sub-tb3132.html 56 KB

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  44. <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
  45. <div class="collapse navbar-collapse">
  46. <ul class="navbar-nav">
  47. <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
  48. <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
  49. <li class="nav-item dropdown">
  50. <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
  51. <div class="dropdown-menu" aria-labelledby="navbarFunctional">
  52. <a class="dropdown-item" href="#task-story1"> Task: story1</a>
  53. <a class="dropdown-item" href="#task-story2"> Task: story2</a>
  54. <a class="dropdown-item" href="#task-story3"> Task: story3</a>
  55. </div>
  56. </li>
  57. <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
  58. <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
  59. <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
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  63. <noscript>
  64. <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
  65. </noscript>
  66. <div id="Summary">
  67. <h1 class="sub-report-title">Summary</h1>
  68. <ul class="elem-desc">
  69. <li>Subject ID: sub-tb3132</li>
  70. <li>Structural images: 1 T1-weighted </li>
  71. <li>Functional series: 3</li>
  72. <ul class="elem-desc">
  73. <li>Task: story1 (1 run)</li>
  74. <li>Task: story2 (1 run)</li>
  75. <li>Task: story3 (1 run)</li>
  76. </ul>
  77. <li>Resampling targets: MNI152NLin2009cAsym, fsaverage5
  78. <li>FreeSurfer reconstruction: Not run</li>
  79. </ul> </div>
  80. <div id="Anatomical">
  81. <h1 class="sub-report-title">Anatomical</h1>
  82. <h3 class="elem-title">Anatomical Conformation</h3>
  83. <ul class="elem-desc">
  84. <li>Input T1w images: 1</li>
  85. <li>Output orientation: RAS</li>
  86. <li>Output dimensions: 208x256x256</li>
  87. <li>Output voxel size: 1mm x 1mm x 1mm</li>
  88. <li>Discarded images: 0</li>
  89. </ul><h3 class="elem-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-desc">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><br /> <div class="elem-image">
  90. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_seg_brainmask.svg">filename:sub-tb3132/figures/sub-tb3132_seg_brainmask.svg</object>
  91. </div>
  92. <div class="elem-filename">
  93. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_seg_brainmask.svg" target="_blank">sub-tb3132/figures/sub-tb3132_seg_brainmask.svg</a>
  94. </div>
  95. <h3 class="elem-title">T1 to MNI registration</h3><p class="elem-desc">Nonlinear mapping of the T1w image into MNI space. Hover on the panel with the mouse to transition between both spaces.<p><br /> <div class="elem-image">
  96. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_t1_2_mni.svg">filename:sub-tb3132/figures/sub-tb3132_t1_2_mni.svg</object>
  97. </div>
  98. <div class="elem-filename">
  99. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_t1_2_mni.svg" target="_blank">sub-tb3132/figures/sub-tb3132_t1_2_mni.svg</a>
  100. </div>
  101. </div>
  102. <div id="Functional">
  103. <h1 class="sub-report-title">Functional</h1>
  104. <div id="task-story1">
  105. <h2 class="run-title">Reports for Task: story1</h2>
  106. <h3 class="elem-title">Summary</h3>
  107. <ul class="elem-desc">
  108. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  109. <li>Slice timing correction: Not applied</li>
  110. <li>Susceptibility distortion correction: None</li>
  111. <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
  112. <li>Functional series resampled to spaces: template, fsaverage5</li>
  113. <li>Confounds collected: csf, white_matter, global_signal, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z</li>
  114. </ul><h3 class="elem-title">ROIs in BOLD space</h3><p class="elem-desc">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> <div class="elem-image">
  115. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story1_rois.svg">
  116. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story1_rois.svg. If the link below works, please try reloading the report in your browser.</object>
  117. </div>
  118. <div class="elem-filename">
  119. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story1_rois.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story1_rois.svg</a>
  120. </div>
  121. <h3 class="elem-title">EPI to T1 registration</h3><p class="elem-desc">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<p> <div class="elem-image">
  122. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story1_flirtbbr.svg">
  123. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story1_flirtbbr.svg. If the link below works, please try reloading the report in your browser.</object>
  124. </div>
  125. <div class="elem-filename">
  126. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story1_flirtbbr.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story1_flirtbbr.svg</a>
  127. </div>
  128. <h3 class="elem-title">BOLD Summary</h3><p class="elem-desc">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> <div class="elem-image">
  129. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story1_carpetplot.svg">
  130. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story1_carpetplot.svg. If the link below works, please try reloading the report in your browser.</object>
  131. </div>
  132. <div class="elem-filename">
  133. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story1_carpetplot.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story1_carpetplot.svg</a>
