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.
Nonlinear mapping of the T1w image into MNI space. Hover on the panel with the mouse to transition between both spaces.
flirt with boundary-based registration (BBR) metric - 6 dofBrain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.
The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds.
The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.
FSL flirt was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL fast (brain tissue segmentation) was used for BBR
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. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.
flirt with boundary-based registration (BBR) metric - 6 dofBrain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.
The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds.
The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.
FSL flirt was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL fast (brain tissue segmentation) was used for BBR
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. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.
flirt with boundary-based registration (BBR) metric - 6 dofBrain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.
The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds.
The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.
FSL flirt was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL fast (brain tissue segmentation) was used for BBR
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. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.
/usr/local/miniconda/bin/fmriprep /work/04578/delavega/lonestar/datasets/ds001338 /work/04578/delavega/lonestar/fmriprep/out/ds001338 participant --fs-license-file /work/04578/delavega/lonestar/fmriprep/license.txt --fs-no-reconall --participant-label tb3964 tb3929 tb3512 tb4547 tb3240 tb3757 tb3858 tb3592 tb4450 tb3920 tb3810 --nthreads 24 --mem_mb 64000We kindly ask to report results preprocessed with fMRIPrep using the following boilerplate
Results included in this manuscript come from preprocessing performed using fMRIPprep 1.2.6-1 (Esteban, Markiewicz, et al. (2018); Esteban, Blair, et al. (2018); RRID:SCR_016216), which is based on Nipype 1.1.7 (Gorgolewski et al. (2011); Gorgolewski et al. (2018); RRID:SCR_002502).
The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using N4BiasFieldCorrection (Tustison et al. 2010, ANTs 2.2.0), and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using antsBrainExtraction.sh (ANTs 2.2.0), using OASIS as target template. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c (Fonov et al. 2009, RRID:SCR_008796) was performed through nonlinear registration with antsRegistration (ANTs 2.2.0, RRID:SCR_004757, Avants et al. 2008), using brain-extracted versions of both T1w volume and 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).
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. The BOLD reference was then co-registered to the T1w reference using flirt (FSL 5.0.9, Jenkinson and Smith 2001) with the boundary-based registration (Greve and Fischl 2009) cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. 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 a single, composite transform to correct for head-motion and susceptibility distortions. 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 to MNI152NLin2009cAsym 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 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). Six tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, six components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. 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 template 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 0.5.0 (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.
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.
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, Organization for human brain mapping 2009 annual meeting, 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.
Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” Medical Image Analysis 5 (2): 143–56. https://doi.org/10.1016/S1361-8415(01)00036-6.
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.
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 *fMRIPprep* 1.2.6-1 (@fmriprep1; @fmriprep2; RRID:SCR_016216), which is based on *Nipype* 1.1.7 (@nipype1; @nipype2; RRID:SCR_002502). Anatomical data preprocessing : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using `N4BiasFieldCorrection` [@n4, ANTs 2.2.0], and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using `antsBrainExtraction.sh` (ANTs 2.2.0), using OASIS as target template. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c [@mni, RRID:SCR_008796] was performed through nonlinear registration with `antsRegistration` [ANTs 2.2.0, RRID:SCR_004757, @ants], using brain-extracted versions of both T1w volume and 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]. 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*. The BOLD reference was then co-registered to the T1w reference using `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr] cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. 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 a single, composite transform to correct for head-motion and susceptibility distortions. 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 to MNI152NLin2009cAsym 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 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). Six tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, six components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. 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 template 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* 0.5.0 [@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"). ### References
Results included in this manuscript come from preprocessing performed
using \emph{fMRIPprep} 1.2.6-1 (\citet{fmriprep1}; \citet{fmriprep2};
RRID:SCR\_016216), which is based on \emph{Nipype} 1.1.7
(\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
\begin{description}
\item[Anatomical data preprocessing]
The T1-weighted (T1w) image was corrected for intensity non-uniformity
(INU) using \texttt{N4BiasFieldCorrection} \citep[ANTs 2.2.0]{n4}, and
used as T1w-reference throughout the workflow. The T1w-reference was
then skull-stripped using \texttt{antsBrainExtraction.sh} (ANTs 2.2.0),
using OASIS as target template. Spatial normalization to the ICBM 152
Nonlinear Asymmetrical template version 2009c
\citep[RRID:SCR\_008796]{mni} was performed through nonlinear
registration with \texttt{antsRegistration} \citep[ANTs 2.2.0,
RRID:SCR\_004757,][]{ants}, using brain-extracted versions of both T1w
volume and 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}.
