Structural similarity networks reveal brain vulnerability in dementia

作者：Marcella Montagnese, Amir Ebneabbasi, Natalia García‐San‐Martín, Clara Pecci‐Terroba, Rafael Romero‐García, Sarah E. Morgan, James H. Cole, Jakob Seidlitz, Timothy Rittman, RAI Bethlehem · 发表于：Alzheimer s & Dementia · 年份：2025 · DOI：10.1002/alz.70973 · 被引用次数：3 · 研究领域：Functional Brain Connectivity Studies、Dementia and Cognitive Impairment Research、Alzheimer's disease research and treatments

INTRODUCTION: Alzheimer's disease (AD) is characterized by inter-individual heterogeneity in brain degeneration, limiting diagnostic and prognostic precision. We present a novel framework integrating Morphometric Inverse Divergence (MIND) networks with hierarchical Bayesian large-scale population modeling to identify individual-level neuroanatomical deviations. METHODS: MIND networks quantify similarity between brain regions using multivariate magnetic resonance imaging (MRI) features. A normative model of regional MIND values trained on UK Biobank (N = 35,133) was applied to the National Alzheimer's Coordinating Center cohort (N = 3,567). We examined brain deviations across clinical stages, apolipoprotein E (APOE) genotypes, mortality risk, and neuropathological burden. RESULTS: Negative deviations (reduced MIND) stratified disease stages (p < 0.01) and were concentrated in specific functional networks in AD. Greater negative deviations characterized APOE ε4 homozygotes and correlated with post mortem neuropathological severity (p = 0.032). Spatially, deviation patterns were associated with maps of neurotransmitter receptor density. DISCUSSION: This population neuroimaging modeling enables individualized brain mapping with direct utility for diagnosis, prognosis, and understanding of biological mechanisms. HIGHLIGHTS: MIND networks were systematically integrated with normative modeling in AD. Negative deviations stratify clinical stages and correlate with neuropathology. Neg...