Computational protocol: Multimodal fMRI Resting-State Functional Connectivity in Granulin Mutations: The Case of Fronto-Parietal Dementia

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Protocol publication

[…] All preprocessing steps were carried out using Advanced Data Processing Assistant for Resting-State fMRI (DPARSFA) (http://rfmri.org/DPARSF) which is based on Resting-State fMRI Data Analysis Toolkit (REST, http://www.restfmri.net) and Statistical Parametric Mapping (SPM8) (http://www.fil.ion.ucl.ac.uk/spm). T1-weighted images from all recruited subjects were visually inspected for a qualitative assessment of macroscopic atrophy, and to check for the quality of data before carrying out a quantitative volumetric analysis. For each subject, an iterative combination of segmentations and normalizations (implemented within the “Segment” module in SPM8) produced a GM probability map in Montreal Neurological Institute (MNI) coordinates. To compensate for compression or expansion during warping of images to match the template, GM maps were modulated by multiplying the intensity of each voxel by the local value derived from the deformation field (Jacobian determinants) . All data were then smoothed using a 10 mm FWHM Gaussian kernel. For each subject the first 4 volumes of the fMRI series were discarded to allow for T1 equilibration effects. The remaining 191 volumes were compensated for slice-dependent time shifts, corrected for geometrical displacements according to the estimated head movement and realigned to the first volume. Correction for head motion and head motion scrubbing regressor was also performed. Any subject who had a maximum displacement in any direction larger than 1.5 mm, or a maximum rotation (x,y,z) larger than 1.5° was excluded. All data were subsequently spatially normalized to the T1 unified segmentation template in Montreal Neurological Institute coordinates derived from SPM8 software and resampled to 3×3×3 cubic voxels. A linear regression to remove sources of spurious variances (motion parameters, linear drift and the average time series in the cerebrospinal fluid and white matter regions) was performed. Then, all images were filtered by a phase-insensitive bandpass filter (pass band 0.01–0.08 Hz) to reduce the effect of low frequency drift and high frequency physiological noise. Finally, a spatial smoothing with an isotropic Gaussian kernel (full-width at half-maximum, 8 mm) was applied to reduce spatial noise. This last step was used for all the analyses except for Regional Homogeneity; in fact, previous studies demonstrated that spatial smoothing artificially enhanced the ReHo intensity , . For this reason, in this case, the spatial smoothing was carried out after ReHo calculation. All the preprocessing steps to obtain the functional maps below (ReHo, fALFF, DC) were performed with DPARSFA. […]

Pipeline specifications

Software tools DPABI, REST, SPM
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens
Diseases Brain Diseases, Dementia, Frontotemporal Dementia