| Part I Regional homogeneity changes in patients with primary insomniaObjective:The neurobiological mechanisms of primary insomnia (PI) are poorly understood. In current study, we aimed to explore the regional spontaneous activity changes in PI patients using regional homogeneity (ReHo) of blood oxygenation level dependent (BOLD) signals in resting-state.Materials and Methods:The imaging datasets were evaluated by one or two experienced radiologists who were blinded to whether the images were from the PI patients or the control group. After the evaluation, we excluded three PI patients with abnormal intense signals in the conventional T2 image. Finally,59 PI patients (mean age,41.0±9.1 years, range:18-57 years) were included in the study. According to the ISI scores, there were two patients with mild PI,41 patients with moderate PI, and 16 patients with severe PI. Forty-seven healthy controls (mean age,39.3± 10.7 years, range: 20-59 years) participated in the study.All subjects underwent a battery of the tests before undergoing MRI. The tests were as follows:(i) The Pittsburgh Sleep Quality Index (PSQI) and the ISI to evaluate sleep quality; (ii) The Self-rating Anxiety Scale (SAS); and the Self-rating Depression Scale (SDS) to estimate the mental status of the PI patients.The MRI data were acquired using a 1.5-T MR imager (Achieva Nova-Dual; Philips, Best, the Netherlands) in the Department of Medical Imaging, Guang Dong No.2 Provincial People’s Hospital. Each subject was supine with the head snugly secured using a belt and foam pads. The rs-fMRI datasets were obtained with a gradient-echo planar imaging (EPI) sequence. During rs-fMRI scanning, subjects were asked to close and not move their eyes, lie still, and not think of anything special or fall asleep. The rs-fMRI acquisition parameters were as follows:TR/TE= 2500 ms/50 ms, repetition time matrix=64×64, FOV (field of view)= 224 mm × 224 mm, flip angle= 90°, slice thickness= 4 mm with a 0.8-mm gap, interleaved scanning,27 axonal slices covering the whole brain were positioned approximately along the AC-PC line, and 240 volumes were acquired in approximately 10 min. After the MRI scanning, each participant was asked questions to determine their degree of cooperation. In addition, we also obtained the T1-weighted images and T2-FLAIR images to exclude clinically silent lesions.Image pre-processing was conducted using the DPARSF toolbox (http://rfmri.org/DPARSF). For each participant, the first 10 time points were discarded because of transient signal changes before magnetization reached a steady-state and participants adapting to the fMRI scanning noise. In order to minimize the influence of head motion, we first eliminated the subjects with more than 1.5 mm maximum displacement in any dimension and 1.5 degrees of angular motion during the entire fMRI scan..We then compared the motion courses of the two groups using a two-sample t-test and confirmed no statistical significance. Finally, we set the head-motion measures as covariates for group-level comparisons. Following the motion correction, all data were spatially normalized to the Montreal Neurological Institute (MNI) template (resampling voxel size= 3x3×3 mm3). To remove the effects of very low-frequency drift and physiological high frequency respiratory and cardiac noise, normalized data were processed with the removal of linear trends and temporally filtered (band pass,0.01-0.08 Hz).The ReHo calculation procedure was the same as that reported in previous studies. In short, ReHo was estimated on a voxel-by-voxel basis by calculating KCC for a given voxel time series with those of its nearest 26 neighbors:where W is the KCC among given voxels, ranging from 0 to 1; Ri is the sum rank of the ith time point,where rij is the rank of the ith time point in the jth voxel; R=((n+1) k)/2 is the mean of the Ri; k is the number of time series within a measured cluster (27, one given voxel plus the number of its neighbors); and n is the number of ranks (n = 150 for this study). The KCC value was calculated to this voxel, and an individual KCC map was obtained for each subject. To reduce the influence of individual variations in the KCC value, ReHo map normalization was performed by dividing the KCC among each voxel by the averaged KCC of the whole brain. The resulting data were then spatially smoothed with an 8-mm full-width at half-maximum (FWHM) Gaussian kernel to reduce noise and residual differences in gyral anatomy.Two-sample t-tests were performed to assess the differences in age, duration of education, PSQI, SAS, and SDS scores between patients with PI and healthy subjects. A chi-square test was used to assess the gender composition between PI patients and healthy controls.To explore the ReHo differences between PI patients and healthy subjects, a two-sample t-test was performed on the individual normalized ReHo maps and P< 0.05 (corrected for multiple comparisons with AlphaSim, http://afni.nimh.nih.gov/afni/) was considered as significant. To identify the association between the ReHo alteration and the performance level on the PSQI, SAS, and SDS tests, the mean ReHo values for all voxels in the significant areas were extracted separately using REST (http://resting-fmri.sourceforge.net) Then, Spearman correlation analysis was performed between the mean ReHo values in significantly different areas and the of PSQI, SAS, and SDS scores in SPSS 20.0 (P< 0.05).Result:There were no significant between-group differences in demographic data including gender (P=0.096), sex (P=0.704), and education level (P=0.288). As expected, PI patients had higher PSQI, SAS, and SDS scores than controls.Rs-fFMRI analysis showed that, compared