mean fold changes [cognitive performance, brain volume]: cluster-1 represented least severe disease [+17.3, +13.3]; cluster-0 [-4.6, +3.8] and cluster-3 [+10.8, -4.9] represented mid-severity sub-populations; cluster-2 represented most severe disease [-18.4, -8.4]. ARM assessed frequently occurring pharmacologic substances within the 4 sub-populations. No drug class was associated with the least severe AD (cluster-1), likely due to lesser antecedent disease. Anti-hyperlipidemia drugs associated with cluster-0 (mid-severity, higher volume). Interestingly, antioxidants vitamin C and E associated with cluster-3 (mid-severity, higher cognition). Anti-depressants like Zoloft associated with most severe disease (cluster-2). Vitamin D is protective for AD, but ARM identified significant underutilization across all AD sub-populations. Identification and feature characterization of four distinct AD sub-population "clusters" using standard clinical features enhances future clinical trial selection criteria and cross-study comparative analysis.
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Affiliation(s)
- Jayant Prakash
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
- Department of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Velda Wang
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
| | - Robert E. Quinn
- Laboratory for Pathology Dynamics, Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA 30332, USA; (J.P.); (V.W.); (R.E.Q.III)
- Department of Computer Science, 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; (J.P.); (V.W.); (R.E.Q.III)
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Hsieh TJ, Lee WJ, Liao YC, Hsu CC, Fang YH, Chen TY, Lin YS, Chang IS, Wang SJ, Hsiung CA, Fuh JL. Association between Alzheimer's disease genes and trajectories of cognitive function decline in Han Chinese in Taiwan. Aging (Albany NY) 2021; 13:17237-17252. [PMID: 34214049 PMCID: PMC8312434 DOI: 10.18632/aging.203204] [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: 10/05/2020] [Accepted: 06/08/2021] [Indexed: 01/01/2023]
Abstract
Genetic background has been considered one of the important contributors to the rate of cognitive decline among patients with Alzheimer’s disease (AD). We conducted a 4-year longitudinal follow-up study, recruited 255 AD and 44 mild cognitive impairment (MCI) patients, and used a data-driven trajectory analysis to examine the influence of selected AD risk genes on the age for and the rate of cognitive decline in Han Chinese population. Genotyping of selected single-nucleotide polymorphisms in the APOE, ABCA7, SORL1, BIN1, GAB2, and CD33 genes was conducted, and a Bayesian hierarchical model was fitted to analyze the trajectories of cognitive decline among different genotypes. After adjusting for sex and education years, the APOE ε4 allele was associated with an earlier mean change of −2.39 years in the age at midpoint of cognitive decline, the G allele in ABCA7 rs3764650 was associated with an earlier mean change of −1.75 years, and the T allele in SORL1 rs3737529 was associated with a later mean change of 2.6 years. Additionally, the rate of cognitive decline was associated with the APOE ε4 allele and SORL1 rs3737529. In summary, APOE and SORL1 might be the most important genetic factors related to cognitive decline in Han Chinese population.
