Core and bridge nodes between sleep quality and depression in Chinese patients with amyotrophic lateral sclerosis: A network analysis perspective.

Background

Sleep quality and depression are prevalent yet under-recognized non-motor symptoms (NMS) in amyotrophic lateral sclerosis (ALS), and the interplay between their symptom domains remains poorly characterized. This study applied network analysis to identify core and bridge nodes linking sleep quality and depression in Chinese ALS patients.

Methods

A cross-sectional study was conducted among 372 ALS patients recruited from a tertiary hospital in Wuhan, China. Sleep quality and depressive symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and the Patient Health Questionnaire-9 (PHQ-9), respectively. A regularized Gaussian graphical model was estimated using the graphical LASSO algorithm. Node centrality metrics including strength, closeness, betweenness, and expected influence (EI) were calculated to identify core nodes, and bridge expected influence (BEI) was calculated to identify bridge nodes. A case-dropping bootstrap procedure was used to evaluate the stability of the network estimates.

Results

Among all patients, 50.81% screened positive for poor sleep quality and 41.40% for depressive symptoms, with 28.49% screening positive for both. Subjective sleep quality showed the highest strength and EI, identifying it as the most central node. Daytime dysfunction exhibited the highest closeness and betweenness centrality and the largest BEI, emerging as the principal bridge node connecting the communities. The network demonstrated good stability, with CS-coefficients of 0.67 and 0.60 for EI and BEI, respectively.

Conclusion

The findings highlight subjective sleep quality and daytime dysfunction as potentially influential nodes within the sleep quality-depression network, representing candidate targets for future longitudinal and intervention studies that may inform strategies to support well-being in patients with ALS.