Deep Learning-Based Assessment of Brain Connectivity Related to Obstructive Sleep Apnea and Daytime Sleepiness

Abstract

Purpose: Obstructive sleep apnea (OSA) is associated with altered pairwise connections between brain regions, which might explain cognitive impairment and daytime sleepiness. By adopting a deep learning method, we investigated brain connectivity related to the severity of OSA and daytime sleepiness. Patients and Methods: A cross-sectional design applied a deep learning model on structural brain networks obtained from 553 subjects (age, 59.2 +/- 7.4 years; men, 35.6%). The model performance was evaluated with the Pearson's correlation coefficient (R) and probability of absolute error less than standard deviation (PAE<SD) between the estimated and the actual scores. In addition, we investigated sex effects on deep learning outputs for OSA and daytime sleepiness and examined the differences in brain connectivity related to daytime sleepiness between OSA and non-OSA groups. Results: We achieved a meaningful R (up to 0.74) and PAE<SD (up to 0.92) in a test dataset of whole group and subgroups. Motor, frontal and limbic areas, and default mode network were the prominent hubs of important connectivity to predict OSA severity and daytime sleepiness. Sex affected brain connectivity relevant to OSA severity as well as daytime sleepiness. Brain connectivity associated with daytime sleepiness also differed by the presence vs absence of OSA. Conclusion: A deep learning method can assess the association of brain network character-istics with OSA severity and daytime sleepiness and specify the relevant brain connectivity.