Summary

Anatomical

Anatomical Conformation

Brain mask and brain tissue segmentation of the T1w

This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.

Get figure file: figures/sub-10_dseg.svg

Spatial normalization of the anatomical T1w reference

Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.

Spatial normalization of the T1w image to the MNI152NLin2009cAsym template.

Problem loading figure sub-10/figures/sub-10_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.
Get figure file: figures/sub-10_space-MNI152NLin2009cAsym_T1w.svg

Surface reconstruction

Surfaces (white and pial) reconstructed with FreeSurfer (recon-all) overlaid on the participant's T1w template.

Get figure file: figures/sub-10_desc-reconall_T1w.svg

Functional

Reports for: task mixedgamblestask, run 1.

Summary
  • Repetition time (TR): 2s
  • Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior
  • Single-echo EPI sequence.
  • Slice timing correction: Not applied
  • Susceptibility distortion correction: None
  • Registration: FreeSurfer bbregister (boundary-based registration, BBR) - 6 dof
  • Non-steady-state volumes: 1
Confounds collected

global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, csf_wm, tcompcor, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, a_comp_cor_135, a_comp_cor_136, a_comp_cor_137, a_comp_cor_138, a_comp_cor_139, a_comp_cor_140, a_comp_cor_141, a_comp_cor_142, a_comp_cor_143, a_comp_cor_144, a_comp_cor_145, a_comp_cor_146, a_comp_cor_147, a_comp_cor_148, a_comp_cor_149, a_comp_cor_150, a_comp_cor_151, a_comp_cor_152, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05.

Alignment of functional and anatomical MRI data (surface driven)

bbregister was used to generate transformations from EPI-space to T1w-space. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.

Problem loading figure sub-10/figures/sub-10_task-mixedgamblestask_run-1_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.
Get figure file: figures/sub-10_task-mixedgamblestask_run-1_desc-bbregister_bold.svg

Brain mask and (anatomical/crown/temporal) CompCor ROIs

Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in a/crown/tCompCor for extracting physiological and movement confounding components.
The anatomical CompCor ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.

The crown CompCor ROI (coral contour) corresponds to the crown mask, that is voxels in a closed band around the brain.
The temporal CompCor ROI (blue contour) contains the top 2% most variable voxels within the brain mask.

Get figure file: figures/sub-10_task-mixedgamblestask_run-1_desc-rois_bold.svg

Variance explained by a/crown/tCompCor components

The cumulative variance explained by the first k components of the a/crown/tCompCor decomposition, plotted for all values of k. The number of components that must be included in the model in order to explain some fraction of variance in the decomposition mask can be used as a feature selection criterion for confound regression.

Get figure file: figures/sub-10_task-mixedgamblestask_run-1_desc-compcorvar_bold.svg

BOLD Summary

Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.
A carpet plot shows the time series for all voxels within the brain mask, or if --cifti-output was enabled, all grayordinates. Voxels are grouped into cortical (Ctx GM), subcortical/deep (dGM) gray matter, deep white matter and CSF (dWM+dCSF), shallow white matter and CSF (sWM+sCSF), and cerebellum (Cb).

Get figure file: figures/sub-10_task-mixedgamblestask_run-1_desc-carpetplot_bold.svg

Correlations among nuisance regressors

Left: Heatmap summarizing the correlation structure among confound variables. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.) Right: magnitude of the correlation between each confound time series and the mean global signal. Strong correlations might be indicative of partial volume effects and can inform decisions about feature orthogonalization prior to confound regression.

Get figure file: figures/sub-10_task-mixedgamblestask_run-1_desc-confoundcorr_bold.svg

About

Methods

We kindly ask to report results preprocessed with this tool using the following boilerplate.

Results included in this manuscript come from preprocessing performed using fMRIPrep 20.2.3 (Esteban, Markiewicz, et al. (2018); Esteban, Blair, et al. (2018); RRID:SCR_016216), which is based on Nipype 1.6.1 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502).

