sub-S49.html 57 KB

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  1. <?xml version="1.0" encoding="utf-8" ?>
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  49. </head>
  50. <body>
  51. <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
  52. <div class="collapse navbar-collapse">
  53. <ul class="navbar-nav">
  54. <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
  55. <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
  56. <li class="nav-item dropdown">
  57. <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
  58. <div class="dropdown-menu" aria-labelledby="navbarFunctional">
  59. <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
  60. </div>
  61. </li>
  62. <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
  63. <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
  64. <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
  65. </ul>
  66. </div>
  67. </nav>
  68. <noscript>
  69. <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
  70. </noscript>
  71. <div id="Summary">
  72. <h1 class="sub-report-title">Summary</h1>
  73. <div id="datatype-anat_desc-summary_suffix-T1w">
  74. <ul class="elem-desc">
  75. <li>Subject ID: S49</li>
  76. <li>Structural images: 1 T1-weighted </li>
  77. <li>Functional series: 1</li>
  78. <ul class="elem-desc">
  79. <li>Task: localizer (1 run)</li>
  80. </ul>
  81. <li>Standard output spaces: MNI152NLin2009cAsym</li>
  82. <li>Non-standard output spaces: </li>
  83. <li>FreeSurfer reconstruction: Not run</li>
  84. </ul>
  85. </div>
  86. </div>
  87. <div id="Anatomical">
  88. <h1 class="sub-report-title">Anatomical</h1>
  89. <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
  90. <h3 class="elem-title">Anatomical Conformation</h3>
  91. <ul class="elem-desc">
  92. <li>Input T1w images: 1</li>
  93. <li>Output orientation: RAS</li>
  94. <li>Output dimensions: 192x256x128</li>
  95. <li>Output voxel size: 1mm x 1mm x 1.2mm</li>
  96. <li>Discarded images: 0</li>
  97. </ul>
  98. </div>
  99. <div id="datatype-anat_suffix-dseg">
  100. <h3 class="run-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-caption">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.</p> <img class="svg-reportlet" src="./sub-S49/figures/sub-S49_dseg.svg" style="width: 100%" />
  101. </div>
  102. <div class="elem-filename">
  103. Get figure file: <a href="./sub-S49/figures/sub-S49_dseg.svg" target="_blank">sub-S49/figures/sub-S49_dseg.svg</a>
  104. </div>
  105. </div>
  106. <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
  107. <h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">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.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p> <object class="svg-reportlet" type="image/svg+xml" data="./sub-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg">
  108. Problem loading figure sub-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
  109. </div>
  110. <div class="elem-filename">
  111. Get figure file: <a href="./sub-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg</a>
  112. </div>
  113. </div>
  114. </div>
  115. <div id="Functional">
  116. <h1 class="sub-report-title">Functional</h1>
  117. <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
  118. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2> <h3 class="elem-title">Summary</h3>
  119. <ul class="elem-desc">
  120. <li>Repetition time (TR): 2.4s</li>
  121. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  122. <li>Slice timing correction: Not applied</li>
  123. <li>Susceptibility distortion correction: None</li>
  124. <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
  125. <li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, 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, cosine00, cosine01, cosine02, 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_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2</li>
  126. <li>Non-steady-state volumes: 0</li>
  127. </ul>
  128. </div>
  129. <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
  130. <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
  131. <p class="elem-desc">
  132. The qform has been copied from sform.
  133. The difference in angle is 0.
  134. The difference in translation is 0.
  135. </p>
  136. </div>
  137. <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
  138. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. 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.</p> <object class="svg-reportlet" type="image/svg+xml" data="./sub-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg">
  139. Problem loading figure sub-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  140. </div>
  141. <div class="elem-filename">
  142. Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg</a>
  143. </div>
  144. </div>
  145. <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
  146. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p> <img class="svg-reportlet" src="./sub-S49/figures/sub-S49_task-localizer_desc-rois_bold.svg" style="width: 100%" />
  147. </div>
  148. <div class="elem-filename">
  149. Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-rois_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-rois_bold.svg</a>
  150. </div>
  151. </div>
  152. <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
  153. <h3 class="run-title">Variance explained by t/aCompCor components</h3><p class="elem-caption">The cumulative variance explained by the first k components of the <em>t/aCompCor</em> decomposition, plotted for all values of <em>k</em>. 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.</p> <img class="svg-reportlet" src="./sub-S49/figures/sub-S49_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
  154. </div>
  155. <div class="elem-filename">
  156. Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-compcorvar_bold.svg</a>
  157. </div>
  158. </div>
  159. <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
  160. <h3 class="run-title">BOLD Summary</h3><p class="elem-caption">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.<br />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.</p> <img class="svg-reportlet" src="./sub-S49/figures/sub-S49_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
  161. </div>
  162. <div class="elem-filename">
  163. Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-carpetplot_bold.svg</a>
  164. </div>
  165. </div>
  166. <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
  167. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  168. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  169. Right: magnitude of the correlation between each confound time series and the
  170. mean global signal. Strong correlations might be indicative of partial volume
  171. effects and can inform decisions about feature orthogonalization prior to
  172. confound regression.
  173. </p> <img class="svg-reportlet" src="./sub-S49/figures/sub-S49_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
  174. </div>
  175. <div class="elem-filename">
  176. Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-confoundcorr_bold.svg</a>
  177. </div>
  178. </div>
  179. </div>
  180. <div id="About">
  181. <h1 class="sub-report-title">About</h1>
  182. <div id="datatype-anat_desc-about_suffix-T1w">
  183. <ul>
  184. <li>fMRIPrep version: 20.0.6</li>
  185. <li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S49 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
  186. <li>Date preprocessed: 2020-05-14 13:29:09 -0400</li>
  187. </ul>
  188. </div>
  189. </div>
  190. </div>
  191. <div id="boilerplate">
  192. <h1 class="sub-report-title">Methods</h1>
  193. <p>We kindly ask to report results preprocessed with this tool using the following
  194. boilerplate.</p>
  195. <ul class="nav nav-tabs" id="myTab" role="tablist">
  196. <li class="nav-item">
  197. <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
  198. </li>
  199. <li class="nav-item">
  200. <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
  201. </li>
  202. <li class="nav-item">
  203. <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
  204. </li>
  205. </ul>
  206. <div class="tab-content" id="myTabContent">
  207. <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
  208. <dl>
  209. <dt>Anatomical data preprocessing</dt>
  210. <dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> 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 <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
  211. </dd>
  212. <dt>Functional data preprocessing</dt>
  213. <dd><p>For each of the 1 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 <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> 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 <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. 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 <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. 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 <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). 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, 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). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> 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 <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. 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 <em>a single interpolation step</em> 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 <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
  214. </dd>
  215. </dl>
  216. <p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.6.2 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
  217. <h3 id="copyright-waiver">Copyright Waiver</h3>
  218. <p>The above boilerplate text was automatically generated by fMRIPrep with the express intention that users should copy and paste this text into their manuscripts <em>unchanged</em>. It is released under the <a href="https://creativecommons.org/publicdomain/zero/1.0/">CC0</a> license.</p>
  219. <h3 id="references" class="unnumbered">References</h3>
  220. <div id="refs" class="references">
  221. <div id="ref-nilearn">
  222. <p>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.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
  223. </div>
  224. <div id="ref-ants">
  225. <p>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.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
  226. </div>
  227. <div id="ref-compcor">
  228. <p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
  229. </div>
  230. <div id="ref-fmriprep2">
  231. <p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
  232. </div>
  233. <div id="ref-fmriprep1">
  234. <p>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.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
  235. </div>
  236. <div id="ref-mni152nlin2009casym">
  237. <p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
  238. </div>
  239. <div id="ref-nipype1">
  240. <p>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.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
  241. </div>
  242. <div id="ref-nipype2">
  243. <p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
  244. </div>
  245. <div id="ref-bbr">
  246. <p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
  247. </div>
  248. <div id="ref-mcflirt">
  249. <p>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.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
  250. </div>
  251. <div id="ref-flirt">
  252. <p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
  253. </div>
  254. <div id="ref-lanczos">
  255. <p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
  256. </div>
  257. <div id="ref-power_fd_dvars">
  258. <p>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.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
  259. </div>
  260. <div id="ref-confounds_satterthwaite_2013">
  261. <p>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.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
  262. </div>
  263. <div id="ref-n4">
  264. <p>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.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
  265. </div>
  266. <div id="ref-fsl_fast">
  267. <p>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.” <em>IEEE Transactions on Medical Imaging</em> 20 (1): 45–57. <a href="https://doi.org/10.1109/42.906424" class="uri">https://doi.org/10.1109/42.906424</a>.</p>
  268. </div>
  269. </div></div></div>
  270. <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
  271. Results included in this manuscript come from preprocessing
  272. performed using *fMRIPrep* 20.0.6
  273. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
  274. which is based on *Nipype* 1.4.2
  275. (@nipype1; @nipype2; RRID:SCR_002502).
  276. Anatomical data preprocessing
  277. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
  278. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
  279. The T1w-reference was then skull-stripped with a *Nipype* implementation of
  280. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
  281. as target template.
  282. Brain tissue segmentation of cerebrospinal fluid (CSF),
  283. white-matter (WM) and gray-matter (GM) was performed on
  284. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
  285. @fsl_fast].
  286. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
  287. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
  288. using brain-extracted versions of both T1w reference and the T1w template.
  289. The following template was selected for spatial normalization:
  290. *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],
  291. Functional data preprocessing
  292. : For each of the 1 BOLD runs found per subject (across all
  293. tasks and sessions), the following preprocessing was performed.
  294. First, a reference volume and its skull-stripped version were generated
  295. using a custom methodology of *fMRIPrep*.
  296. Susceptibility distortion correction (SDC) was omitted.
  297. The BOLD reference was then co-registered to the T1w reference using
  298. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
  299. cost-function.
  300. Co-registration was configured with nine degrees of freedom to account
  301. for distortions remaining in the BOLD reference.
  302. Head-motion parameters with respect to the BOLD reference
  303. (transformation matrices, and six corresponding rotation and translation
  304. parameters) are estimated before any spatiotemporal filtering using
  305. `mcflirt` [FSL 5.0.9, @mcflirt].
  306. The BOLD time-series (including slice-timing correction when applied)
  307. were resampled onto their original, native space by applying
  308. the transforms to correct for head-motion.
  309. These resampled BOLD time-series will be referred to as *preprocessed
  310. BOLD in original space*, or just *preprocessed BOLD*.
  311. The BOLD time-series were resampled into standard space,
  312. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
  313. First, a reference volume and its skull-stripped version were generated
  314. using a custom methodology of *fMRIPrep*.
  315. Several confounding time-series were calculated based on the
  316. *preprocessed BOLD*: framewise displacement (FD), DVARS and
  317. three region-wise global signals.
  318. FD and DVARS are calculated for each functional run, both using their
  319. implementations in *Nipype* [following the definitions by @power_fd_dvars].
  320. The three global signals are extracted within the CSF, the WM, and
  321. the whole-brain masks.
  322. Additionally, a set of physiological regressors were extracted to
  323. allow for component-based noise correction [*CompCor*, @compcor].
  324. Principal components are estimated after high-pass filtering the
  325. *preprocessed BOLD* time-series (using a discrete cosine filter with
  326. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
  327. and anatomical (aCompCor).
  328. tCompCor components are then calculated from the top 5% variable
  329. voxels within a mask covering the subcortical regions.
  330. This subcortical mask is obtained by heavily eroding the brain mask,
  331. which ensures it does not include cortical GM regions.
  332. For aCompCor, components are calculated within the intersection of
  333. the aforementioned mask and the union of CSF and WM masks calculated
  334. in T1w space, after their projection to the native space of each
  335. functional run (using the inverse BOLD-to-T1w transformation). Components
  336. are also calculated separately within the WM and CSF masks.
  337. For each CompCor decomposition, the *k* components with the largest singular
  338. values are retained, such that the retained components' time series are
  339. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
  340. WM, combined, or temporal). The remaining components are dropped from
  341. consideration.
  342. The head-motion estimates calculated in the correction step were also
  343. placed within the corresponding confounds file.
  344. The confound time series derived from head motion estimates and global
  345. signals were expanded with the inclusion of temporal derivatives and
  346. quadratic terms for each [@confounds_satterthwaite_2013].
  347. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
  348. were annotated as motion outliers.
  349. All resamplings can be performed with *a single interpolation
  350. step* by composing all the pertinent transformations (i.e. head-motion
  351. transform matrices, susceptibility distortion correction when available,
  352. and co-registrations to anatomical and output spaces).
  353. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
  354. configured with Lanczos interpolation to minimize the smoothing
  355. effects of other kernels [@lanczos].
  356. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
  357. (FreeSurfer).
  358. Many internal operations of *fMRIPrep* use
  359. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
  360. mostly within the functional processing workflow.
  361. For more details of the pipeline, see [the section corresponding
  362. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
  363. ### Copyright Waiver
  364. The above boilerplate text was automatically generated by fMRIPrep
  365. with the express intention that users should copy and paste this
  366. text into their manuscripts *unchanged*.
