Methods section
Dataset description
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Preprocessing & GLM
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Output example - Preprocessing
Pre processing
The (f)MRI data were pre-processed with bidspm (v2.2.0; https://github.com/cpp-lln-lab/bidspm; DOI: https://doi.org/10.5281/zenodo.3554331) using statistical parametric mapping (SPM12 - 7771; Wellcome Center for Neuroimaging, London, UK; https://www.fil.ion.ucl.ac.uk/spm; RRID:SCR_007037) using MATLAB 9.4.0.813654 (R2018a) on a unix computer (Linux version 5.15.0-53-generic (build@lcy02-amd64-047) (gcc (Ubuntu 11.2.0-19ubuntu1) 11.2.0, GNU ld (GNU Binutils for Ubuntu) 2.38) #59-Ubuntu SMP Mon Oct 17 18:53:30 UTC 2022 ). .
The preprocessing of the functional images was performed in the following order:
removing of dummy scans
slice timing correction
realignment and unwarping
segmentation and skullstripping
normalization MNI space
smoothing
{{nb}} dummy scans were removed to allow for signal stabilization.
Slice timing correction was performed taking the ^th slice as a reference (interpolation: sinc interpolation).
Functional scans from each participant were realigned and unwarped using the mean image as a reference (SPM single pass; number of degrees of freedom: 6 ; cost function: least square) (Friston et al, 1995).
The anatomical image was bias field corrected. The bias field corrected image was segmented and normalized to MNI space (target space: IXI549Space; target resolution: 1 mm; interpolation: 4th degree b-spline) using a unified segmentation.
The tissue probability maps generated by the segmentation were used to skullstripp the bias corrected image removing any voxel with p(gray matter) + p(white matter) + p(CSF) > 0.75.
The mean functional image obtained from realignement was co-registered to the bias corrected anatomical image (number of degrees of freedom: 6 ; cost function: normalized mutual information) (Friston et al, 1995). The transformation matrix from this coregistration was applied to all the functional images.
The deformation field obtained from the segmentation was applied to all the functional images (target space: IXI549Space; target resolution: equal to that used at acquisition; interpolation: 4th degree b-spline).
Preprocessed functional images were spatially smoothed using a 3D gaussian kernel (FWHM = 6 mm).
References
This method section was automatically generated using bidspm (v2.2.0; https://github.com/cpp-lln-lab/bidspm; DOI: https://doi.org/10.5281/zenodo.3554331) and octache (https://github.com/Remi-Gau/Octache).
Output example - GLM subject level
fMRI statistical analysis
The fMRI data were analysed with bidspm (v2.2.0; https://github.com/cpp-lln-lab/bidspm; DOI: https://doi.org/10.5281/zenodo.3554331) using statistical parametric mapping (SPM12 - 7771; Wellcome Center for Neuroimaging, London, UK; https://www.fil.ion.ucl.ac.uk/spm; RRID:SCR_007037) using MATLAB 9.4.0.813654 (R2018a) on a unix computer (Linux version 5.15.0-53-generic (build@lcy02-amd64-047) (gcc (Ubuntu 11.2.0-19ubuntu1) 11.2.0, GNU ld (GNU Binutils for Ubuntu) 2.38) #59-Ubuntu SMP Mon Oct 17 18:53:30 UTC 2022 ).
The input data were the preprocessed BOLD images in IXI549Space space for the task “ facerepetition “.
Run / subject level analysis
At the subject level, we performed a mass univariate analysis with a linear regression at each voxel of the brain, using generalized least squares with a global AR(1) model to account for temporal auto-correlation and a drift fit with discrete cosine transform basis ( 128 seconds cut-off).
Image intensity scaling was done run-wide before statistical modeling such that the mean image would have a mean intracerebral intensity of 100.
We modeled the fMRI experiment in a event design with regressors entered into the run-specific design matrix. The onsets were convolved with SPM canonical hemodynamic response function (HRF) and its temporal and dispersion derivatives for the conditions:
famous_1,famous_2,unfamiliar_1,unfamiliar_2, .
Nuisance covariates included:
trans_?,rot_?,
to account for residual motion artefacts, .
References
This method section was automatically generated using bidspm (v2.2.0; https://github.com/cpp-lln-lab/bidspm; DOI: https://doi.org/10.5281/zenodo.3554331) and octache (https://github.com/Remi-Gau/Octache).