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Affiliation(s)
- Tsung-Jen Hsieh
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan.,School of Medicine, I-Shou University, Kaohsiung, Taiwan
| | - Wei-Ju Lee
- Neurological Institute, Taichung Veterans General Hospital, Taichung, Taiwan.,Faculty of Medicine, National Yang-Ming University Schools of Medicine, Taipei, Taiwan.,Dementia Center, Taichung Veterans General Hospital, Taichung, Taiwan.,Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Yi-Chu Liao
- Faculty of Medicine, National Yang-Ming University Schools of Medicine, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chih-Cheng Hsu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Yao-Hwei Fang
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Tzu-Yu Chen
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Yung-Shuan Lin
- Faculty of Medicine, National Yang-Ming University Schools of Medicine, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Miaoli, Taiwan
| | - Shuu-Jiun Wang
- Faculty of Medicine, National Yang-Ming University Schools of Medicine, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Chao A Hsiung
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan
| | - Jong-Ling Fuh
- Faculty of Medicine, National Yang-Ming University Schools of Medicine, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan
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45
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Yashin AI, Wu D, Arbeev K, Bagley O, Akushevich I, Duan M, Yashkin A, Ukraintseva S. Interplay between stress-related genes may influence Alzheimer's disease development: The results of genetic interaction analyses of human data. Mech Ageing Dev 2021; 196:111477. [PMID: 33798591 PMCID: PMC8173104 DOI: 10.1016/j.mad.2021.111477] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 03/22/2021] [Accepted: 03/23/2021] [Indexed: 01/05/2023]
Abstract
Emerging evidence from experimental and clinical research suggests that stress-related genes may play key roles in AD development. The fact that genome-wide association studies were not able to detect a contribution of such genes to AD indicates the possibility that these genes may influence AD non-linearly, through interactions of their products. In this paper, we selected two stress-related genes (GCN2/EIF2AK4 and APP) based on recent findings from experimental studies which suggest that the interplay between these genes might influence AD in humans. To test this hypothesis, we evaluated the effects of interactions between SNPs in these two genes on AD occurrence, using the Health and Retirement Study data on white indidividuals. We found several interacting SNP-pairs whose associations with AD remained statistically significant after correction for multiple testing. These findings emphasize the importance of nonlinear mechanisms of polygenic AD regulation that cannot be detected in traditional association studies. To estimate collective effects of multiple interacting SNP-pairs on AD, we constructed a new composite index, called Interaction Polygenic Risk Score, and showed that its association with AD is highly statistically significant. These results open a new avenue in the analyses of mechanisms of complex multigenic AD regulation.
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Affiliation(s)
| | - Deqing Wu
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | | | - Olivia Bagley
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | - Igor Akushevich
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | - Matt Duan
- Biodemography of Aging Research Unit, Duke University SSRI, USA
| | - Arseniy Yashkin
- Biodemography of Aging Research Unit, Duke University SSRI, USA
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46
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Kraguljac NV, McDonald WM, Widge AS, Rodriguez CI, Tohen M, Nemeroff CB. Neuroimaging Biomarkers in Schizophrenia. Am J Psychiatry 2021; 178:509-521. [PMID: 33397140 PMCID: PMC8222104 DOI: 10.1176/appi.ajp.2020.20030340] [Citation(s) in RCA: 110] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Schizophrenia is a complex neuropsychiatric syndrome with a heterogeneous genetic, neurobiological, and phenotypic profile. Currently, no objective biological measures-that is, biomarkers-are available to inform diagnostic or treatment decisions. Neuroimaging is well positioned for biomarker development in schizophrenia, as it may capture phenotypic variations in molecular and cellular disease targets, or in brain circuits. These mechanistically based biomarkers may represent a direct measure of the pathophysiological underpinnings of the disease process and thus could serve as true intermediate or surrogate endpoints. Effective biomarkers could validate new treatment targets or pathways, predict response, aid in selection of patients for therapy, determine treatment regimens, and provide a rationale for personalized treatments. In this review, the authors discuss a range of mechanistically plausible neuroimaging biomarker candidates, including dopamine hyperactivity, N-methyl-d-aspartate receptor hypofunction, hippocampal hyperactivity, immune dysregulation, dysconnectivity, and cortical gray matter volume loss. They then focus on the putative neuroimaging biomarkers for disease risk, diagnosis, target engagement, and treatment response in schizophrenia. Finally, they highlight areas of unmet need and discuss strategies to advance biomarker development.
