Classification of depressed and non-depressed MCI and non-depressed cognitively normal individuals using...

Depressive symptoms frequently co-occur in individuals with Mild Cognitive Impairment (MCI) and are thought to accelerate neurodegenerative progression. However, the underlying neural mechanisms of Depressed MCI (DMCI) remain largely unclear. This study employed a multimodal resting-state functional magnetic resonance imaging (rs-fMRI) approach combined with advanced machine learning techniques, to systematically examine spontaneous brain activity patterns and topological organization differences among DMCI, non-depressed MCI (nDMCI), and non-depressed cognitively normal controls (nDCN). The research analyzed amplitude-based rs-fMRI measures and graph-theoretical features. Voxel-wise analyses and connectivity comparisons were conducted between groups. Additionally, classification tasks were performed using classical machine learning models and a graph reinforcement learning (GRL) model. DMCI individuals exhibited increased activity in the right insula and decreased amplitude of low-frequency fluctuation (ALFF) in the left calcarine cortex, along with heightened fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) in the precuneus and parahippocampal regions. Graph metrics revealed disrupted global and local efficiency in nDMCI compared to nDCN. Using differential matrices, machine learning achieved optimal accuracies of 0.82 ± 0.15 (DMCI vs. nDMCI) and 0.84 ± 0.15 (DMCI vs. nDCN). Conversely, the GRL model for nDMCI vs. nDCN peaked at 0.66 ± 0.02 using full matrices, dropping to 0.60 ± 0.04 with filtering, indicating deep graph models require complete topological data for subtle differences. Rs-fMRI and graph learning approaches offer promising avenues for subtype classification, highlighting the hyperactivity of the right insula and the integrity of the whole-brain functional connectivity matrix as crucial potential biomarkers of early pathological changes.