Computational protocol: Altered intrinsic regional brain activity in male patients with severe obstructive sleep apnea: a resting-state functional magnetic resonance imaging study

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Protocol publication

[…] Functional data were checked using MRIcro software (www.MRIcro.com) to exclude defective data. The first ten time points for the functional images were discarded due to the possible instability of the initial MRI signal and the participants’ adaptation to the scanning environment. On the basis of MATLAB2010a software (Mathworks, Natick, MA, USA), the rest of the data preprocessing was performed using DPARSFA software (http://rfmri.org/DPARSF), including DICOM form transformation, slice timing, head motion correction, and spatial normalization. Motion time courses were obtained by estimating the values for translation (mm) and rotation (degrees) for each subject. Participants who had more than 1.5 mm maximum displacement in x, y, or z and 1.5 degrees of angular motion during whole functional MRI scans were rejected. The six Friston head motion parameters was used to correct for the effects of head motion based on recent work showing that higher-order models are more effective in removing head motion effects., After correction for head motion, the functional MRI images were spatially normalized to the Montreal Neurological Institute space using the standard echo-planar imaging template in Statistical Parametric Mapping 8 (http://www.fil.ion.ucl.ac.uk/spm) software and resampling the images at a resolution of 3 mm ×3 mm ×3 mm. After preprocessing, the time series for each voxel were detrended and filtered (bandpass 0.01–0.08 Hz) to reduce low-frequency drift and high frequency physiological respiratory and cardiac noise and time series linear detrending. ReHo computation based on protocols from previous studies was performed with REST software (http://www.resting-fmri.Sourceforge.net).Individual ReHo maps were generated by calculating Kendall’s coefficient of concordance (KCC) for the time series of a given voxel with those of its nearest neighbors (26 voxels) in a voxel-wise analysis. The computational formula was ReHo=∑(Ri)2−n(R¯)2K2(n3−n)/12,where ReHo is the KCC among the given voxels, ranging from 0 to 1; when a given cluster and its adjacent cluster in a time series is more consistent, the KCC value is more close to 1. K is the voxel number of time series within a measured cluster (the smallest unit of measured ReHo, comprised of more adjacent clusters); here, K =27 (one given voxel which was located in the cubic center plus its adjacent 26 voxels); n is the number of ranks; Ri is the sum rank of the ith time point, where R¯ = (n + 1)K/2 is the mean of the Ri values; the standard ReHo value is the ReHo value of each cluster/mean of the whole brain ReHo value; thus, the individual ReHo map was generated for each dataset. To reduce the influence of individual variations in KCC value, normalization of the ReHo maps was done by dividing the KCC for each voxel by the average KCC of the whole brain. The resulting functional MRI data were then spatially smoothed with a Gaussian kernel of 6×6×6 mm3 full-width at half-maximum. […]

Pipeline specifications

Software tools MRIcro, DPABI, SPM
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Brain Diseases, Sleep Apnea, Obstructive