\item[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 \emph{fMRIPrep}. The BOLD reference was then
co-registered to the T1w reference using \texttt{flirt} \citep[FSL
5.0.9,][]{flirt} with the boundary-based registration \citep{bbr}
cost-function. Co-registration was configured with nine degrees of
freedom to account for distortions remaining in the BOLD reference.
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 a single, composite transform
to correct for head-motion and susceptibility distortions. 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 to MNI152NLin2009cAsym 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 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). Six tCompCor components are then
calculated from the top 5\% variable voxels within a mask covering the
subcortical regions. This subcortical mask is obtained by heavily
eroding the brain mask, which ensures it does not include cortical GM
regions. For aCompCor, six components are calculated within the
intersection of the aforementioned mask and the union of CSF and WM
masks calculated in T1w space, after their projection to the native
space of each functional run (using the inverse BOLD-to-T1w
transformation). The head-motion estimates calculated in the correction
step were also placed within the corresponding confounds file. 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 template 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} 0.5.0
\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{references}{%
\subsubsection{References}\label{references}}
\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}
@article{fmriprep1,
author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
year = {2018},
doi = {10.1038/s41592-018-0235-4},
journal = {Nature Methods}
}
@article{fmriprep2,
author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
title = {fMRIPrep},
year = 2018,
doi = {10.5281/zenodo.852659},
publisher = {Zenodo},
journal = {Software}
}
@article{nipype1,
author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
doi = {10.3389/fninf.2011.00013},
journal = {Frontiers in Neuroinformatics},
pages = 13,
shorttitle = {Nipype},
title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
volume = 5,
year = 2011
}
@article{nipype2,
author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
title = {Nipype},
year = 2018,
doi = {10.5281/zenodo.596855},
publisher = {Zenodo},
journal = {Software}
}
@article{n4,
author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
doi = {10.1109/TMI.2010.2046908},
issn = {0278-0062},
journal = {IEEE Transactions on Medical Imaging},
number = 6,
pages = {1310-1320},
shorttitle = {N4ITK},
title = {N4ITK: Improved N3 Bias Correction},
volume = 29,
year = 2010
}
@article{fs_reconall,
author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
doi = {10.1006/nimg.1998.0395},
issn = {1053-8119},
journal = {NeuroImage},
number = 2,
pages = {179-194},
shorttitle = {Cortical Surface-Based Analysis},
title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
volume = 9,
year = 1999
}
@article{mindboggle,
author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
doi = {10.1371/journal.pcbi.1005350},
issn = {1553-7358},
journal = {PLOS Computational Biology},
number = 2,
pages = {e1005350},
title = {Mindboggling morphometry of human brains},
url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
volume = 13,
year = 2017
}
@article{mni,
author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
doi = {10.1016/S1053-8119(09)70884-5},
issn = {1053-8119},
journal = {NeuroImage},
pages = {S102},
series = {Organization for Human Brain Mapping 2009 Annual Meeting},
title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
url = {http://www.sciencedirect.com/science/article/pii/S1053811909708845},
volume = {47, Supplement 1},
year = 2009
}
@article{ants,
author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
doi = {10.1016/j.media.2007.06.004},
issn = {1361-8415},
journal = {Medical Image Analysis},
number = 1,
pages = {26-41},
shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
volume = 12,
year = 2008
}
@article{fsl_fast,
author = {Zhang, Y. and Brady, M. and Smith, S.},
doi = {10.1109/42.906424},
issn = {0278-0062},
journal = {IEEE Transactions on Medical Imaging},
number = 1,
pages = {45-57},
title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
volume = 20,
year = 2001
}
@article{fieldmapless1,
author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
doi = {10.3389/fninf.2017.00017},
issn = {1662-5196},
