Preprocessing of fMRI data involved motion-realignment, linear drift correction (detrending), regression of motion (six motion parameters and first derivatives; Power et al., 2014 (link)) and physiological noise (WM and CSF signal fluctuations extracted from T1 image based tissue probability masks from SPM Dartel segmentation; Chang and Glover, 2009 (link); Birn et al., 2014 (link)) followed by co-registration of functional to individual structural images and normalization to MNI standard space and final smoothing with a Gaussian Kernel (FWHM 6 mm).
Functional MRI Preprocessing Pipeline for Brain Imaging
Preprocessing of fMRI data involved motion-realignment, linear drift correction (detrending), regression of motion (six motion parameters and first derivatives; Power et al., 2014 (link)) and physiological noise (WM and CSF signal fluctuations extracted from T1 image based tissue probability masks from SPM Dartel segmentation; Chang and Glover, 2009 (link); Birn et al., 2014 (link)) followed by co-registration of functional to individual structural images and normalization to MNI standard space and final smoothing with a Gaussian Kernel (FWHM 6 mm).
Corresponding Organization : University of Bern
Other organizations : University of Southern California, Karolinska Institutet
Variable analysis
- Magnetic field strength (3T)
- GE EPI fMRI BOLD signal
- Slice parameters (26 transversal slices, 3.0 × 3.0 in-plane and 4 mm slice thickness)
- TR/TE (1600/35 ms)
- Flip angle (90°)
- Field of view (240 × 240 mm)
- Matrix size (92 × 92)
- Number of volumes (400)
- Acquisition time (10 min 40 s)
- Structural T1-weighted MPRAGE image parameters (176 sagittal slices, 0.9 × 0.9 in-plane and 1 mm slice thickness, TR/TE = 1900/2.57 ms, FoV = 230 × 230 mm, matrix = 256 × 256)
- Preprocessing steps (motion-realignment, linear drift correction, regression of motion and physiological noise, co-registration, normalization to MNI standard space, smoothing with 6 mm FWHM Gaussian Kernel)
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