Structural Similarity Networks Reveal Brain Vulnerability in Dementia

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.

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.

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.

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