Functional Connectivity Changes in Primary Motor Cortex Subregions of Patients With Obstructive Sleep Apnea - Lifeng Li; Qimeng Shi; Bowen Fang; Yuting Liu; Xiang Liu; Yongqiang Shu; Yingke Deng; Yumeng Liu; Haijun Li; Junjie Zhou; Dechang Peng (Brain and Behavior)
ABSTRACT Background and Purpose Obstructive sleep apnea (OSA) is linked to cognitive impairment and altered motor‐related brain networks. This study examined functional connectivity (FC) changes in subregions of the primary motor cortex (M1) in patients with OSA and their association with sleep structure, cognition, and clinical features. Methods Sixty‐five patients with OSA and 65 healthy controls (HC) participants matched in age and educational background were included. Resting‐state functional MRI data were acquired for all participants using a 3T MRI system. Based on the Human Brainnetome Atlas, we analyzed FC changes of 12 subregions of M1 across the whole brain in patients with OSA. The two‐sample t‐tests were conducted to compare FC values between subregions of M1 and other brain regions in two groups. Partial correlation analyses examined the association between FC and clinical variables in patients with OSA. Additionally, we employed three machine learning algorithms—support vector machine (SVM), random forest (RF), and logistic regression (LR)—to distinguish patients with OSA from HC based on FC features. Results Compared to HC, the OSA group found that significant FC enhancements were identified in right A6cdl with the left inferior parietal lobule (IPL); left A4tl with the left inferior frontal gyrus (IFG), bilateral middle frontal gyrus (MFG), and left IPL; and left A6cvl with the right parahippocampal gyrus, bilateral MFG, left IFG, left superior temporal gyrus, and right cingulate gyrus. After Bonferroni correction, a negative correlation was observed between the FC value of A4tl (L)‐IPL (L) and N2 (p < 0.05). Furthermore, SVM yielded the highest area under the receiver operating characteristic (ROC) curve (AUC) among all classifiers, indicating its superior performance in discriminating OSA patients from HC based on FC features. Conclusion The study demonstrates that OSA significantly impacts brain functional networks, particularly affecting motor control through altered FC in subregions of M1. These alterations correlate with upper airway dysfunction and cognitive impairments, increasing accident risks. The high‐accuracy SVM classification based on FC patterns demonstrates potential as a diagnostic biomarker for OSA. Future research should explore M1 FC patterns as diagnostic markers and neuromodulation therapies.