with controls, PI patients had increased ReHo in multiple brain regions, including the left insula, left anterior cingulate gyrus, bilateral precentral gyrus, and left cuneus, as well as decreased ReHo in the left middle cingulate cortex and right fusiform.There was no significant correlation between ReHo values in abnormal regions and PSQI scores. The ReHo values for the left insula showed a significant positive correlation with SAS scores (r=0.377, P=0.003). We also found a significantly positive correlation between the decreased ReHo values for the right middle cingulate cortex and SDS scores (r=0.312, P=0.016) and SAS scores (r=0.303, P=0.020), as well as a negative correlation between and the increased ReHo in the right precentral gyrus and SDS scores (r=-0.292, P=0.025;).Conclusion:Our study provides information on functional specialization underlying primary insomnia. The alteration of spontaneous brain activity might contribute to insomnia and its clinical features.Part Ⅱ Abnormal insular connectivity in primary insomnia patients:evidence from resting state fMRIObjective:Despite of the highly prevalent condition, Neurobiological mechanisms of primary insomnia(PI) is still elusive. Previous research indicated that dysfunction in emotional circuit might promote the procession of PI.In current study, we aimed to explore the functional integration changes in PI patients.Materials and Methods:57 PI patients (mean age:39.58±10.742 years, range:18-57 years) and 46 healthy controls(mean age,39.93 ± 9.229 years, range:20-59 years) were included in the study. According to the ISI scores, there were two patients with mild PI,39 patients with moderate PI, and 16 patients with severe PI.The MRI data were acquired using a 1.5-T MR imager (Achieva Nova-Dual; Philips, Best, the Netherlands) in the Department of Medical Imaging, Guang Dong No.2 Provincial People’s Hospital. Each subject was supine with the head snugly secured using a belt and foam pads. The rs-fMRI datasets with a gradient-echo planar imaging (EPI) sequence were obtained. During the resting state scan, subjects were instructed to keep their eyes closed, relax and not think of anything special or fall asleep. The rs-fMRI acquisition parameters were as follows:TR/TE= 2500 ms/50 ms, repetition time matrix=64×64, FOV (field of view)= 224 mm × 224 mm, flip angle= 90°, slice thickness= 4 mm with a 0.8-mm gap, interleaved scanning,27 axonal slices covering the whole brain were positioned approximately along the AC-PC line, and 240 volumes were acquired in approximately 10 min. After the MRI scanning, each participant was asked questions to determine their degree of cooperation. In addition, T1-weighted images and T2-FLAIR images were also acquired to exclude clinically silent lesions.The fMRI data preprocessing was conducted using DPARSF toolbox (http://rfmri.org/DPARSF). The first 10 time points were discarded for the signal equilibrium and subject s’adaptation to the scanning noise. The slice timing and head motion correction were performed,then the remaining fMRI data were normalized to the standard Montreal Neurological Institute (MNI) template by applying the EPI template at a 3 x3x3mm3 resolution.No participants had head motion exceeding 1.0 mm of maximal displacement and 1.0 of maximal rotation in any direction. To minimize the effects of confounding factors and head motion, we also used a linear regression to regressed out:(i) Six motion parameters, (ii)the cerebrospinal fluid (CSF),(iii) the global mean signal,and(iv) the white matter signalstTwo anatomical regions of interest (ROIs), left insula(BA29),which have shown abnormal activty in PI patients demonstrated by our current study and previous studies, were generated using the software REST, Functional connectivity analysis was performed to explore the cortical connectivity patterns respectively between the seed ROIs and the voxels within the whole brain. For each subject, the seed reference time course was calculated by averaging the time series of all voxels in the ROI, and FC correlation map was acquired respectively by a voxel-wise mul-tiple-regression. Correlation analysis was performed between reference time course of the seed and time series from the whole brain in a voxel-wise way. Then, Z values were converted from the correlation coefficients using Fisher’s transformation to improve normality.Using SPSS 20.0 software (SPSS Inc., Chicago, IL, USA), two-sample two-tailed t-tests were performed to assess the differences in age, duration of education, between PI patients and healthy control subjects. A chi-square test was performed to assess the gender composition between the two groups.Two-sample t-test was also adopted to identify the regions showing between-group differences in connectivity to the seed ROI based on the zFC maps of brain regions (Alphasim corrected,P< 0.05).Result:There were no significant between-group differences in demographic data including age (P=0.859), sex (p=0.618), and education level (P=0.301).In current study, the ROI, left insula(BA29), was selected as seed regions. Compared to the healthy controls, the ROI-based functional connectivity analysis revealed increased functional connectivity with the right frontal sup orb(BA26), right anterior cingulate cortex(BA32), bilateral thalamus and left precuneus(BA67), and decreased functional connectivity with the left middle temporal_gyrus(BA85), and right fusiform(BA56).Conclusion:Our study provides information on functional integration underlying primary insomnia. The aberrant connectivity in emotional circuit might contribute to insomnia and its clinical features. |
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