Computational protocol: Increased default mode network connectivity and increased regional homogeneity in migraineurs without aura

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Protocol publication

[…] Resting-state fMRI data preprocessing was conducted with statistical parametric mapping software (SPM12; http://www.fil.ion.ucl.ac.uk/spm/software/spm12) and MATLAB (The Math Works, Natick, MA) software on a personal computer. For each participant, the first 10 volumes were discarded to avoid scanner instability and to adapt the participants to the noise of the scanner. The remaining volumes were corrected for the intra-volume acquisition time delay using slice-timing and were realigned to the first volume using the six-parameter (rigid body) spatial transformation. After these corrections, we co-registered the high-resolution T1-weighted image to the mean functional image. The T1 images were then segmented into grey matter, white matter and bias field-corrected structural images. Then, the images were spatially normalized to the standard Montreal Neurological Institute (MNI) stereotaxic space and resampled to 3 × 3 × 3 mm. Finally, spatial smoothing was performed on the functional images using a Gaussian filter (8 mm full width half-maximum, FWHM).The head motion parameters of all participants were calculated in the translational and rotational directions (i.e., x, y, z, roll, pitch and yaw). Participants were excluded if their maximum translation was > 2 mm or their rotation was > 2° in any direction; none of the participants exhibited excessive movement. Head motion in all directions was compared between the groups, and we found that the head motion parameters of the migraine and control groups did not significantly differ (x, P = 0.79; y, P = 0.99; z, P = 0.82; pitch, P = 0.62; roll, P = 0.70; yaw, P = 0.99.). [...] We used DPABI v2.1 to conduct the ReHo and FC analysis. Linear trends were removed from the unsmoothed data. Spurious signals, including the time series of six head motion parameters, the white matter and the cerebrospinal fluid were regressed out using a general linear model based on the fMRI data. Then, a temporal band-pass filter (0.01 < f < 0.1 Hz) was applied to reduce the influence of low-frequency drift and high-frequency respiratory and cardiac noise.An individual ReHo map was generated by calculating the concordance of KCC of the time series of a given voxel with those of its 26 nearest neighbours []. To eliminate the effect of individual diversification, the ReHo value of each voxel was converted into a z-score by subtracting the mean ReHo value and dividing by the standard deviationof the whole-brain ReHo map. The standardized ReHo maps were spatially smoothed withan 8 mm FWHM Gaussian kernel. Before evaluating FC, we smoothed the filtered fMRI data with an 8 mm FWHM Gaussian kernel. The brain regions for which ReHo was significantly altered were used as seed regions in the whole-brain FC analysis. Then, we extracted the mean time course from the seed regions (using a 6-mm spherical region of interest (ROI) centred at the peak significant coordinate), and Pearson correlation was used to correlate these time courses with whole-brain voxels. Finally, the FC maps were converted to z-score maps by Fisher Z-transformation.The maps of significant differences in ReHo and FC maps of the 22 migraineurs without aura and the 22 age- and gender-matched controls were compared using voxel-wise two-sample t-tests with age and gender as covariates within a brain mask. The ReHo statistical maps used the same correction method as the ICA statistical analysis to address the issue of multiple comparisons. A stricter correction level (p < 0.005, FDR corrected) was used for the FC statistical maps because we obtained too many clusters if we used the same multiple comparison correction methods described above. The surviving clusters were reported. […]

Pipeline specifications

Software tools SPM, DPABI
Application Functional magnetic resonance imaging
Organisms Homo sapiens