| Objective:Neuromyelitis optica spectrum disorders(NMOSD) is an autoimmune, inflammatory and demyelinating condition of the central nervous system that is recognized as an anti-aquaporin(AQP)-4 antibody-mediated astrocytopathy. Slow wave sleep abnormality has been reported in NMOSD. SWS is considered to be a vital part of non-rapid-eye-movement(NREM) sleep. But mechanism for such abnormality is unknown.In the current study, we combined voxel-based morphometry(VBM) analysis of high resolution structural MRI data and polysomnography to determine the structural defects in the brain that account for the decrease of slow wave sleep in NMOSD patients.Methods: 1.Thirty-three patients with NMOSD were enrolled from Aug 2013 to May 2014, which were in stable state, were consecutively enrolled. Eighteen age and gender matched healthy controls with no sleep abnormalities were recruited. 2. Overnight video PSG examinations were performed for two groups using the sleep laboratory digital system. According to The American Academy of Sleep Medicine(AASM) manual for the scoring of sleep and associated Events(2007), The stages of sleep was defined. In order to explore the potential impact factors on decrease of SWS, we only compared NMOSD patients with low a percentage of SWS and matched HC. 3. All MRI data were acquired using a 3.0-Tesla MR system(Discovery MR750, General Electric, Milwaukee, WI, USA), included 3D high-resolution T1 as well as fluid attenuated inversion recovery(FLAIR). Brain Lesions were examined and classified as normal, nonspecific, multiple sclerosis(MS)-like, NMO-like, or acute disseminated encephalomyelitis(ADEM)-like lesions. All preprocessing steps of high-resolution T1 WI were carried out using the statistical parametric mapping(SPM8) based on Matlab. The preproeessing of high resolution structural data included segmentation of grey and white matter, spatial normalization and smooth. At last, the grey matter volume(GMV)and white matter volume(WMV) map will be generated. 4. Compared GMV and WMV between patients with a low percentage SWS and HC.The ROIs were defined as the brain areas which showed significant differences. ROIs were extracted from the AAL template. The volume will be calculated in the ROIs using software of Resting-State f MRI Data Analysis Toolkit(REST). The correlation will be analyzed between the ROIs and SWS percentage.Results: 1.Comparing patients with a low percentage SWS and HC using VBM, a difference was found in the bilateral thalamus(FWE corrected at cluster-level, p<0.05, cluster size > 400 voxels). Right thalamus volumes were positively correlated with the percentage of SWS using voxel-level analysis(p < 0.05, FDR corrected, cluster size > 200 voxels); 2. The NMOSD patients with a low percentage of SWS were found to have a significantly lower TF than HC in both the right(0.29 ± 0.02 vs. 0.33 ± 0.03; p < 0.0001) and the left(0.29 ± 0.03 vs. 0.31 ± 0.03; p = 0.0037) thalamus. The right TF was positively correlated with the percentage of SWS in patients with NMOSD(p = 0.0244, r = 0.4243); 3. There was no significant difference with regard to the GMF between patients with a low percentage of SWS and HC(0.46 ± 0.02 vs. 0.47 ± 0.02, p = 0.0667); 4. We were unable to identify the correlationship between the percentage of SWS and the left TF(p = 0.4252, r = 0.1569).Conclusion: 1.SWS reduction was highly prevalent in NMOSD patients; 2.Our study identified that thalamic atrophy was associated with decrease of SWS in NMOSD patients, suggesting that thalamic atrophy maybe a potential mechanism for SWS abnormality in NMOSD. 3. Further studies should evaluate whether neurotransmitters or hormones which stem from thalamus are involved in the decrease of slow wave sleep. |
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