Anatomical data preprocessing

A total of 1 T1-weighted (T1w) images were found within the input BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with N4BiasFieldCorrection (Tustison et al. 2010), distributed with ANTs 2.3.3 (Avants et al. 2008, RRID:SCR_004757), and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a Nipype implementation of the antsBrainExtraction.sh workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using fast (FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001). Brain surfaces were reconstructed using recon-all (FreeSurfer 6.0.1, RRID:SCR_001847, Dale, Fischl, and Sereno 1999), and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle (RRID:SCR_002438, Klein et al. 2017). Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with antsRegistration (ANTs 2.3.3), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: ICBM 152 Nonlinear Asymmetrical template version 2009c [Fonov et al. (2009), RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],

Functional data preprocessing

For each of the 3 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using bbregister (FreeSurfer) which implements boundary-based registration (Greve and Fischl 2009). Co-registration was configured with six degrees of freedom. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using mcflirt (FSL 5.0.9, Jenkinson et al. 2002). The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as preprocessed BOLD in original space, or just preprocessed BOLD. The BOLD time-series were resampled into standard space, generating a preprocessed BOLD run in MNI152NLin2009cAsym space. First, a reference volume and its skull-stripped version were generated using a custom methodology of fMRIPrep. Several confounding time-series were calculated based on the preprocessed BOLD: framewise displacement (FD), DVARS and three region-wise global signals. FD was computed using two formulations following Power (absolute sum of relative motions, Power et al. (2014)) and Jenkinson (relative root mean square displacement between affines, Jenkinson et al. (2002)). FD and DVARS are calculated for each functional run, both using their implementations in Nipype (following the definitions by Power et al. 2014). The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction (CompCor, Behzadi et al. 2007). Principal components are estimated after high-pass filtering the preprocessed BOLD time-series (using a discrete cosine filter with 128s cut-off) for the two CompCor variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are subtracted a mask of pixels that likely contain a volume fraction of GM. This mask is obtained by dilating a GM mask extracted from the FreeSurfer’s aseg segmentation, and it ensures components are not extracted from voxels containing a minimal fraction of GM. Finally, these masks are resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the k components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each (Satterthwaite et al. 2013). Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with a single interpolation step by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using antsApplyTransforms (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels (Lanczos 1964). Non-gridded (surface) resamplings were performed using mri_vol2surf (FreeSurfer).

Many internal operations of fMRIPrep use Nilearn 0.6.2 (Abraham et al. 2014, RRID:SCR_001362), mostly within the functional processing workflow. For more details of the pipeline, see the section corresponding to workflows in fMRIPrep’s documentation.

The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts unchanged. It is released under the CC0 license.

References

Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” Frontiers in Neuroinformatics 8. https://doi.org/10.3389/fninf.2014.00014.

Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” Medical Image Analysis 12 (1): 26–41. https://doi.org/10.1016/j.media.2007.06.004.

Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” NeuroImage 37 (1): 90–101. https://doi.org/10.1016/j.neuroimage.2007.04.042.

Dale, Anders M., Bruce Fischl, and Martin I. Sereno. 1999. “Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction.” NeuroImage 9 (2): 179–94. https://doi.org/10.1006/nimg.1998.0395.

Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” Software. Zenodo. https://doi.org/10.5281/zenodo.852659.

Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” Nature Methods. https://doi.org/10.1038/s41592-018-0235-4.

Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” NeuroImage 47, Supplement 1: S102. https://doi.org/10.1016/S1053-8119(09)70884-5.

Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” Frontiers in Neuroinformatics 5: 13. https://doi.org/10.3389/fninf.2011.00013.

Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” Software. Zenodo. https://doi.org/10.5281/zenodo.596855.

Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” NeuroImage 48 (1): 63–72. https://doi.org/10.1016/j.neuroimage.2009.06.060.

Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” NeuroImage 17 (2): 825–41. https://doi.org/10.1006/nimg.2002.1132.