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Affiliation(s)
- Nina V. Kraguljac
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL,Corresponding Author: Nina Vanessa Kraguljac, MD, Department of Psychiatry and Behavioral Neurobiology, The University of Alabama at Birmingham, SC 501, 1720 7th Ave S, Birmingham, AL 35294-0017, 205-996-7171,
| | - William M. McDonald
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine
| | - Alik S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN
| | - Carolyn I. Rodriguez
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA,Veterans Affairs Palo Alto Health Care System, Palo Alto, CA
| | - Mauricio Tohen
- Department of Psychiatry and Behavioral Sciences, University of New Mexico Health Sciences Center, Albuquerque, NM, USA
| | - Charles B. Nemeroff
- Department of Psychiatry, University of Texas Dell Medical School, Austin, TX
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47
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Ji Z, Liu C, Zhao W, Soto C, Zhou X. Multi-scale modeling for systematically understanding the key roles of microglia in AD development. Comput Biol Med 2021; 133:104374. [PMID: 33864975 DOI: 10.1016/j.compbiomed.2021.104374] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 11/30/2022]
Abstract
Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States. Unfortunately, current therapies are largely palliative and several potential drug candidates have failed in late-stage clinical trials. Studies suggest that microglia-mediated neuroinflammation might be responsible for the failures of various therapies. Microglia contribute to Aβ clearance in the early stage of neurodegeneration and may contribute to AD development at the late stage by releasing pro-inflammatory cytokines. However, the activation profile and phenotypic changes of microglia during the development of AD are poorly understood. To systematically understand the key role of microglia in AD progression and predict the optimal therapeutic strategy in silico, we developed a 3D multi-scale model of AD (MSMAD) by integrating multi-level experimental data, to manipulate the neurodegeneration in a simulated system. Based on our analysis, we revealed that how TREM2-related signal transduction leads to an imbalance in the activation of different microglia phenotypes, thereby promoting AD development. Our MSMAD model also provides an optimal therapeutic strategy for improving the outcome of AD treatment.
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Affiliation(s)
- Zhiwei Ji
- College of Artificial Intelligence, Nanjing Agricultural University, No.1 Weigang Road, Nanjing, Jiangsu, 210095, China; School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, 77030, USA.
| | - Changan Liu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, 77030, USA
| | - Weiling Zhao
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, 77030, USA
| | - Claudio Soto
- Mitchell Center for Alzheimer's Disease & Brain Disorder, Department of Neurology, The University of Texas McGovern Medical School, 6431 Fannin Street, Houston, TX, 77030, USA
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Houston, TX, 77030, USA.
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48
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Robert C, Wilson CS, Lipton RB, Arreto CD. Evolution of the Research Literature and the Scientific Community of Alzheimer's Disease from 1983-2017: A 35-Year Survey. J Alzheimers Dis 2021; 75:1105-1134. [PMID: 32390624 DOI: 10.3233/jad-191281] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
This study surveys the development of Alzheimer's disease (AD) in the research literature, the scientific community, and the journals containing AD papers over a 35-year period. Research papers on AD published from 1983 to 2017 in journals indexed in the Web of Science were analyzed in seven five-year periods. The number of AD papers increased from 1,095 in 1983-1987 to 50,532 by 2013-2017 and in the same time period, the number of participating countries went from 27 to 152. The US was the most prolific country throughout, followed by several European countries, Canada, Australia, and Japan. Asian countries have emerged and by 2013-2017, China surpassed all but the US in productivity. Countries in Latin America and Africa have also contributed to AD research. Additionally, several new non-governmental institutions (e.g., ADNI, ADI) have emerged and now play a key role in the fight against AD. Likewise the AD scientific publishing universe evolved in various aspects: an increase in number of journals containing AD papers (227 journals in 1983-1987 to 3,257 in 2013-2017); appearance of several AD-focused journals, e.g., Alzheimer's & Dementia, Journal of Alzheimer's Disease; and the development of special issues dedicated to AD. Our paper complements the numerous extant papers on theoretical and clinical aspects of AD and provides a description of the research landscape of the countries and journals contributing papers related to AD.
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Affiliation(s)
- Claude Robert
- Université Paris Descartes, Paris, France.,Gliaxone, Saint Germain Sous Doue, France
| | - Concepción S Wilson
- Formerly at: School of Information Systems, Technology and Management, University of New South Wales, UNSW Sydney, Australia
| | - Richard B Lipton
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Charles-Daniel Arreto
- Gliaxone, Saint Germain Sous Doue, France.,Université Paris Descartes, Faculté de Chirurgie Dentaire, Hôpital Bretonneau, HUPNVS, AP-HP, Paris, France
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49
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Swedlow JR, Kankaanpää P, Sarkans U, Goscinski W, Galloway G, Malacrida L, Sullivan RP, Härtel S, Brown CM, Wood C, Keppler A, Paina F, Loos B, Zullino S, Longo DL, Aime S, Onami S. A global view of standards for open image data formats and repositories. Nat Methods 2021; 18:1440-1446. [PMID: 33948027 DOI: 10.1038/s41592-021-01113-7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Jason R Swedlow
- Divisions of Computational Biology and Gene Regulation and Expression, School of Life Sciences, University of Dundee, Dundee, UK.
| | - Pasi Kankaanpää
- Turku BioImaging, Åbo Akademi University and University of Turku, Turku, Finland.,Euro-BioImaging ERIC, Turku, Finland
| | - Ugis Sarkans
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge, UK
| | - Wojtek Goscinski
- Monash eResearch Centre, Monash University, Melbourne, Victoria, Australia
| | - Graham Galloway
- National Imaging Facility, The University of Queensland, Brisbane, Queensland, Australia
| | - Leonel Malacrida
- Advanced Bioimaging Unit, Institut Pasteur Montevideo and Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
| | - Ryan P Sullivan
- Microscopy Australia, The University of Sydney, Sydney, Australia
| | - Steffen Härtel
- National Center for Health Information Systems (CENS), Center for Medical Informatics and Telemedicine (CIMT), and Biomedical Neuroscience Institute (BNI), Faculty of Medicine, University of Chile, Santiago, Chile
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University and Canada BioImaging, Montreal, Quebec, Canada
| | - Christopher Wood
- Laboratorio Nacional de Microscopía Avanzada, Instituto de Biotecnología, Universidad Nacional Autónoma de México, Cuernavaca, México
| | - Antje Keppler
- Euro-BioImaging Bio-Hub, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Federica Paina
- Department of Physiological Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Ben Loos
- Department of Physiological Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Sara Zullino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Torino, Italy
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
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50
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Tate DF, Dennis EL, Adams JT, Adamson MM, Belanger HG, Bigler ED, Bouchard HC, Clark AL, Delano-Wood LM, Disner SG, Eapen BC, Franz CE, Geuze E, Goodrich-Hunsaker NJ, Han K, Hayes JP, Hinds SR, Hodges CB, Hovenden ES, Irimia A, Kenney K, Koerte IK, Kremen WS, Levin HS, Lindsey HM, Morey RA, Newsome MR, Ollinger J, Pugh MJ, Scheibel RS, Shenton ME, Sullivan DR, Taylor BA, Troyanskaya M, Velez C, Wade BS, Wang X, Ware AL, Zafonte R, Thompson PM, Wilde EA. Coordinating Global Multi-Site Studies of Military-Relevant Traumatic Brain Injury: Opportunities, Challenges, and Harmonization Guidelines. Brain Imaging Behav 2021; 15:585-613. [PMID: 33409819 PMCID: PMC8035292 DOI: 10.1007/s11682-020-00423-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/24/2020] [Indexed: 12/19/2022]
Abstract
Traumatic brain injury (TBI) is common among military personnel and the civilian population and is often followed by a heterogeneous array of clinical, cognitive, behavioral, mood, and neuroimaging changes. Unlike many neurological disorders that have a characteristic abnormal central neurologic area(s) of abnormality pathognomonic to the disorder, a sufficient head impact may cause focal, multifocal, diffuse or combination of injury to the brain. This inconsistent presentation makes it difficult to establish or validate biological and imaging markers that could help improve diagnostic and prognostic accuracy in this patient population. The purpose of this manuscript is to describe both the challenges and opportunities when conducting military-relevant TBI research and introduce the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Military Brain Injury working group. ENIGMA is a worldwide consortium focused on improving replicability and analytical power through data sharing and collaboration. In this paper, we discuss challenges affecting efforts to aggregate data in this patient group. In addition, we highlight how "big data" approaches might be used to understand better the role that each of these variables might play in the imaging and functional phenotypes of TBI in Service member and Veteran populations, and how data may be used to examine important military specific issues such as return to duty, the late effects of combat-related injury, and alteration of the natural aging processes.
