Multimodal Integration of Plasma Biomarkers, MRI, and Genetic Risk to Predict Cerebral Amyloid Burden in Alzheimer's Disease.
Authors
Affiliations (15)
Affiliations (15)
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China. Electronic address: [email protected].
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Division of Life Science and State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China. Electronic address: [email protected].
- State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu, 610500, Sichuan, P. R. China. Electronic address: [email protected].
- School of Economics and Business Administration, Beijing Normal University. Electronic address: [email protected].
- School of Mathematical Sciences, Beijing Normal University, Beijing, China. Electronic address: [email protected].
- School of Mathematical Sciences, Beijing Normal University, Beijing, China. Electronic address: [email protected].
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China. Electronic address: [email protected].
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China. Electronic address: [email protected].
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China. Electronic address: [email protected].
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China. Electronic address: [email protected].
- College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, MOE Key Laboratory of Brain Computer Intelligence Technology, Nanjing 211106, China. Electronic address: [email protected].
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, USA. Electronic address: [email protected].
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China; School of Biomedical Engineering, Hainan University, Haikou 570228, China; Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; The Central Hospital of Karamay, Xinjiang 834000, China; Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China. Electronic address: [email protected].
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; BABRI Centre, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China. Electronic address: [email protected].
Abstract
Alzheimer's disease (AD), the most prevalent neurodegenerative disorder, is marked by the accumulation of amyloid-β (Aβ) plaques. Although cerebral Aβ positron emission tomography (Aβ-PET) remains the gold standard for assessing cerebral Aβ burden, its clinical utility is hindered by cost, radiation exposure, and limited availability. Plasma biomarkers have emerged as promising, non‑invasive indicators of Aβ pathology, yet they do not incorporate individual genetic risk or neuroanatomical context. To address this gap, we developed a multimodal machine‑learning framework that integrates plasma biomarkers, MRI‑derived brain structural features (regional volumes, cortical thickness, cortical area and structural connectivity), and genetic risk profiles to predict cerebral Aβ burden. This approach was evaluated in 150 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 101 participants from a domestic Chinese Sino Longitudinal Study of Cognitive Decline (SILCODE). Incorporating multimodal features substantially improved predictive performance: the baseline model using plasma and clinical variables alone achieved an R<sup>2</sup> of 0.56, whereas integrating neuroimaging and genetic information increased accuracy (R<sup>2</sup> = 0.63 with apolipoprotein E genotypes and R<sup>2</sup> = 0.64 with polygenic risk scores). Furthermore, a multiclass classifier trained on the same multimodal features achieved robust discrimination of cognitive status, with area‑under‑the‑curve values of 0.87 for normal controls, 0.76 for mild cognitive impairment, and 0.95 for AD dementia. These findings highlight the value of combining plasma, imaging, and genetic data to non-invasively estimate cerebral Aβ burden, offering a potential alternative to PET imaging for early AD risk assessment.