Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals

作者：Ioanna Skampardoni, Ilya M. Nasrallah, Ahmed Abdulkadir, Junhao Wen, Randa Melhem, Elizabeth Mamourian, Güray Erus, Jimit Doshi, Ashish Singh, Zhijian Yang, Yuhan Cui, Gyujoon Hwang, Zheng Ren, Raymond Pomponio, Dhivya Srinivasan, Sindhuja Tirumalai Govindarajan, Paraskevi Parmpi, Katharina Wittfeld, Hans J. Grabe, Robin Bülow, Stefan Frenzel, Duygu Tosun, Murat Bilgel, Yang An, Daniel S. Marcus, Pamela LaMontagne, Susan R. Heckbert, Thomas R. Austin, Lenore J. Launer, Aristeidis Sotiras, Mark A. Espeland, Colin L. Masters, Paul Maruff, Jürgen Fripp, Sterling C. Johnson, John C. Morris, Marilyn S. Albert, R. Nick Bryan, Kristine Yaffe, Henry Völzke, Luigi Ferrucci, Tammie L.S. Benzinger, Ali Ezzati, Russell T. Shinohara, Yong Fan, Susan M. Resnick, Mohamad Habes, David A. Wolk, Haochang Shou, Konstantina S. Nikita, Christos Davatzikos · 发表于：JAMA Psychiatry · 年份：2024 · DOI：10.1001/jamapsychiatry.2023.5599 · 被引用次数：26 · 研究领域：Dementia and Cognitive Impairment Research、Functional Brain Connectivity Studies、Machine Learning in Healthcare

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid β (Aβ), and future cognitive decline were assessed. Resul...