  134. </div>
  135. </div>
  136. <div id="task-story2">
  137. <h2 class="run-title">Reports for Task: story2</h2>
  138. <h3 class="elem-title">Summary</h3>
  139. <ul class="elem-desc">
  140. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  141. <li>Slice timing correction: Not applied</li>
  142. <li>Susceptibility distortion correction: None</li>
  143. <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
  144. <li>Functional series resampled to spaces: template, fsaverage5</li>
  145. <li>Confounds collected: csf, white_matter, global_signal, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z</li>
  146. </ul><h3 class="elem-title">ROIs in BOLD space</h3><p class="elem-desc">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> <div class="elem-image">
  147. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story2_rois.svg">
  148. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story2_rois.svg. If the link below works, please try reloading the report in your browser.</object>
  149. </div>
  150. <div class="elem-filename">
  151. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story2_rois.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story2_rois.svg</a>
  152. </div>
  153. <h3 class="elem-title">EPI to T1 registration</h3><p class="elem-desc">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<p> <div class="elem-image">
  154. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story2_flirtbbr.svg">
  155. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story2_flirtbbr.svg. If the link below works, please try reloading the report in your browser.</object>
  156. </div>
  157. <div class="elem-filename">
  158. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story2_flirtbbr.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story2_flirtbbr.svg</a>
  159. </div>
  160. <h3 class="elem-title">BOLD Summary</h3><p class="elem-desc">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> <div class="elem-image">
  161. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story2_carpetplot.svg">
  162. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story2_carpetplot.svg. If the link below works, please try reloading the report in your browser.</object>
  163. </div>
  164. <div class="elem-filename">
  165. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story2_carpetplot.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story2_carpetplot.svg</a>
  166. </div>
  167. </div>
  168. <div id="task-story3">
  169. <h2 class="run-title">Reports for Task: story3</h2>
  170. <h3 class="elem-title">Summary</h3>
  171. <ul class="elem-desc">
  172. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  173. <li>Slice timing correction: Not applied</li>
  174. <li>Susceptibility distortion correction: None</li>
  175. <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
  176. <li>Functional series resampled to spaces: template, fsaverage5</li>
  177. <li>Confounds collected: csf, white_matter, global_signal, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, cosine00, cosine01, cosine02, cosine03, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z</li>
  178. </ul><h3 class="elem-title">ROIs in BOLD space</h3><p class="elem-desc">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> <div class="elem-image">
  179. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story3_rois.svg">
  180. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story3_rois.svg. If the link below works, please try reloading the report in your browser.</object>
  181. </div>
  182. <div class="elem-filename">
  183. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story3_rois.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story3_rois.svg</a>
  184. </div>
  185. <h3 class="elem-title">EPI to T1 registration</h3><p class="elem-desc">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<p> <div class="elem-image">
  186. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story3_flirtbbr.svg">
  187. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story3_flirtbbr.svg. If the link below works, please try reloading the report in your browser.</object>
  188. </div>
  189. <div class="elem-filename">
  190. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story3_flirtbbr.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story3_flirtbbr.svg</a>
  191. </div>
  192. <h3 class="elem-title">BOLD Summary</h3><p class="elem-desc">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> <div class="elem-image">
  193. <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3132/figures/sub-tb3132_task-story3_carpetplot.svg">
  194. Problem loading figure sub-tb3132/figures/sub-tb3132_task-story3_carpetplot.svg. If the link below works, please try reloading the report in your browser.</object>
  195. </div>
  196. <div class="elem-filename">
  197. Get figure file: <a href="./sub-tb3132/figures/sub-tb3132_task-story3_carpetplot.svg" target="_blank">sub-tb3132/figures/sub-tb3132_task-story3_carpetplot.svg</a>
  198. </div>
  199. </div>
  200. </div>
  201. <div id="About">
  202. <h1 class="sub-report-title">About</h1>
  203. <ul>
  204. <li>FMRIPrep version: 1.2.6-1</li>
  205. <li>FMRIPrep command: <code>/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 tb3977 tb3784 tb4572 tb3602 tb3132 tb3279 tb3744 tb3846 tb3646 tb3626 tb2994 --nthreads 24 --mem_mb 64000</code></li>
