Structural Similarity Networks Reveal Brain Vulnerability in Dementia.

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) stratified disease stages (p<0.01) and showed functional network enrichment in AD. Greater negative deviations characterised APOE ε4 homozygotes and correlated with postmortem neuropathological severity (p=0.032). Neurobiological decoding revealed associations with neurotransmitter receptor distributions and cortical organisation properties.

DISCUSSION: This population neuroimaging modelling enables individualised brain mapping with direct utility for diagnosis, prognosis, and understanding of biological mechanisms.