  367. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
  368. ### References
  369. </pre>
  370. </div>
  371. <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
  372. using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
  373. RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
  374. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
  375. \begin{description}
  376. \item[Anatomical data preprocessing]
  377. The T1-weighted (T1w) image was corrected for intensity non-uniformity
  378. (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
  379. ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
  380. throughout the workflow. The T1w-reference was then skull-stripped with
  381. a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
  382. workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
  383. segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
  384. gray-matter (GM) was performed on the brain-extracted T1w using
  385. \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
  386. Volume-based spatial normalization to one standard space
  387. (MNI152NLin2009cAsym) was performed through nonlinear registration with
  388. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
  389. of both T1w reference and the T1w template. The following template was
  390. selected for spatial normalization: \emph{ICBM 152 Nonlinear
  391. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
  392. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
  393. \item[Functional data preprocessing]
  394. For each of the 1 BOLD runs found per subject (across all tasks and
  395. sessions), the following preprocessing was performed. First, a reference
  396. volume and its skull-stripped version were generated using a custom
  397. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
  398. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
  399. reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
  400. boundary-based registration \citep{bbr} cost-function. Co-registration
  401. was configured with nine degrees of freedom to account for distortions
  402. remaining in the BOLD reference. Head-motion parameters with respect to
  403. the BOLD reference (transformation matrices, and six corresponding
  404. rotation and translation parameters) are estimated before any
  405. spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
  406. 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
  407. correction when applied) were resampled onto their original, native
  408. space by applying the transforms to correct for head-motion. These
  409. resampled BOLD time-series will be referred to as \emph{preprocessed
  410. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
  411. time-series were resampled into standard space, generating a
  412. \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
  413. reference volume and its skull-stripped version were generated using a
  414. custom methodology of \emph{fMRIPrep}. Several confounding time-series
  415. were calculated based on the \emph{preprocessed BOLD}: framewise
  416. displacement (FD), DVARS and three region-wise global signals. FD and
  417. DVARS are calculated for each functional run, both using their
  418. implementations in \emph{Nipype} \citep[following the definitions
  419. by][]{power_fd_dvars}. The three global signals are extracted within the
  420. CSF, the WM, and the whole-brain masks. Additionally, a set of
  421. physiological regressors were extracted to allow for component-based
  422. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
  423. components are estimated after high-pass filtering the
  424. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
  425. with 128s cut-off) for the two \emph{CompCor} variants: temporal
  426. (tCompCor) and anatomical (aCompCor). tCompCor components are then
  427. calculated from the top 5\% variable voxels within a mask covering the
  428. subcortical regions. This subcortical mask is obtained by heavily
  429. eroding the brain mask, which ensures it does not include cortical GM
  430. regions. For aCompCor, components are calculated within the intersection
  431. of the aforementioned mask and the union of CSF and WM masks calculated
  432. in T1w space, after their projection to the native space of each
  433. functional run (using the inverse BOLD-to-T1w transformation).
  434. Components are also calculated separately within the WM and CSF masks.
  435. For each CompCor decomposition, the \emph{k} components with the largest
  436. singular values are retained, such that the retained components' time
  437. series are sufficient to explain 50 percent of variance across the
  438. nuisance mask (CSF, WM, combined, or temporal). The remaining components
  439. are dropped from consideration. The head-motion estimates calculated in
  440. the correction step were also placed within the corresponding confounds
  441. file. The confound time series derived from head motion estimates and
  442. global signals were expanded with the inclusion of temporal derivatives
  443. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
  444. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
  445. were annotated as motion outliers. All resamplings can be performed with
  446. \emph{a single interpolation step} by composing all the pertinent
  447. transformations (i.e.~head-motion transform matrices, susceptibility
  448. distortion correction when available, and co-registrations to anatomical
  449. and output spaces). Gridded (volumetric) resamplings were performed
  450. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
  451. interpolation to minimize the smoothing effects of other kernels
  452. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
  453. \texttt{mri\_vol2surf} (FreeSurfer).
  454. \end{description}
  455. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
  456. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
  457. processing workflow. For more details of the pipeline, see
  458. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
  459. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
  460. \hypertarget{copyright-waiver}{%
  461. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
  462. The above boilerplate text was automatically generated by fMRIPrep with
  463. the express intention that users should copy and paste this text into
  464. their manuscripts \emph{unchanged}. It is released under the
  465. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
  466. \hypertarget{references}{%
  467. \subsubsection{References}\label{references}}
  468. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
  469. <h3>Bibliography</h3>
  470. <pre>@article{fmriprep1,
  471. 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},
  472. title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
  473. year = {2018},
  474. doi = {10.1038/s41592-018-0235-4},
  475. journal = {Nature Methods}
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  780. </div>
  781. </div>
  782. </div>
  783. <div id="errors">
  784. <h1 class="sub-report-title">Errors</h1>
  785. <p>No errors to report!</p>
  786. </div>
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