  206. <li>Date preprocessed: 2019-02-04 21:20:22 +0000</li>
  207. </ul>
  208. </div> </div>
  209. <div id="boilerplate">
  210. <h1 class="sub-report-title">Methods</h1>
  211. <p>We kindly ask to report results preprocessed with fMRIPrep using the following
  212. boilerplate</p>
  213. <ul class="nav nav-tabs" id="myTab" role="tablist">
  214. <li class="nav-item">
  215. <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
  216. </li>
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  218. <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
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  220. <li class="nav-item">
  221. <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
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  223. </ul>
  224. <div class="tab-content" id="myTabContent">
  225. <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>fMRIPprep</em> 1.2.6-1 (<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> 1.1.7 (<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>
  226. <dl>
  227. <dt>Anatomical data preprocessing</dt>
  228. <dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010, ANTs 2.2.0)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using <code>antsBrainExtraction.sh</code> (ANTs 2.2.0), using OASIS as target template. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c <span class="citation" data-cites="mni">(Fonov et al. 2009, RRID:SCR_008796)</span> was performed through nonlinear registration with <code>antsRegistration</code> <span class="citation" data-cites="ants">(ANTs 2.2.0, RRID:SCR_004757, Avants et al. 2008)</span>, 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 <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>.</p>
  229. </dd>
  230. <dt>Functional data preprocessing</dt>
  231. <dd><p>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 <em>fMRIPrep</em>. 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 a single, composite transform to correct for head-motion and susceptibility distortions. 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 to MNI152NLin2009cAsym 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). 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 <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 template 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>
  232. </dd>
  233. </dl>
  234. <p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.5.0 <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>
  235. <h3 id="references" class="unnumbered">References</h3>
  236. <div id="refs" class="references">
  237. <div id="ref-nilearn">
  238. <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>
  239. </div>
  240. <div id="ref-ants">
  241. <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>
  242. </div>
  243. <div id="ref-compcor">
  244. <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>
  245. </div>
  246. <div id="ref-fmriprep2">
  247. <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>
  248. </div>
  249. <div id="ref-fmriprep1">
  250. <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>
  251. </div>
  252. <div id="ref-mni">
  253. <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>, Organization for human brain mapping 2009 annual meeting, 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>
  254. </div>
  255. <div id="ref-nipype1">
  256. <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>
  257. </div>
  258. <div id="ref-nipype2">
  259. <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>
  260. </div>
  261. <div id="ref-bbr">
  262. <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>
  263. </div>
  264. <div id="ref-mcflirt">
  265. <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>
  266. </div>
  267. <div id="ref-flirt">
  268. <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>
  269. </div>
  270. <div id="ref-lanczos">
  271. <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>
  272. </div>
  273. <div id="ref-power_fd_dvars">
  274. <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>
  275. </div>
  276. <div id="ref-n4">
  277. <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>
  278. </div>
  279. <div id="ref-fsl_fast">
  280. <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>
  281. </div>
  282. </div></div></div>
  283. <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
  284. Results included in this manuscript come from preprocessing
  285. performed using *fMRIPprep* 1.2.6-1
  286. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
  287. which is based on *Nipype* 1.1.7
  288. (@nipype1; @nipype2; RRID:SCR_002502).
  289. Anatomical data preprocessing
  290. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
  291. using `N4BiasFieldCorrection` [@n4, ANTs 2.2.0],
  292. and used as T1w-reference throughout the workflow.
  293. The T1w-reference was then skull-stripped using `antsBrainExtraction.sh`
  294. (ANTs 2.2.0), using OASIS as target template.
  295. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical
  296. template version 2009c [@mni, RRID:SCR_008796] was performed
  297. through nonlinear registration with `antsRegistration`
  298. [ANTs 2.2.0, RRID:SCR_004757, @ants], using
  299. brain-extracted versions of both T1w volume and template.
  300. Brain tissue segmentation of cerebrospinal fluid (CSF),
  301. white-matter (WM) and gray-matter (GM) was performed on
  302. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
  303. @fsl_fast].
