ArticlemedRxiv : the preprint server for health sciences2023
Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.
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Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1citing papers in PubMed
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Who cites it
1 citing paper in PubMed.
- GPT-4 Turbo with Vision fails to outperform text-only GPT-4 Turbo in the Japan Diagnostic Radiology Board Examination.Japanese journal of radiology · 2024Article
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Authors and funding
53 authors.
Zhijian YangArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Junhao WenLaboratory of AI and Biomedical Science (LABS), Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA.
Guray ErusArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Sindhuja T GovindarajanArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Randa MelhemArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Elizabeth MamourianArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Yuhan CuiArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Dhivya SrinivasanArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ahmed AbdulkadirLaboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland.
Paraskevi ParmpiArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Katharina WittfeldDepartment of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.
Hans J GrabeDepartment of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.
Robin BülowInstitute of Diagnostic Radiology and Neuroradiology, University of Greifswald, Germany.
Stefan FrenzelDepartment of Psychiatry and Psychotherapy, University Medicine Greifswald, Germany.
Duygu TosunDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Murat BilgelLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Yang AnLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Dahyun YiInstitute of Human Behavioral Medicine, Medical Research Center Seoul National University, Seoul, Republic of Korea.
Daniel S MarcusMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Pamela LaMontagneMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Tammie L S BenzingerMallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
Susan R HeckbertCardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA.
Thomas R AustinCardiovascular Health Research Unit and Department of Epidemiology, University of Washington, Seattle, WA, USA.
Shari R WaldsteinDepartment of Psychology, University of Maryland, Baltimore County, Catonsville, MD, USA.
Michele K EvansHealth Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA.
Alan B ZondermanHealth Disparities Research Section, Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA.
Lenore J LaunerNeuroepidemiology Section, Intramural Research Program, National Institute on Aging, Bethesda, Maryland, USA.
Aristeidis SotirasDepartment of Radiology and Institute of Informatics, Washington University in St. Luis, St. Luis, MO63110, USA.
Mark A EspelandDepartment of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Colin L MastersFlorey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia.
Paul MaruffFlorey Institute, The University of Melbourne, Parkville, VIC, 3052, Australia.
Jurgen FrippCSIRO Health and Biosecurity, Australian e-Health Research Centre CSIRO, Brisbane, Queensland, Australia.
Arthur TogaLaboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, California, USA.
Sid O'BryantInstitute for Translational Research University of North Texas Health Science Center Fort Worth Texas USA.
Mallar M ChakravartyComputational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Center, Douglas Mental Health University Institute, McGill University, Verdun, Quebec, Canada.
Sylvia VilleneuveMcConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
Sterling C JohnsonWisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, USA.
John C MorrisKnight Alzheimer Disease Research Center, Washington University in St. Louis, St. Louis, MO, USA.
Marilyn S AlbertDepartment of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Kristine YaffeDepartments of Neurology, Psychiatry and Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
Henry VölzkeInstitute for Community Medicine, University Medicine Greifswald, Greifswald, Germany.
Luigi FerrucciTranslational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA.
Nick R BryanDepartment of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Russell T ShinoharaArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Yong FanArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Mohamad HabesBiggs Alzheimer's Institute, University of Texas San Antonio Health Science Center, USA.
Paris Alexandros LalousisDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
Nikolaos KoutsoulerisDepartment of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.
David A WolkDepartment of Neurology, University of Pennsylvania, Philadelphia, PA, USA.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
Haochang ShouArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Ilya M NasrallahArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Christos DavatzikosArtificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Funding
Project 2 MeasurementU19AG033655 · NIA · JOHNS HOPKINS UNIVERSITY · PI Abhay Moghekar · 2014 to 2026
$51.5MHealth and Aging Brain among Latino Elders (HABLE-AT(N)) StudyR01AG058533 · NIA · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI JOHNSON, LEIGH A, O'BRYANT, SID E · 2020 to 2025
$45.4MMVP Data Integration into the ADSP Phenotype Harmonization ConsortiumU24AG074855 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CUCCARO, MICHAEL L, HOHMAN, TIMOTHY J · 2021 to 2025
$37.5MResearch Education ComponentP30AG072979 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVID A WOLK · 2021 to 2026
$24.8MWake Forest University School of Medicine Alzheimer's Disease Research CenterP30AG072947 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SUZANNE CRAFT · 2021 to 2026
$24.3MUltrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7MHealth Disparities in Alzheimer's Disease and Mild Cognitive Impairment among Mexican AmericansR01AG054073 · NIA · UNIVERSITY OF NORTH TEXAS HLTH SCI CTR · PI O'BRYANT, SID E, TOGA, ARTHUR W · 2017 to 2021
$12.6MHealthy Aging Neighborhoods of Diversity Across LifespanZ01AG000513 · NIA · NATIONAL INSTITUTE ON AGING · PI EVANS, MICHELE K · 2001 to 2008
$6.8MMachine Learning and Large-scale Imaging analytics for dimensional representations of brain trajectories in aging and preclinical Alzheimer's Disease: The brain aging chart and the iSTAGING consortiumRF1AG054409 · NIA · UNIVERSITY OF PENNSYLVANIA · PI DAVATZIKOS, CHRISTOS · 2017 to 2023
$6.3MPersonalized Functional Network Modeling to Characterize and Predict Psychopathology in YouthR01EB022573 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI Yong Fan, Theodore Satterthwaite · 2016 to 2026
$6.0MThe Neuroimaging Brain Chart Software SuiteU24NS130411 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI Christos Davatzikos, Yong Fan · 2023 to 2026
$3.7MMultiethnic machine learning brain signatures of ADRDR01AG080821 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI HABES, MOHAMAD · 2022 to 2025
$2.9MIntramural NIH HHS Z01 AG000513NIA NIH HHS P30 AG072947NIA NIH HHS P30 AG072979NIA NIH HHS R01 AG054073NIA NIH HHS R01 AG058533NIA NIH HHS R01 AG080821NIA NIH HHS RF1 AG054409NIA NIH HHS U01 AG068057NIA NIH HHS U19 AG033655NIA NIH HHS U24 AG074855NIBIB NIH HHS R01 EB022573NIDA NIH HHS HHSN271201600059CNINDS NIH HHS U24 NS130411
Abstract
Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.
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