Structural Similarity Networks Reveal Brain Vulnerability in Dementia.

ABSTRACT:

Introduction

Alzheimer's disease (AD) is characterised 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 modelling to identify individual-level neuroanatomical deviations.

Methods

MIND networks quantify similarity between brain regions using multivariate 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, APOE genotypes, mortality risk, and neuropathological burden.

Results

Negative deviations (reduced MIND) strati

SUBMITTER: Montagnese M 

PROVIDER: S-EPMC12204435 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature