Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems. Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD. IBRAIN accurately differentiated individuals with AD from NCs (AUC=0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson's disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC=0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR)=6.52 [95% CI: 4.42∼9.62], p<1×10-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aβ (HR=3.78 [95% CI: 2.63∼5.43], p=2.13×10-14) and CSF Tau (HR=3.77 [95% CI: 2.64∼5.39], p=9.53×10-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta=-0.70, p<1×10-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aβ (beta=-0.26, p=4.40×10-9) and CSF Tau (beta=0.12, p=1.02×10-5). Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials. Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.
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