  304. Functional data preprocessing
  305. : For each of the 3 BOLD runs found per subject (across all
  306. tasks and sessions), the following preprocessing was performed.
  307. First, a reference volume and its skull-stripped version were generated
  308. using a custom methodology of *fMRIPrep*.
  309. The BOLD reference was then co-registered to the T1w reference using
  310. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
  311. cost-function.
  312. Co-registration was configured with nine degrees of freedom to account
  313. for distortions remaining in the BOLD reference.
  314. Head-motion parameters with respect to the BOLD reference
  315. (transformation matrices, and six corresponding rotation and translation
  316. parameters) are estimated before any spatiotemporal filtering using
  317. `mcflirt` [FSL 5.0.9, @mcflirt].
  318. The BOLD time-series (including slice-timing correction when applied)
  319. were resampled onto their original, native space by applying
  320. a single, composite transform to correct for head-motion and
  321. susceptibility distortions.
  322. These resampled BOLD time-series will be referred to as *preprocessed
  323. BOLD in original space*, or just *preprocessed BOLD*.
  324. The BOLD time-series were resampled to MNI152NLin2009cAsym standard space,
  325. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
  326. First, a reference volume and its skull-stripped version were generated
  327. using a custom methodology of *fMRIPrep*.
  328. Several confounding time-series were calculated based on the
  329. *preprocessed BOLD*: framewise displacement (FD), DVARS and
  330. three region-wise global signals.
  331. FD and DVARS are calculated for each functional run, both using their
  332. implementations in *Nipype* [following the definitions by @power_fd_dvars].
  333. The three global signals are extracted within the CSF, the WM, and
  334. the whole-brain masks.
  335. Additionally, a set of physiological regressors were extracted to
  336. allow for component-based noise correction [*CompCor*, @compcor].
  337. Principal components are estimated after high-pass filtering the
  338. *preprocessed BOLD* time-series (using a discrete cosine filter with
  339. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
  340. and anatomical (aCompCor).
  341. Six tCompCor components are then calculated from the top 5% variable
  342. voxels within a mask covering the subcortical regions.
  343. This subcortical mask is obtained by heavily eroding the brain mask,
  344. which ensures it does not include cortical GM regions.
  345. For aCompCor, six components are calculated within the intersection of
  346. the aforementioned mask and the union of CSF and WM masks calculated
  347. in T1w space, after their projection to the native space of each
  348. functional run (using the inverse BOLD-to-T1w transformation).
  349. The head-motion estimates calculated in the correction step were also
  350. placed within the corresponding confounds file.
  351. All resamplings can be performed with *a single interpolation
  352. step* by composing all the pertinent transformations (i.e. head-motion
  353. transform matrices, susceptibility distortion correction when available,
  354. and co-registrations to anatomical and template spaces).
  355. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
  356. configured with Lanczos interpolation to minimize the smoothing
  357. effects of other kernels [@lanczos].
  358. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
  359. (FreeSurfer).
  360. Many internal operations of *fMRIPrep* use
  361. *Nilearn* 0.5.0 [@nilearn, RRID:SCR_001362],
  362. mostly within the functional processing workflow.
  363. For more details of the pipeline, see [the section corresponding
  364. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
  365. ### References
  366. </pre>
  367. </div>
  368. <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
  369. using \emph{fMRIPprep} 1.2.6-1 (\citet{fmriprep1}; \citet{fmriprep2};
  370. RRID:SCR\_016216), which is based on \emph{Nipype} 1.1.7
  371. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
  372. \begin{description}
  373. \item[Anatomical data preprocessing]