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Affiliation(s)
- David F Tate
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA.
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA.
| | - Emily L Dennis
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
| | - John T Adams
- Western University of Health Sciences, Pomona, CA, USA
| | - Maheen M Adamson
- Defense and Veterans Brain Injury Center, VA Palo Alto, Palo Alto, CA, USA
- Neurosurgery, Stanford School of Medicine, Stanford, CA, USA
| | - Heather G Belanger
- United States Special Operations Command (USSOCOM), Tampa, FL, USA
- Department of Psychology, University of South Florida, Tampa, FL, USA
- Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA
- St Michaels Inc, Tampa, FL, USA
| | - Erin D Bigler
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
- Neuroscience Center, Brigham Young University, Provo, UT, USA
| | - Heather C Bouchard
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
| | - Alexandra L Clark
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
| | - Lisa M Delano-Wood
- VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, USA
| | - Seth G Disner
- Department of Psychiatry, University of Minnesota Medical School, Minneapolis, MN, USA
- Minneapolis VA Health Care System, Minneapolis, MN, USA
| | - Blessen C Eapen
- Department of Physical Medicine and Rehabilitation, VA Greater Los Angeles Health Care System, Los Angeles, CA, USA
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Carol E Franz
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Elbert Geuze
- University Medical Center Utrecht, Utrecht, Netherlands
- Brain Research and Innovation Centre, Ministry of Defence, Utrecht, The Netherlands
| | - Naomi J Goodrich-Hunsaker
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
| | - Kihwan Han
- School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, USA
| | - Jasmeet P Hayes
- Psychology Department, The Ohio State University, Columbus, OH, USA
- Chronic Brain Injury Program, The Ohio State University, Columbus, OH, USA
| | - Sidney R Hinds
- Department of Defense/United States Army Medical Research and Materiel Command, Fort Detrick, Frederick, MD, USA
- Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Cooper B Hodges
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
| | - Elizabeth S Hovenden
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Andrei Irimia
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Kimbra Kenney
- Department of Neurology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Inga K Koerte
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - William S Kremen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, La Jolla, CA, USA
- Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - Harvey S Levin
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Hannah M Lindsey
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- Department of Psychology, Brigham Young University, Provo, UT, USA
| | - Rajendra A Morey
- Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, NC, USA
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Mary R Newsome
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - John Ollinger
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Mary Jo Pugh
- Information Decision-Enhancement and Analytic Sciences Center, VA Salt Lake City, Salt Lake City, UT, USA
- Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Randall S Scheibel
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Boston, MA, USA
- Brockton Division, VA Boston Healthcare System, Brockton, MA, USA
| | - Danielle R Sullivan
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Brian A Taylor
- Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA, USA
- C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, USA
| | - Maya Troyanskaya
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
- Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Carmen Velez
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
| | - Benjamin Sc Wade
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Xin Wang
- Department of Psychiatry, University of Toledo, Toledo, OH, USA
| | - Ashley L Ware
- Department of Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Ross Zafonte
- Department of Physical Medicine and Rehabilitation, Massachusetts General Hospital/Brigham & Women's Hospital, Boston, MA, USA
- Spaulding Rehabilitation Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USC, Marina del Rey, Los Angeles, CA, USA
- Department of Neurology, USC, Los Angeles, CA, USA
- Department of Pediatrics, USC, Los Angeles, CA, USA
- Department of Psychiatry, USC, Los Angeles, CA, USA
- Department of Radiology, USC, Los Angeles, CA, USA
- Department of Engineering, USC, Los Angeles, CA, USA
- Department of Ophthalmology, USC, Los Angeles, CA, USA
| | - Elisabeth A Wilde
- Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT, USA
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT, USA
- H. Ben Taub Department of Physical Medicine and Rehabilitation, Baylor College of Medicine, Houston, TX, USA
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