Computational protocol: Aberrant spontaneous low-frequency brain activity in male patients with severe obstructive sleep apnea revealed by resting-state functional MRI

Similar protocols

Protocol publication

[…] Functional data were checked using MRIcro software (www.MRIcro.com) to exclude defective data. The first ten time points of the functional images were discarded due to the possible instability of the initial MRI signals and the participants’ adaptation time to the scanning environment. On the basis of MATLAB2010a (Mathworks, Natick, MA, USA), the remainder of the data preprocessing was performed by DPARSFA (http://rfmri.org/DPARSF) software, including DICOM form transformation, slice timing, head motion correction, spatial normalization, smoothing with a Gaussian kernel of 6×6×6 mm3 full width at half maximum (FWHM). Motion time courses were obtained by estimating the values for translation (mm) and rotation (degrees) for each subject. Participants who had >1.5 mm maximum displacement in x, y, or z planes and 1.5° of angular motion during the whole fMRI scans were rejected. The Friston six head motion parameters were used to regress out head motion effects based on recent work showing that higher-order models were more effective in removing head motion effects., Linear regression was also applied to remove other sources of spurious covariates along with their temporal derivatives, including the signal from a ventricular region of interest (ROI) and the signal from a region centered in the white matter. Of note, the global signal was not regressed out in the present data, as in the study by Guo et al for the reason that there is still a controversy around the removal of the global signal in the preprocessing step of resting-state data., After head-motion correction, the fMRI images were spatially normalized to the Montreal Neurological Institute (MNI) space using the standard EPI template and resampling the images at a resolution of 3×3×3 mm3. After preprocessing, the time series for each voxel were linearly detrended to reduce low-frequency drift, physiological high-frequency respiratory and cardiac noise, and time series linear detrending. The time series for each voxel were transformed to the frequency domain, and the power spectrum was then obtained. Because the power of a given frequency is proportional to the square of the amplitude of this frequency component, the square root was calculated at each frequency of the power spectrum, and the averaged square root was obtained across 0.01–0.08 Hz at each voxel. This averaged square root was taken as the ALFF. The details of ALFF calculation are as described in a previous study. To reduce the global effects of variability across the participants, the ALFF of each voxel was divided by the global mean ALFF value for each participant. […]

Pipeline specifications

Software tools MRIcro, DPABI
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Brain Diseases, Sleep Apnea, Obstructive, Nocturnal Myoclonus Syndrome
Chemicals Oxygen