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"description": "Preprocessing Pipeline FMRI data processing was carried out using FEAT (FMRI Expert Analysis Tool) Version 6.00, part of FSL (FMRIB's Software Library, www.fmrib.ox.ac.uk/fsl). The following pre-statistics processing was applied; motion correction using MCFLIRT [Jenkinson 2002]; slice-timing correction using Fourier-space time-series phase- shifting; non-brain removal using BET [Smith 2002]; spatial smoothing using a Gaussian kernel of FWHM 4.0mm; grand-mean intensity normalisation of the entire 4D dataset by a single multiplicative factor; highpass temporal filtering (Gaussian- weighted least-squares straight line fitting, with sigma=50.0s). ICA-based exploratory data analysis was carried out using MELODIC [Beckmann 2004], in order to investigate the possible presence of unexpected artefacts or activation. ICA-FIX was trained on a set of 20 scans that were hand-classified into noise and non-noise components, with the scans randomly selected from 5 bins sorting scans by the amount of average motion present to have high and low motion data in the trained set. The component classification derived from the trained data was then used in ICA-FIX to classify noise and non-noise components from all subject data and non- aggressively remove the noise components. After denoising, ICA-FIX applied a high- pass filter to each subject’s data. Registration to high resolution structural and/or standard space images was carried out using FNIRT [Jenkinson 2001, 2002]. Lastly, average time series were extract from each subject’s data based on brain nodes specified by the atlas then detrended for cubic trends and finally normalized. We used the brain parcellation proposed by Van De Ville.(Van De Ville et al. 2021). Briefly this parcellation includes a Schaefer 400 brain region cortical parcellation (Schaefer 2018, https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal) combined with 16 subcortical regions and 3 cerebellar regions from the HCP release for a total of 419 nodes.",
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},
{
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"url": "http://neurovault.org/collections/19568/",
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"owner": 17835,
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"name": "Diffusion Tensor Imaging in Patients with focal dystonia",
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"description": "Results of Tract-based tractography comparing patients with upper limb dystonia, cervical dystonia and healthy controls. Data was processed using FSL.6.01.10, following the steps:\r\nWe extracted the first b0 volume from the DTI acquisition, corresponding to the anterior-posterior sequence. Using the acquisition with reversed gradient polarity, we applied the Topup tool to estimate susceptibility-induced field distortions and correct them. Through Brain Extraction tool (BET), a binary mask was used for skull extraction and applied to all images in the sequence using a fractional intensity threshold of 0.45. Next, to correct distortions caused by eddy currents—generated by the rapid switching of gradient directions—we used the Eddy Current Correction tool, part of the FMRIB Diffusion Toolbox (FSL). This process aligned the entire DTI sequence to the first b0 reference image using rigid-body registration to correct for head motion. Head motion (translation and rotation) was calculated using the susceptibility motion estimation tool incorporated into the Eddy Current Correction function. After this step, a visual inspection was performed to assess the image quality obtained after preprocessing the corrected DWI image.\r\nDiffusion tensors were estimated for each voxel (DTIFIT) using weighted linear least squares (WLLS) to improve accuracy. Finally, FA, AD, MD, and RD maps were generated and visually inspected for artifacts (e.g., signal dropout, geometric distortion, noise, and gross head motion) to assess the need for data exclusion.\r\nAll FA maps were subsequently refined to create a mean FA skeleton. Skeletonized images for each subject were generated by projecting maximum FA values near the center of the white matter tracts onto the mean FA skeleton. The mean FA skeleton was then thresholded (FA ≤ 0.2) to focus on major fiber bundles. The exact transformations derived from the nonlinear registration of FA maps to standard space (MNI152) were applied to the AD, MD, and RD maps for combined processing of all image volumes.",
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}
]
}