Explainable and Externally Validated Resting-State fMRI Machine Learning Reveals Network Mechanisms Supporting Preserved Consciousness: A Cross-Sectional Study Comparing Minimally Conscious State and Unresponsive Wakefulness Syndrome

Background Accurate differentiation between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) is a significant clinical challenge because behavioral assessments are often constrained by patients' motor impairments and fluctuating arousal. Beyond diagnostic classification, clarifying the specific brain network mechanisms that sustain residual consciousness in MCS remains a clinical priority for developing targeted therapeutic interventions. Methods This multicenter study acquired rs-fMRI data from 100 patients across two independent clinical centers. Six participants were excluded due to suboptimal data quality related to excessive head motion, resulting in a final analysis of 94 patients (discovery cohort: n = 48; external validation cohort: n = 46). A diagnostic framework was developed using regional and network markers, including the amplitude of low-frequency fluctuations, regional homogeneity, degree centrality, and functional connectivity. Nine machine learning classifiers were optimized, with the best-performing model tested on the external validation cohort. SHAP analysis quantified circuit contributions to elucidate neurobiological mechanisms. Results L1-regularized logistic regression selected 10 core features, dominated by default mode network (DMN) interactions with cerebellar and subcortical nodes and salience-related local synchrony. The support vector machine emerged as the leading model, achieving an AUC of 0.859 (accuracy: 82.6%; sensitivity: 90.5%) in the external validation cohort. SHAP attribution identified a core neurobiological signature dominated by DMN–cerebellar coupling (specifically left angular gyrus to right cerebellum), alongside DMN–subcortical (thalamus and putamen) pathways and insular synchrony. Conclusions An externally validated rs-fMRI framework differentiated MCS from UWS and localized residual consciousness in MCS to preserved DMN-centered corticocerebellar and cortico–subcortical circuits.