Klein, Arno, Satrajit S. Ghosh, Forrest S. Bao, Joachim Giard, Yrjö Häme, Eliezer Stavsky, Noah Lee, et al. 2017. “Mindboggling Morphometry of Human Brains.” PLOS Computational Biology 13 (2): e1005350. https://doi.org/10.1371/journal.pcbi.1005350.

Lanczos, C. 1964. “Evaluation of Noisy Data.” Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis 1 (1): 76–85. https://doi.org/10.1137/0701007.

Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” NeuroImage 84 (Supplement C): 320–41. https://doi.org/10.1016/j.neuroimage.2013.08.048.

Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” NeuroImage 64 (1): 240–56. https://doi.org/10.1016/j.neuroimage.2012.08.052.

Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” IEEE Transactions on Medical Imaging 29 (6): 1310–20. https://doi.org/10.1109/TMI.2010.2046908.

Zhang, Y., M. Brady, and S. Smith. 2001. “Segmentation of Brain MR Images Through a Hidden Markov Random Field Model and the Expectation-Maximization Algorithm.” IEEE Transactions on Medical Imaging 20 (1): 45–57. https://doi.org/10.1109/42.906424.

Results included in this manuscript come from preprocessing
performed using *fMRIPrep* 20.2.3
(@fmriprep1; @fmriprep2; RRID:SCR_016216),
which is based on *Nipype* 1.6.1
(@nipype1; @nipype2; RRID:SCR_002502).

Anatomical data preprocessing

: A total of 1 T1-weighted (T1w) images were found within the input
BIDS dataset.The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.3.3 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
The T1w-reference was then skull-stripped with a *Nipype* implementation of
the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
as target template.
Brain tissue segmentation of cerebrospinal fluid (CSF),
white-matter (WM) and gray-matter (GM) was performed on
the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
@fsl_fast].
Brain surfaces were reconstructed using `recon-all` [FreeSurfer 6.0.1,
RRID:SCR_001847, @fs_reconall], and the brain mask estimated
previously was refined with a custom variation of the method to reconcile
ANTs-derived and FreeSurfer-derived segmentations of the cortical
gray-matter of Mindboggle [RRID:SCR_002438, @mindboggle].
Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
nonlinear registration with `antsRegistration` (ANTs 2.3.3),
using brain-extracted versions of both T1w reference and the T1w template.
The following template was selected for spatial normalization:
*ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 

Functional data preprocessing

: For each of the 1 BOLD run found per subject (across all
tasks and sessions), the following preprocessing was performed.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
Susceptibility distortion correction (SDC) was omitted.
The BOLD reference was then co-registered to the T1w reference using
`bbregister` (FreeSurfer) which implements boundary-based registration [@bbr].
Co-registration was configured with six degrees of freedom.
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
`mcflirt` [FSL 5.0.9, @mcflirt].
The BOLD time-series (including slice-timing correction when applied)
were resampled onto their original, native space by applying
the transforms to correct for head-motion.
These resampled BOLD time-series will be referred to as *preprocessed
BOLD in original space*, or just *preprocessed BOLD*.
The BOLD time-series were resampled into standard space,
generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
First, a reference volume and its skull-stripped version were generated
 using a custom
methodology of *fMRIPrep*.
Several confounding time-series were calculated based on the
*preprocessed BOLD*: framewise displacement (FD), DVARS and
three region-wise global signals.
FD was computed using two formulations following Power (absolute sum of
relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
displacement between affines, @mcflirt).
FD and DVARS are calculated for each functional run, both using their
implementations in *Nipype* [following the definitions by @power_fd_dvars].
The three global signals are extracted within the CSF, the WM, and
the whole-brain masks.
Additionally, a set of physiological regressors were extracted to
allow for component-based noise correction [*CompCor*, @compcor].
Principal components are estimated after high-pass filtering the
*preprocessed BOLD* time-series (using a discrete cosine filter with
128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
and anatomical (aCompCor).
tCompCor components are then calculated from the top 2% variable
voxels within the brain mask.
For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
are generated in anatomical space.
The implementation differs from that of Behzadi et al. in that instead
of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by dilating a GM mask extracted from the FreeSurfer's *aseg* segmentation, and it ensures components are not extracted
from voxels containing a minimal fraction of GM.
Finally, these masks are resampled into BOLD space and binarized by
thresholding at 0.99 (as in the original implementation).
Components are also calculated separately within the WM and CSF masks.
For each CompCor decomposition, the *k* components with the largest singular
values are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask (CSF,
WM, combined, or temporal). The remaining components are dropped from
consideration.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file.
The confound time series derived from head motion estimates and global
signals were expanded with the inclusion of temporal derivatives and
quadratic terms for each [@confounds_satterthwaite_2013].
Frames that exceeded a threshold of 0.5 mm FD or
1.5 standardised DVARS were annotated as motion outliers.
All resamplings can be performed with *a single interpolation
step* by composing all the pertinent transformations (i.e. head-motion
transform matrices, susceptibility distortion correction when available,
and co-registrations to anatomical and output spaces).
Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
configured with Lanczos interpolation to minimize the smoothing
effects of other kernels [@lanczos].
Non-gridded (surface) resamplings were performed using `mri_vol2surf`
(FreeSurfer).


