Structural similarity networks reveal brain vulnerability in dementia.

ABSTRACT:

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.

Re

SUBMITTER: Montagnese M 

PROVIDER: S-EPMC12741943 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature