CITATION.tex 8.0 KB

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  54. \date{}
  55. \begin{document}
  56. Results included in this manuscript come from preprocessing performed
  57. using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
  58. RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
  59. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
  60. \begin{description}
  61. \item[Anatomical data preprocessing]
  62. The T1-weighted (T1w) image was corrected for intensity non-uniformity
  63. (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
  64. ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
  65. throughout the workflow. The T1w-reference was then skull-stripped with
  66. a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
  67. workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
  68. segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
  69. gray-matter (GM) was performed on the brain-extracted T1w using
  70. \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
  71. Volume-based spatial normalization to one standard space
  72. (MNI152NLin2009cAsym) was performed through nonlinear registration with
  73. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
  74. of both T1w reference and the T1w template. The following template was
  75. selected for spatial normalization: \emph{ICBM 152 Nonlinear
  76. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
  77. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
  78. \item[Functional data preprocessing]
  79. For each of the 3 BOLD runs found per subject (across all tasks and
  80. sessions), the following preprocessing was performed. First, a reference
  81. volume and its skull-stripped version were generated using a custom
  82. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
  83. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
  84. reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
  85. boundary-based registration \citep{bbr} cost-function. Co-registration
  86. was configured with nine degrees of freedom to account for distortions
  87. remaining in the BOLD reference. Head-motion parameters with respect to
  88. the BOLD reference (transformation matrices, and six corresponding
  89. rotation and translation parameters) are estimated before any
  90. spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
  91. 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
  92. correction when applied) were resampled onto their original, native
  93. space by applying the transforms to correct for head-motion. These
  94. resampled BOLD time-series will be referred to as \emph{preprocessed
  95. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
  96. time-series were resampled into standard space, generating a
  97. \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
  98. reference volume and its skull-stripped version were generated using a
  99. custom methodology of \emph{fMRIPrep}. Several confounding time-series
  100. were calculated based on the \emph{preprocessed BOLD}: framewise
  101. displacement (FD), DVARS and three region-wise global signals. FD and
  102. DVARS are calculated for each functional run, both using their
  103. implementations in \emph{Nipype} \citep[following the definitions
  104. by][]{power_fd_dvars}. The three global signals are extracted within the
  105. CSF, the WM, and the whole-brain masks. Additionally, a set of
  106. physiological regressors were extracted to allow for component-based
  107. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
  108. components are estimated after high-pass filtering the
  109. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
  110. with 128s cut-off) for the two \emph{CompCor} variants: temporal
  111. (tCompCor) and anatomical (aCompCor). tCompCor components are then
  112. calculated from the top 5\% variable voxels within a mask covering the
  113. subcortical regions. This subcortical mask is obtained by heavily
  114. eroding the brain mask, which ensures it does not include cortical GM
  115. regions. For aCompCor, components are calculated within the intersection
  116. of the aforementioned mask and the union of CSF and WM masks calculated
  117. in T1w space, after their projection to the native space of each
  118. functional run (using the inverse BOLD-to-T1w transformation).
  119. Components are also calculated separately within the WM and CSF masks.
  120. For each CompCor decomposition, the \emph{k} components with the largest
  121. singular values are retained, such that the retained components' time
  122. series are sufficient to explain 50 percent of variance across the
  123. nuisance mask (CSF, WM, combined, or temporal). The remaining components
  124. are dropped from consideration. The head-motion estimates calculated in
  125. the correction step were also placed within the corresponding confounds
  126. file. The confound time series derived from head motion estimates and
  127. global signals were expanded with the inclusion of temporal derivatives
  128. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
  129. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
  130. were annotated as motion outliers. All resamplings can be performed with
  131. \emph{a single interpolation step} by composing all the pertinent
  132. transformations (i.e.~head-motion transform matrices, susceptibility
  133. distortion correction when available, and co-registrations to anatomical
  134. and output spaces). Gridded (volumetric) resamplings were performed
  135. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
  136. interpolation to minimize the smoothing effects of other kernels
  137. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
  138. \texttt{mri\_vol2surf} (FreeSurfer).
  139. \end{description}
  140. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
  141. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
  142. processing workflow. For more details of the pipeline, see
  143. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
  144. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
  145. \hypertarget{copyright-waiver}{%
  146. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
  147. The above boilerplate text was automatically generated by fMRIPrep with
  148. the express intention that users should copy and paste this text into
  149. their manuscripts \emph{unchanged}. It is released under the
  150. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
  151. \hypertarget{references}{%
  152. \subsubsection{References}\label{references}}
  153. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}
  154. \end{document}