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Interpretable MRI-Based Deep Learning for Alzheimer's Risk and Progression

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机构： [1]State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. [2]Department of Psychology, University of the Chinese Academy of Sciences, Beijing 100049, China. [3]Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, 6525 EN, the Netherlands. [4]Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. [5]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China. [6]Department of Radiology and Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing 100053, China. [7]Department of Neurology, Beijing Chaoyang Hospital, Capital Medical University, No.8 Gongti South Road, Chaoyang District, Beijing 100020, China. [8]Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA. [9]Imaging Genetics Center, Mark & Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA. [10]State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, Hainan 572025, China. [11]The Institute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China. [12]National Clinical Research Center for Geriatric Diseases, Beijing 100053, China. [13]The Central Hospital of Karamay, Xinjiang 834000, China. [14]Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China.

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Timely intervention for Alzheimer's disease (AD) requires early detection. The development of immunotherapies targeting amyloid-beta and tau underscores the need for accessible, time-efficient biomarkers for early diagnosis. Here, we directly applied our previously developed MRI-based deep learning model for AD to the large Chinese SILCODE cohort (722 participants, 1,105 brain MRI scans). The model - initially trained on North American data - demonstrated robust cross-ethnic generalization, without any retraining or fine-tuning, achieving an AUC of 91.3% in AD classification with a sensitivity of 95.2%. It successfully identified 86.7% of individuals at risk of AD progression more than 5 years in advance. Individuals identified as high-risk exhibited significantly shorter median progression times. By integrating an interpretable deep learning brain risk map approach, we identified AD brain subtypes, including an MCI subtype associated with rapid cognitive decline. The model's risk scores showed significant correlations with cognitive measures and plasma biomarkers, such as tau proteins and neurofilament light chain (NfL). These findings underscore the exceptional generalizability and clinical utility of MRI-based deep learning models, especially in large and diverse populations, offering valuable tools for early therapeutic intervention. The model has been made open-source and deployed to a free online website for AD risk prediction, to assist in early screening and intervention.

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第一作者机构： [1]State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. [2]Department of Psychology, University of the Chinese Academy of Sciences, Beijing 100049, China. [3]Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, 6525 EN, the Netherlands.

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通讯作者：

通讯机构： [1]State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. [2]Department of Psychology, University of the Chinese Academy of Sciences, Beijing 100049, China. [4]Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China. [5]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China. [10]State Key Laboratory of Digital Medical Engineering, Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Sanya, Hainan 572025, China. [11]The Institute of Neurological and Psychiatric Disorders, Shenzhen Bay Laboratory, Shenzhen, Guangdong 518132, China. [12]National Clinical Research Center for Geriatric Diseases, Beijing 100053, China. [13]The Central Hospital of Karamay, Xinjiang 834000, China. [14]Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China.

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