Many internal operations of *fMRIPrep* use
*Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
mostly within the functional processing workflow.
For more details of the pipeline, see [the section corresponding
to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").


### Copyright Waiver

The above boilerplate text was automatically generated by fMRIPrep
with the express intention that users should copy and paste this
text into their manuscripts *unchanged*.
It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.

### References

Results included in this manuscript come from preprocessing performed
using \emph{fMRIPrep} 20.2.3 (\citet{fmriprep1}; \citet{fmriprep2};
RRID:SCR\_016216), which is based on \emph{Nipype} 1.6.1
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).

\begin{description}
\item[Anatomical data preprocessing]
A total of 1 T1-weighted (T1w) images were found within the input BIDS
dataset.The T1-weighted (T1w) image was corrected for intensity
non-uniformity (INU) with \texttt{N4BiasFieldCorrection} \citep{n4},
distributed with ANTs 2.3.3 \citep[RRID:SCR\_004757]{ants}, and used as
T1w-reference throughout the workflow. The T1w-reference was then
skull-stripped with a \emph{Nipype} implementation of the
\texttt{antsBrainExtraction.sh} workflow (from ANTs), using OASIS30ANTs
as target template. Brain tissue segmentation of cerebrospinal fluid
(CSF), white-matter (WM) and gray-matter (GM) was performed on the
brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
RRID:SCR\_002823,][]{fsl_fast}. Brain surfaces were reconstructed using
\texttt{recon-all} \citep[FreeSurfer 6.0.1,
RRID:SCR\_001847,][]{fs_reconall}, and the brain mask estimated
previously was refined with a custom variation of the method to
reconcile ANTs-derived and FreeSurfer-derived segmentations of the
cortical gray-matter of Mindboggle
\citep[RRID:SCR\_002438,][]{mindboggle}. Volume-based spatial
normalization to one standard space (MNI152NLin2009cAsym) was performed
through nonlinear registration with \texttt{antsRegistration} (ANTs
2.3.3), using brain-extracted versions of both T1w reference and the T1w
template. The following template was selected for spatial normalization:
\emph{ICBM 152 Nonlinear Asymmetrical template version 2009c}
{[}\citet{mni152nlin2009casym}, RRID:SCR\_008796; TemplateFlow ID:
MNI152NLin2009cAsym{]},
\item[Functional data preprocessing]
For each of the 1 BOLD run found per subject (across all tasks and
sessions), the following preprocessing was performed. First, a reference
volume and its skull-stripped version were generated using a custom
methodology of \emph{fMRIPrep}. Susceptibility distortion correction
(SDC) was omitted. The BOLD reference was then co-registered to the T1w
reference using \texttt{bbregister} (FreeSurfer) which implements
boundary-based registration \citep{bbr}. Co-registration was configured
with six degrees of freedom. Head-motion parameters with respect to the
BOLD reference (transformation matrices, and six corresponding rotation
and translation parameters) are estimated before any spatiotemporal
filtering using \texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. The BOLD
time-series (including slice-timing correction when applied) were
resampled onto their original, native space by applying the transforms
to correct for head-motion. These resampled BOLD time-series will be
referred to as \emph{preprocessed BOLD in original space}, or just
\emph{preprocessed BOLD}. The BOLD time-series were resampled into
standard space, generating a \emph{preprocessed BOLD run in
MNI152NLin2009cAsym space}. First, a reference volume and its
skull-stripped version were generated using a custom methodology of
\emph{fMRIPrep}. Several confounding time-series were calculated based
on the \emph{preprocessed BOLD}: framewise displacement (FD), DVARS and
