Linking dynamic connectivity states to cognitive decline and anatomical changes in Alzheimer's disease.

Alterations in brain connectivity provide early indications of neurodegenerative diseases like Alzheimer's disease (AD). Here, we present a novel framework that integrates a Hidden Markov Model (HMM) within the architecture of a convolutional neural network (CNN) to analyze dynamic functional connectivity (dFC) in resting-state functional magnetic resonance imaging (rs-fMRI). Our unsupervised approach captures recurring connectivity states in a large cohort of subjects spanning the Alzheimer's disease continuum, including healthy controls, individuals with mild cognitive impairment (MCI), and patients with clinically diagnosed AD. We propose a deep neural model with embedded HMM dynamics to identify stable recurring brain states from resting-state fMRI. These states exhibit distinct connectivity patterns and are differentially expressed across the Alzheimer's disease continuum. Our analysis shows that the fraction of time each state is active varies systematically with disease severity, highlighting dynamic network alterations that track neurodegeneration. Our findings suggest that the disruption of dynamic connectivity patterns in AD may follow a two-stage trajectory, where early shifts toward integrative network states give way to reduced connectivity organization as the disease progresses. This framework offers a promising tool for early diagnosis and monitoring of AD, and may have broader applications in the study of other neurodegenerative conditions.