Widespread aberrant functional connectivity throughout the whole brain in obstructive sleep apnea

Ailin Hou; Xueming Pang; Xi Zhang; Yanmin Peng; Dongyue Li; He Wang; Quan Zhang; Meng Liang; Feng Gao

doi:10.3389/fnins.2022.920765

Widespread aberrant functional connectivity throughout the whole brain in obstructive sleep apnea

Front Neurosci. 2022 Aug 1:16:920765. doi: 10.3389/fnins.2022.920765. eCollection 2022.

Authors

Ailin Hou^{1

2}, Xueming Pang², Xi Zhang², Yanmin Peng², Dongyue Li², He Wang², Quan Zhang³, Meng Liang², Feng Gao^{1

4}

Affiliations

¹ College of Precision Instrument and Optoelectronics Engineering, Tianjin University, Tianjin, China.
² School of Medical Imaging and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China.
³ Department of Radiology, Characteristic Medical Center of Chinese People's Armed Police Force, Tianjin, China.
⁴ Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China.

Abstract

Objective: Obstructive sleep apnea (OSA) is a sleep-related breathing disorder with high prevalence and is associated with cognitive impairment. Previous neuroimaging studies have reported abnormal brain functional connectivity (FC) in patients with OSA that might contribute to their neurocognitive impairments. However, it is unclear whether patients with OSA have a characteristic pattern of FC changes that can serve as a neuroimaging biomarker for identifying OSA.

Methods: A total of 21 patients with OSA and 21 healthy controls (HCs) were included in this study and scanned using resting-state functional magnetic resonance imaging (fMRI). The automated anatomical labeling (AAL) atlas was used to divide the cerebrum into 90 regions, and FC between each pair of regions was calculated. Univariate analyses were then performed to detect abnormal FCs in patients with OSA compared with controls, and multivariate pattern analyses (MVPAs) were applied to classify between patients with OSA and controls.

Results: The univariate comparisons did not detect any significantly altered FC. However, the MVPA showed a successful classification between patients with OSA and controls with an accuracy of 83.33% (p = 0.0001). Furthermore, the selected FCs were associated with nearly all brain regions and widely distributed in the whole brain, both within and between, many resting-state functional networks. Among these selected FCs, 3 were significantly correlated with the apnea-hypopnea index (AHI) and 2 were significantly correlated with the percentage of time with the saturation of oxygen (SaO₂) below 90% of the total sleep time (%TST < 90%).

Conclusion: There existed widespread abnormal FCs in the whole brain in patients with OSA. This aberrant FC pattern has the potential to serve as a neurological biomarker of OSA, highlighting its importance for understanding the complex neural mechanism underlying OSA and its cognitive impairment.

Keywords: functional connectivity; machine learning; multivariate pattern analyses; obstructive sleep apnea; resting-state functional magnetic resonance imaging.