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.
Effect of MP-PCA denoising on a low and high-b image.
Effect of bias correction on a low and high-b image. Bias field contour lines are drawn as an overlay.
The qform has been copied from sform.
b=0 template and final mask output. The t1 and signal intersection mask is blue, their xor is red and the entire mask is plotted in cyan.
Animation of the DWI sampling scheme. Each separate scan is its own color.
antsRegistration was used to generate transformations from the b=0 reference image to the T1w-image.
Summary statistics are plotted.
/usr/local/miniconda/bin/qsiprep /data /out participant -w /work --participant_label 0522566501 --fs-license-file /mhome/license.txt --ignore fieldmaps --output-resolution 2We kindly ask to report results preprocessed with qsiprep using the following boilerplate
Preprocessing was performed using QSIPrep 0.13.0RC2, which is based on Nipype 1.6.0 (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 2.3.1), and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using antsBrainExtraction.sh (ANTs 2.3.1), 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 2.3.1, 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 6.0.3:b862cdd5, RRID:SCR_002823, Zhang, Brady, and Smith 2001).
Any images with a b-value less than 100 s/mm^2 were treated as a b=0 image. MP-PCA denoising as implemented in MRtrix3’s dwidenoise(Veraart et al. 2016) was applied with a 5-voxel window. After MP-PCA, B1 field inhomogeneity was corrected using dwibiascorrect from MRtrix3 with the N4 algorithm (Tustison et al. 2010). After B1 bias correction, the mean intensity of the DWI series was adjusted so all the mean intensity of the b=0 images matched across eachseparate DWI scanning sequence.
FSL (version 6.0.3:b862cdd5)’s eddy was used for head motion correction and Eddy current correction (Andersson and Sotiropoulos 2016). Eddy was configured with a q-space smoothing factor of 10, a total of 5 iterations, and 1000 voxels used to estimate hyperparameters. A linear first level model and a linear second level model were used to characterize Eddy current-related spatial distortion. q-space coordinates were forcefully assigned to shells. Field offset was attempted to be separated from subject movement. Shells were aligned post-eddy. Eddy’s outlier replacement was run (Andersson et al. 2016). Data were grouped by slice, only including values from slices determined to contain at least 250 intracerebral voxels. Groups deviating by more than 4 standard deviations from the prediction had their data replaced with imputed values. Final interpolation was performed using the jac method.
Several confounding time-series were calculated based on the preprocessed DWI: framewise displacement (FD) using the implementation in Nipype (following the definitions by Power et al. 2014). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. Slicewise cross correlation was also calculated. The DWI time-series were resampled to ACPC, generating a preprocessed DWI run in ACPC space with 2mm isotropic voxels.
Many internal operations of QSIPrep use Nilearn 0.7.1 (Abraham et al. 2014, RRID:SCR_001362) and Dipy (Garyfallidis et al. 2014). For more details of the pipeline, see the section corresponding to workflows in QSIPrep’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.
Andersson, Jesper LR, Mark S Graham, Enikő Zsoldos, and Stamatios N Sotiropoulos. 2016. “Incorporating Outlier Detection and Replacement into a Non-Parametric Framework for Movement and Distortion Correction of Diffusion Mr Images.” Neuroimage 141. Elsevier: 556–72.
Andersson, Jesper LR, and Stamatios N Sotiropoulos. 2016. “An Integrated Approach to Correction for Off-Resonance Effects and Subject Movement in Diffusion Mr Imaging.” Neuroimage 125. Elsevier: 1063–78.
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.
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.
Garyfallidis, Eleftherios, Matthew Brett, Bagrat Amirbekian, Ariel Rokem, Stefan Van Der Walt, Maxime Descoteaux, and Ian Nimmo-Smith. 2014. “Dipy, a Library for the Analysis of Diffusion Mri Data.” Frontiers in Neuroinformatics 8. Frontiers: 8.
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.
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.
Veraart, Jelle, Dmitry S Novikov, Daan Christiaens, Benjamin Ades-Aron, Jan Sijbers, and Els Fieremans. 2016. “Denoising of Diffusion Mri Using Random Matrix Theory.” NeuroImage 142. Elsevier: 394–406.
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.
