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A multimodal Neuroimaging-Based risk score for mild cognitive impairment
Elaheh Zendehrouh1, Mohammad S E Sendi2, Anees Abrol3
1Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University, Georgia Institute of Technology, Emory University Atlanta, GA, United States; Department of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, United States.
Introduction:
Alzheimer's disease (AD), the most prevalent age-related dementia, leads to significant cognitive decline. While genetic risk factors and neuroimaging biomarkers have been extensively studied, establishing a neuroimaging-based metric to assess AD risk has received less attention. This study introduces the Brain-wide Risk Score (BRS), a novel approach using multimodal neuroimaging data to assess the risk of mild cognitive impairment (MCI), a precursor to AD.
Methods:
Participants from the OASIS-3 cohort (N = 1,389) were categorized into control (CN) and MCI groups. Structural MRI (sMRI) data provided gray matter (GM) segmentation maps, while resting-state functional MRI (fMRI) data yielded functional network connectivity (FNC) matrices via spatially constrained independent component analysis. Similar imaging features were computed from the UK Biobank (N = 37,780). The BRS was calculated by comparing each participant's neuroimaging features to the difference between average features of CN and MCI groups. Both GM and FNC features were used. The BRS effectively differentiated CN from MCI individuals within OASIS-3 and in an independent dataset from the ADNI cohort (N = 729), demonstrating its ability to identify MCI risk.
Results:
Unimodal analysis revealed that sMRI provided greater differentiation than fMRI, consistent with prior research. Using the multimodal BRS, we identified two distinct groups: one with high MCI risk (negative GM and FNC BRS) and another with low MCI risk (positive GM and FNC BRS). Additionally, 46 UK Biobank participants diagnosed with AD showed FNC and GM patterns similar to the high-risk groups.
Conclusion:
Validation using the ADNI dataset confirmed our results, highlighting the potential of FNC and sMRI-based BRS in early Alzheimer's detection.