journal = {Frontiers in Neuroinformatics},
language = {English},
title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
volume = 11,
year = 2017
}
@phdthesis{fieldmapless2,
address = {Berlin},
author = {Huntenburg, Julia M.},
language = {eng},
school = {Freie Universität},
title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
type = {Master's Thesis},
url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
year = 2014
}
@article{fieldmapless3,
author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
doi = {10.1371/journal.pone.0152472},
issn = {1932-6203},
journal = {PLOS ONE},
number = 3,
pages = {e0152472},
title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
volume = 11,
year = 2016
}
@article{flirt,
title = {A global optimisation method for robust affine registration of brain images},
volume = {5},
issn = {1361-8415},
url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
doi = {10.1016/S1361-8415(01)00036-6},
number = {2},
urldate = {2018-07-27},
journal = {Medical Image Analysis},
author = {Jenkinson, Mark and Smith, Stephen},
year = {2001},
keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
pages = {143--156}
}
@article{mcflirt,
author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
doi = {10.1006/nimg.2002.1132},
issn = {1053-8119},
journal = {NeuroImage},
number = 2,
pages = {825-841},
title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
volume = 17,
year = 2002
}
@article{bbr,
author = {Greve, Douglas N and Fischl, Bruce},
doi = {10.1016/j.neuroimage.2009.06.060},
issn = {1095-9572},
journal = {NeuroImage},
number = 1,
pages = {63-72},
title = {Accurate and robust brain image alignment using boundary-based registration},
volume = 48,
year = 2009
}
@article{aroma,
author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
doi = {10.1016/j.neuroimage.2015.02.064},
issn = {1053-8119},
journal = {NeuroImage},
number = {Supplement C},
pages = {267-277},
shorttitle = {ICA-AROMA},
title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
volume = 112,
year = 2015
}
@article{power_fd_dvars,
author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
doi = {10.1016/j.neuroimage.2013.08.048},
issn = {1053-8119},
journal = {NeuroImage},
number = {Supplement C},
pages = {320-341},
title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
volume = 84,
year = 2014
}
@article{nilearn,
author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
doi = {10.3389/fninf.2014.00014},
issn = {1662-5196},
journal = {Frontiers in Neuroinformatics},
language = {English},
title = {Machine learning for neuroimaging with scikit-learn},
url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
volume = 8,
year = 2014
}
@article{lanczos,
author = {Lanczos, C.},
doi = {10.1137/0701007},
issn = {0887-459X},
journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
number = 1,
pages = {76-85},
title = {Evaluation of Noisy Data},
url = {http://epubs.siam.org/doi/10.1137/0701007},
volume = 1,
year = 1964
}
@article{compcor,
author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
doi = {10.1016/j.neuroimage.2007.04.042},
issn = {1053-8119},
journal = {NeuroImage},
number = 1,
pages = {90-101},
title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
volume = 37,
year = 2007
}
@article{hcppipelines,
author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
doi = {10.1016/j.neuroimage.2013.04.127},
issn = {1053-8119},
journal = {NeuroImage},
pages = {105-124},
series = {Mapping the Connectome},
title = {The minimal preprocessing pipelines for the Human Connectome Project},
url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
volume = 80,
year = 2013
}
@article{fs_template,
author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
doi = {10.1016/j.neuroimage.2010.07.020},
journal = {NeuroImage},
number = 4,
pages = {1181-1196},
title = {Highly accurate inverse consistent registration: A robust approach},
volume = 53,
year = 2010
}
@article{afni,
author = {Cox, Robert W. and Hyde, James S.},
doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
journal = {NMR in Biomedicine},
number = {4-5},
pages = {171-178},
title = {Software tools for analysis and visualization of fMRI data},
volume = 10,
year = 1997
}
@article{posse_t2s,
author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
journal = {Magnetic Resonance in Medicine},
number = 1,
pages = {87-97},
title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
volume = 42,
year = 1999
}
Alternatively, an interactive boilerplate generator is available in the documentation website.