Preprocessing was performed using *QSIPrep* 0.13.0RC2, which is based on *Nipype* 1.6.0 (@nipype1; @nipype2; RRID:SCR_002502). Anatomical data preprocessing : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using `N4BiasFieldCorrection` [@n4, ANTs 2.3.1], and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using `antsBrainExtraction.sh` (ANTs 2.3.1), 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 2.3.1, 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 6.0.3:b862cdd5, RRID:SCR_002823, @fsl_fast]. Diffusion data preprocessing : Any images with a b-value less than 100 s/mm^2 were treated as a *b*=0 image. MP-PCA denoising as implemented in MRtrix3's `dwidenoise`[@dwidenoise1] was applied with a 5-voxel window. After MP-PCA, B1 field inhomogeneity was corrected using `dwibiascorrect` from MRtrix3 with the N4 algorithm [@n4]. After B1 bias correction, the mean intensity of the DWI series was adjusted so all the mean intensity of the b=0 images matched across eachseparate DWI scanning sequence. FSL (version 6.0.3:b862cdd5)'s eddy was used for head motion correction and Eddy current correction [@anderssoneddy]. Eddy was configured with a $q$-space smoothing factor of 10, a total of 5 iterations, and 1000 voxels used to estimate hyperparameters. A linear first level model and a linear second level model were used to characterize Eddy current-related spatial distortion. $q$-space coordinates were forcefully assigned to shells. Field offset was attempted to be separated from subject movement. Shells were aligned post-eddy. Eddy's outlier replacement was run [@eddyrepol]. Data were grouped by slice, only including values from slices determined to contain at least 250 intracerebral voxels. Groups deviating by more than 4 standard deviations from the prediction had their data replaced with imputed values. Final interpolation was performed using the `jac` method. Several confounding time-series were calculated based on the preprocessed DWI: framewise displacement (FD) using the implementation in *Nipype* [following the definitions by @power_fd_dvars]. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. Slicewise cross correlation was also calculated. The DWI time-series were resampled to ACPC, generating a *preprocessed DWI run in ACPC space* with 2mm isotropic voxels. Many internal operations of *QSIPrep* use *Nilearn* 0.7.1 [@nilearn, RRID:SCR_001362] and *Dipy* [@dipy]. For more details of the pipeline, see [the section corresponding to workflows in *QSIPrep*'s documentation](https://qsiprep.readthedocs.io/en/latest/workflows.html "QSIPrep's documentation"). ### References
Preprocessing was performed using \emph{QSIPrep} 0.13.0RC2, which is
based on \emph{Nipype} 1.6.0 (\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 2.3.1]{n4}, and
used as T1w-reference throughout the workflow. The T1w-reference was
then skull-stripped using \texttt{antsBrainExtraction.sh} (ANTs 2.3.1),
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 2.3.1,
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 6.0.3:b862cdd5,
RRID:SCR\_002823,][]{fsl_fast}.
\item[Diffusion data preprocessing]
Any images with a b-value less than 100 s/mm\^{}2 were treated as a
\emph{b}=0 image. MP-PCA denoising as implemented in MRtrix3's
\texttt{dwidenoise}\citep{dwidenoise1} was applied with a 5-voxel
window. After MP-PCA, B1 field inhomogeneity was corrected using
\texttt{dwibiascorrect} from MRtrix3 with the N4 algorithm \citep{n4}.
After B1 bias correction, the mean intensity of the DWI series was
adjusted so all the mean intensity of the b=0 images matched across
eachseparate DWI scanning sequence.
\end{description}
FSL (version 6.0.3:b862cdd5)'s eddy was used for head motion correction
and Eddy current correction \citep{anderssoneddy}. Eddy was configured
with a \(q\)-space smoothing factor of 10, a total of 5 iterations, and
1000 voxels used to estimate hyperparameters. A linear first level model
and a linear second level model were used to characterize Eddy
current-related spatial distortion. \(q\)-space coordinates were
forcefully assigned to shells. Field offset was attempted to be
separated from subject movement. Shells were aligned post-eddy. Eddy's
outlier replacement was run \citep{eddyrepol}. Data were grouped by
slice, only including values from slices determined to contain at least
250 intracerebral voxels. Groups deviating by more than 4 standard
deviations from the prediction had their data replaced with imputed
values. Final interpolation was performed using the \texttt{jac} method.
Several confounding time-series were calculated based on the
preprocessed DWI: framewise displacement (FD) using the implementation
in \emph{Nipype} \citep[following the definitions by][]{power_fd_dvars}.
The head-motion estimates calculated in the correction step were also
placed within the corresponding confounds file. Slicewise cross
correlation was also calculated. The DWI time-series were resampled to
ACPC, generating a \emph{preprocessed DWI run in ACPC space} with 2mm
isotropic voxels.
Many internal operations of \emph{QSIPrep} use \emph{Nilearn} 0.7.1
\citep[RRID:SCR\_001362]{nilearn} and \emph{Dipy} \citep{dipy}. For more
details of the pipeline, see
\href{https://qsiprep.readthedocs.io/en/latest/workflows.html}{the
section corresponding to workflows in \emph{QSIPrep}'s documentation}.
\hypertarget{references}{%
\subsubsection{References}\label{references}}
\bibliography{/usr/local/miniconda/lib/python3.7/site-packages/qsiprep/data/boilerplate.bib}
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Alternatively, an interactive boilerplate generator is available in the documentation website.