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Methods
Participants
25 patients meeting diagnostic criteria for insomnia disorder (ID) according to the ICSD-3 (International Classification of Sleep Disorders, 3rd edition) (Sateia, 2014) and 24 age-matched controls were included in this study. We recruited patients with ID from the Sleep Disorders Research Center, Kermanshah University of Medical Sciences, and underwent overnight polysomnography. Sleep disorders (neurological, psychiatric, etc.) were absent in any subjects.
The participants completed the ISI, the PSQI, and the ESS questionnaires to evaluate sleep patterns before the MRI scan. We recruited HCs through local advertisements among those who scored less than 5 on the PSQI. Table 1 has a summary of clinical and demographic data. Participants were excluded if they had any psychiatric or neurologic comorbidity, were on any sleep-affecting medication, were pregnant, or had any contraindication to MR imaging. Participants signed informed consent forms to take part in this study. The ethics committee of the Kermanshah University of Medical Sciences approved the study protocol (IR. KUMS. REC.1399.259).
Image Acquisition
All subjects underwent MRI on a Siemens Magnetom Avanto 1.5 T MRI whole body scanner with an 8-channel head coil. T1-weighted, 3D magnetization-prepared rapid gradient-echo (MP-RAGE) sequence was performed with the following parameters: TR=1950 ms, TE=3.1 ms, flip angle=15°, FOV=256×256mm2, matrix=256×256 mm2, voxel size=1×1×1 mm3, 176 sagittal slices. The multi-shell diffusion imaging data were acquired using a single-shot spin-echo EPI sequence with the following parameters: TR=10,500 ms, TE=106 ms, FOV=256×256 mm2, matrix size=128×128 mm2, fip angle=90°, voxel size=2×2×2 mm3, NEX=2, b-values = 0, 1000 and 2000 s/mm2 with 30 directions of diffusion-weighted sensitizing gradients. A total of 68 slices were used to cover the entire brain. We conducted our neuroimaging assessment when the patients stopped taking any hypnotic medication for at least a week before the imaging assessment.
Data Processing and Connectomes Construction
Initially, DICOM-to-NifTI conversion of all images was performed (Li et al., 2016). Preprocessing of diffusion-weighted images included: denoising the data (Veraart et al., 2016) using MRtrix3 , correction of EPI distortions (Holland et al., 2010), motion and eddy-current distortions (Andersson & Sotiropoulos, 2016) using FSL , bias field correction (Tustison et al., 2010) using ANTs . After preprocessing, fiber orientation distributions (FODs) were estimated for each subject using the Multi-Shell Multi-Tissue Constrained Spherical Deconvolution (MSMT-CSD) algorithm (Jeurissen et al., 2014) using group averaged response functions for white matter, grey matter, and CSF (Dhollander et al., 2016). To create a whole-brain tractogram using the Anatomically Constrained Tractography (ACT) algorithm, 5tt-images with 5 different tissue types (cortical gray matter, subcortical gray matter, white matter, CSF, and pathological tissue) were generated from anatomical data. Next, anatomical images were co-registered to averaged b0 using a flirt toolbox of FSL. Analysis of five million streamlines was performed over the entire brain through probabilistic tractography, which was implemented in the MRtrix. The cortex parcellation with 7 networks for each hemisphere (200 parcels) (Schaefer et al., 2018) was used to generate the structural connectome including the default mode network (DMN; 27 nodes), the limbic network (6 nodes), the visual network (14 nodes), the somatomotor network (SMN;16 nodes), the frontoparietal network (13 nodes), the dorsal attention network (DAN; 13 nodes), and the ventral attention network (VAN; 11 nodes) (Supplementary Figure 1 and Table 1). We added 14 subcortical areas to the atlas using the FIRST command in FSL software. For each participant, 214 × 214 connectivity matrices were derived using the number of streamlines connecting between each pair of regions, where .I