three region-wise global signals. FD was computed using two formulations
following Power (absolute sum of relative motions,
\citet{power_fd_dvars}) and Jenkinson (relative root mean square
displacement between affines, \citet{mcflirt}). FD and DVARS are
calculated for each functional run, both using their implementations in
\emph{Nipype} \citep[following the definitions by][]{power_fd_dvars}.
The three global signals are extracted within the CSF, the WM, and the
whole-brain masks. Additionally, a set of physiological regressors were
extracted to allow for component-based noise correction
\citep[\emph{CompCor},][]{compcor}. Principal components are estimated
after high-pass filtering the \emph{preprocessed BOLD} time-series
(using a discrete cosine filter with 128s cut-off) for the two
\emph{CompCor} variants: temporal (tCompCor) and anatomical (aCompCor).
tCompCor components are then calculated from the top 2\% variable voxels
within the brain mask. For aCompCor, three probabilistic masks (CSF, WM
and combined CSF+WM) are generated in anatomical space. The
implementation differs from that of Behzadi et al.~in that instead of
eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
subtracted a mask of pixels that likely contain a volume fraction of GM.
This mask is obtained by dilating a GM mask extracted from the
FreeSurfer's \emph{aseg} segmentation, and it ensures components are not
extracted from voxels containing a minimal fraction of GM. Finally,
these masks are resampled into BOLD space and binarized by thresholding
at 0.99 (as in the original implementation). Components are also
calculated separately within the WM and CSF masks. For each CompCor
decomposition, the \emph{k} components with the largest singular values
are retained, such that the retained components' time series are
sufficient to explain 50 percent of variance across the nuisance mask
(CSF, WM, combined, or temporal). The remaining components are dropped
from consideration. The head-motion estimates calculated in the
correction step were also placed within the corresponding confounds
file. The confound time series derived from head motion estimates and
global signals were expanded with the inclusion of temporal derivatives
and quadratic terms for each \citep{confounds_satterthwaite_2013}.
Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
were annotated as motion outliers. All resamplings can be performed with
\emph{a single interpolation step} by composing all the pertinent
transformations (i.e.~head-motion transform matrices, susceptibility
distortion correction when available, and co-registrations to anatomical
and output spaces). Gridded (volumetric) resamplings were performed
using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
interpolation to minimize the smoothing effects of other kernels
\citep{lanczos}. Non-gridded (surface) resamplings were performed using
\texttt{mri\_vol2surf} (FreeSurfer).
\end{description}

Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
\citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
processing workflow. For more details of the pipeline, see
\href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
section corresponding to workflows in \emph{fMRIPrep}'s documentation}.

\hypertarget{copyright-waiver}{%
\subsubsection{Copyright Waiver}\label{copyright-waiver}}

The above boilerplate text was automatically generated by fMRIPrep with
the express intention that users should copy and paste this text into
their manuscripts \emph{unchanged}. It is released under the
\href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.

\hypertarget{references}{%
\subsubsection{References}\label{references}}

\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}

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Errors

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