Computational protocol: Altered baseline brain activity in experts measured by amplitude of low frequency fluctuations (ALFF): a resting state fMRI study using expertise model of acupuncturists

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[…] Data preprocessing procedures were carried out using Statistical Parametric Mapping (SPM8) and Data Processing Assistant for Resting-State fMRI (DPARSF) V2.1 Basic Edition (Chao-Gan and Yu-Feng, ). The first 10 volumes were discarded to eliminate non-equilibrium effects of magnetization and allow the participants to adapt to the EPI scanning environment. The images were corrected for the acquisition delay between slices, aligned to the first image of each session for motion correction, spatially normalized and then resampled to 3 mm isotropic voxels. No subjects had head motions exceeding 1 mm of movement or 1°of rotation in any direction. After this, the functional images were spatially smoothed with a 6 mm full width at half maximum Gaussian kernel. Finally, the linear trend was removed and temporal filtering (0.01–0.08 Hz, (Biswal et al., ; Lowe et al., )) were performed on the time series of each voxel to reduce the effect of low-frequency drifts and high-frequency noise. The ALFF analysis was carried out using the REST package, which has been described in previous studies (Yang et al., ; Zang et al., ). Briefly, filtered time series was transformed to the frequency domain using the fast Fourier transform (FFT). Since the power of a given frequency was 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. For standardization, the ALFF of each voxel was divided by the global mean ALFF value for each subject, resulting in a relative ALFF. The standardized ALFF of each voxel then had a value of about 1, as done in PET studies (Raichle et al., ). […]

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