  374. The T1-weighted (T1w) image was corrected for intensity non-uniformity
  375. (INU) using \texttt{N4BiasFieldCorrection} \citep[ANTs 2.2.0]{n4}, and
  376. used as T1w-reference throughout the workflow. The T1w-reference was
  377. then skull-stripped using \texttt{antsBrainExtraction.sh} (ANTs 2.2.0),
  378. using OASIS as target template. Spatial normalization to the ICBM 152
  379. Nonlinear Asymmetrical template version 2009c
  380. \citep[RRID:SCR\_008796]{mni} was performed through nonlinear
  381. registration with \texttt{antsRegistration} \citep[ANTs 2.2.0,
  382. RRID:SCR\_004757,][]{ants}, using brain-extracted versions of both T1w
  383. volume and template. Brain tissue segmentation of cerebrospinal fluid
  384. (CSF), white-matter (WM) and gray-matter (GM) was performed on the
  385. brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
  386. RRID:SCR\_002823,][]{fsl_fast}.
  387. \item[Functional data preprocessing]
  388. For each of the 3 BOLD runs found per subject (across all tasks and
  389. sessions), the following preprocessing was performed. First, a reference
  390. volume and its skull-stripped version were generated using a custom
  391. methodology of \emph{fMRIPrep}. The BOLD reference was then
  392. co-registered to the T1w reference using \texttt{flirt} \citep[FSL
  393. 5.0.9,][]{flirt} with the boundary-based registration \citep{bbr}
  394. cost-function. Co-registration was configured with nine degrees of
  395. freedom to account for distortions remaining in the BOLD reference.
  396. Head-motion parameters with respect to the BOLD reference
  397. (transformation matrices, and six corresponding rotation and translation
  398. parameters) are estimated before any spatiotemporal filtering using
  399. \texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. The BOLD time-series
  400. (including slice-timing correction when applied) were resampled onto
  401. their original, native space by applying a single, composite transform
  402. to correct for head-motion and susceptibility distortions. These
  403. resampled BOLD time-series will be referred to as \emph{preprocessed
  404. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
  405. time-series were resampled to MNI152NLin2009cAsym standard space,
  406. generating a \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}.
  407. First, a reference volume and its skull-stripped version were generated
  408. using a custom methodology of \emph{fMRIPrep}. Several confounding
  409. time-series were calculated based on the \emph{preprocessed BOLD}:
  410. framewise displacement (FD), DVARS and three region-wise global signals.
  411. FD and DVARS are calculated for each functional run, both using their
  412. implementations in \emph{Nipype} \citep[following the definitions
  413. by][]{power_fd_dvars}. The three global signals are extracted within the
  414. CSF, the WM, and the whole-brain masks. Additionally, a set of
  415. physiological regressors were extracted to allow for component-based
  416. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
  417. components are estimated after high-pass filtering the
  418. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
  419. with 128s cut-off) for the two \emph{CompCor} variants: temporal
  420. (tCompCor) and anatomical (aCompCor). Six tCompCor components are then
  421. calculated from the top 5\% variable voxels within a mask covering the
  422. subcortical regions. This subcortical mask is obtained by heavily
  423. eroding the brain mask, which ensures it does not include cortical GM
  424. regions. For aCompCor, six components are calculated within the
  425. intersection of the aforementioned mask and the union of CSF and WM
  426. masks calculated in T1w space, after their projection to the native
  427. space of each functional run (using the inverse BOLD-to-T1w
  428. transformation). The head-motion estimates calculated in the correction
  429. step were also placed within the corresponding confounds file. All
  430. resamplings can be performed with \emph{a single interpolation step} by
  431. composing all the pertinent transformations (i.e.~head-motion transform
  432. matrices, susceptibility distortion correction when available, and
  433. co-registrations to anatomical and template spaces). Gridded
  434. (volumetric) resamplings were performed using
  435. \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
  436. interpolation to minimize the smoothing effects of other kernels
  437. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
  438. \texttt{mri\_vol2surf} (FreeSurfer).
  439. \end{description}
  440. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.5.0
  441. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
  442. processing workflow. For more details of the pipeline, see
  443. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
  444. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
  445. \hypertarget{references}{%
  446. \subsubsection{References}\label{references}}
  447. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
  448. <h3>Bibliography</h3>
  449. <pre>@article{fmriprep1,
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  452. year = {2018},
  453. doi = {10.1038/s41592-018-0235-4},
  454. journal = {Nature Methods}
  455. }
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  463. }
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  469. shorttitle = {Nipype},
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  471. volume = 5,
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  727. </pre>
  728. </div>
  729. </div>
  730. <p>Alternatively, an interactive <a href="http://fmriprep.readthedocs.io/en/latest/citing.html">boilerplate generator</a> is available in the <a href="https://fmriprep.org">documentation website</a>.</p>
  731. </div>
  732. <div id="errors">
  733. <h1 class="sub-report-title">Errors</h1>
  734. <ul>
  735. <li>No errors to report!</li>
  736. </ul>
  737. </div>
  738. <script type="text/javascript">
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