This study was performed in accordance with the Declaration of Helsinki and approved by the institutional ethical review committee, and informed consent was obtained from all participants. Eighteen never-treated OSAHS patients (age range, 25–65 years) and 18 healthy controls (age range, 25–65 years) were recruited. All participants were right-handed Han Chinese. The participants did not have other sleep disorders or uncontrolled cardiovascular disease, hypertension, or diabetes mellitus and had no current use of psychoactive medications. The Mini-Mental State Examination (MMSE) was used to exclude those with symptoms of cognitive deterioration (the participants with MMSE score < 24 were excluded) .
All OSAHS patients underwent polysomnography in the sleep center. For the 24 h before the examination, all patients were forbidden to take sleeping pills or drink alcohol, coffee, soda, strong tea and other energizing drinks. The sleep monitoring results were corrected and analyzed by specialized technicians. The apnea–hypopnea index (AHI), arousal index (ArI), saturation impair time below 90% during total sleep (SIT90), and sleep stages were recorded for our study.
The day after polysomnography, all participants underwent neuropsychological assessments, including the MMSE, trail-making test (TMT-A and B), digit span test (DST-forward and backward), and Rey's auditory verbal learning test (RAVLT-immediate recall, delayed recall, learning, and forgetting). Tests were administered and scored by an experienced doctor based on published procedures and lasted approximately 30 min.
Functional magnetic resonance imaging (fMRI) data acquisition
The same day as the neuropsychological assessments, imaging data were acquired with a 3.0 T GE MRI scanner (GE Discovery MR750). The resting-state (rs)-fMRI data were acquired with an echo-planar imaging (EPI) sequence covering the whole brain, consisting of 250 sequential volumes (repetition time (TR) = 2000 ms, echo time (TE) = 30 ms, field of view (FOV) = 240 mm × 240 mm, matrix = 64 × 64, flip angle = 90°, slice thickness = 3.6 mm, 35 interleaved slices parallel to the bicommissural line). High-resolution structural T1-weighted images were also obtained (TR = 8.5 ms, TE = 3.3 ms, FOV = 240 mm × 240 mm, matrix = 256 × 256, flip angle = 12°, slice thickness = 1 mm, no gap, 184 sagittal slices). All participants were asked to lie flat with their eyes closed, breathe steadily, stay awake, and not do any specific thinking during fMRI scans. Earplugs or earphones were used to reduce noise, and sponge cushions were added on both sides of the head to reduce head movement.
fMRI data preprocessing and independent component analysis (ICA)
fMRI data preprocessing was carried out with DPARSFA (http://rfmri.org/DPARSF) software. After transformation to the DICOM format, the first 10 time points of the rs-fMRI images were discarded, and slice timing, realignment, spatial normalization to the standard Montreal Neurological Institute (MNI) space, and spatial smoothing proceeded successively. Participants who had head motion > 2 mm in any dimension or angular rotation > 2° were excluded. The group spatial ICA was performed by a group ICA model for fMRI data (GIFT; http://icatb.sourceforge. net/) in three stages: The first stage was data reduction, in which a principal component analysis (PCA) was used to reduce individual fMRI data. The resulting volumes were concatenated and PCA was used again. After data reduction, the Infomax algorithm for ICA decomposition was used to identify the group components across all the participants. Finally, on the basis of the group components and the information found in the data reduction stage, the time courses and spatial maps for each participant were back-reconstructed, and the mean spatial maps for each component across all participants were displayed. Based on visual inspection and previous studies [15,16,17,18], we identified the RSNs we needed (including DMN, ECN, DAN, VAN, SMN, and SN) and saved them for subsequent analyses.
Demographic and clinical characteristics, including age, years of education, sleep-disordered breathing parameters, and neuropsychological test scores, were analyzed using independent sample t-tests to compare OSAHS patients with healthy controls (P < 0.05 was deemed significant). Partial correlation analysis [12, 19] adjusted for age and education was performed to correlate sleep-disordered breathing parameters and neurocognitive performance. All the analyses were completed using the Statistical Package for the Social Sciences version 25.0 (SPSS, Chicago, IL, USA).
For functional imaging data, the analysis was performed using the SPM12 software, with age and education as nuisance covariates. we used a one-sample t-test to create a sample-specific component map as a spatial mask with the component maps for all participants (voxel-level familywise error (FWE)-corrected, P < 0.05). Then, a two-sample t-test was performed to explore between-group differences in the component time course-related activity within the spatial mask (cluster-level FWE-corrected, P < 0.05; single-voxel P = 0.001) and saved them as regions of interest (ROIs). Then, for patient group and control group, the average ICA z-scores of the ROIs were extracted using RESTplus software for further correlation analysis. The correlation analyses with clinical variables were performed using a partial correlation method (P < 0.05) in SPSS, the age and education were considered as the nuisance covariates.