Co-classification network analysis reveals nodal dysfunction and dimension-specific alterations in obstructive sleep apnea-hypopnea syndrome.

BACKGROUND: Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a prevalent sleep disorder linked to brain alterations, but its brain network patterns and convenient screening methods remain unclear. This study aimed to characterize OSAHS functional brain networks via co-classification network analysis and assess their diagnostic utility. METHODS: 174 participants were stratified by the apnea-hypopnea index (AHI) into none or mild (nm-OSAHS) and moderate or severe (ms-OSAHS) groups, and by the lowest oxygen saturation (SpO2 nadir) into none or mild (nm-hypoxemia) and moderate or severe (ms-hypoxemia) groups. (Registered number: MR-32-25-040098). They underwent out-of-center sleep testing, neuropsychological tests, and MRI. Co-classification networks (via consensus modularity analysis) and traditional functional networks (via graph theory) were analyzed, with random forest model for diagnostic accuracy. RESULTS: Ms-OSAHS and ms-hypoxemia groups showed worse sleep respiratory parameters. Co-classification network analysis revealed significant alterations in intra-module z score, participation coefficient (PC), and diversity coefficient (SD) in the visual, dorsal attention, salience/ventral attention, and executive control networks in ms-OSAHS patients. PC in the left temporo-occipital junction and right parietal operculum was positively correlated with AHI, while PC in limbic regions was negatively correlated with lowest SpO2. The random forest model using co-classification metrics demonstrates good diagnostic performance for OSAHS, with dorsal attention, control, and salience networks mostly contributing to classification. CONCLUSIONS: OSAHS induced widespread nodal dysfunction in brain networks, with distinct changes driven by hypoxemia and apnea frequency. Co-classification network analysis outperforms traditional methods in detecting network abnormalities, and machine learning models based on nodal parameters show high diagnostic accuracy, suggesting potential for OSAHS screening.