To begin, prepare a magnetic resonance imaging or MRI scanner and test its functionality. Set all the parameters for the T1-weighted scanning sequence. Position the participant to begin the scanning.
Next, set the sequence parameters to obtain functional MRI images with gradient echo-planar imaging using an 8-channel sense head coil. Start the functional MRI data acquisition while the participant is chanting, Amitabha Buddha"chanting, Santa Claus"and is in a resting state. Launch the Leipzig image processing and statistical inference algorithm software.
First, perform signal intensity normalization, movement correction, and spatial normalization to MNI space. Then, carry out spatial smoothing with full width at half maximum of six millimeters, and set the temporal high pass filtering with a cutoff frequency of 1 by 90 hertz to remove low frequency drifts in the functional MRI time series. Regress out covariates of no interest, such as global signal fluctuations and movement parameters from the data for each scanning sequence corresponding to the three conditions.
Finally, apply eigenvector centrality mapping or ECM to investigate whole brain functional connectomics with the most influential nodes within a network. Subtract the ECM images of two conditions from one another to produce the contrast image. The functional MRI analysis results indicated that the strongest difference in eigenvector centrality between religious and non-religious chanting was predominantly situated in the posterior cingulate cortex.
Post hoc analysis showed that religious chanting induced higher delta power than non-religious chanting and resting conditions.
