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  51. <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
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  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-figures_desc-summary_session-retest_suffix-bold_task-covertverbgeneration">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">covertverbgeneration</span>.</a>
  60. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-retest_suffix-bold_task-fingerfootlips">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">fingerfootlips</span>.</a>
  61. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-retest_suffix-bold_task-linebisection">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">linebisection</span>.</a>
  62. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-retest_suffix-bold_task-overtverbgeneration">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">overtverbgeneration</span>.</a>
  63. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-retest_suffix-bold_task-overtwordrepetition">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">overtwordrepetition</span>.</a>
  64. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-test_suffix-bold_task-covertverbgeneration">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">covertverbgeneration</span>.</a>
  65. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-test_suffix-bold_task-fingerfootlips">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">fingerfootlips</span>.</a>
  66. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-test_suffix-bold_task-linebisection">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">linebisection</span>.</a>
  67. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-test_suffix-bold_task-overtverbgeneration">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtverbgeneration</span>.</a>
  68. <a class="dropdown-item" href="#datatype-figures_desc-summary_session-test_suffix-bold_task-overtwordrepetition">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtwordrepetition</span>.</a>
  69. </div>
  70. </li>
  71. <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
  72. <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
  73. <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
  74. </ul>
  75. </div>
  76. </nav>
  77. <noscript>
  78. <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
  79. </noscript>
  80. <div id="Summary">
  81. <h1 class="sub-report-title">Summary</h1>
  82. <div id="datatype-figures_desc-summary_suffix-T1w">
  83. <ul class="elem-desc">
  84. <li>Subject ID: 06</li>
  85. <li>Structural images: 2 T1-weighted </li>
  86. <li>Functional series: 10</li>
  87. <ul class="elem-desc">
  88. <li>Task: covertverbgeneration (2 runs)</li>
  89. <li>Task: fingerfootlips (2 runs)</li>
  90. <li>Task: linebisection (2 runs)</li>
  91. <li>Task: overtverbgeneration (2 runs)</li>
  92. <li>Task: overtwordrepetition (2 runs)</li>
  93. </ul>
  94. <li>Standard output spaces: MNI152NLin2009cAsym, MNI152NLin6Asym, fsaverage</li>
  95. <li>Non-standard output spaces: anat</li>
  96. <li>FreeSurfer reconstruction: Run by fMRIPrep</li>
  97. </ul>
  98. </div>
  99. </div>
  100. <div id="Anatomical">
  101. <h1 class="sub-report-title">Anatomical</h1>
  102. <div id="datatype-figures_desc-conform_extension-['.html']_suffix-T1w">
  103. <h3 class="elem-title">Anatomical Conformation</h3>
  104. <ul class="elem-desc">
  105. <li>Input T1w images: 2</li>
  106. <li>Output orientation: RAS</li>
  107. <li>Output dimensions: 256x156x256</li>
  108. <li>Output voxel size: 1mm x 1.3mm x 1mm</li>
  109. <li>Discarded images: 0</li>
  110. </ul>
  111. </div>
  112. <div id="datatype-figures_suffix-dseg">
  113. <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-06/figures/sub-06_dseg.svg" style="width: 100%" />
  114. </div>
  115. <div class="elem-filename">
  116. Get figure file: <a href="./sub-06/figures/sub-06_dseg.svg" target="_blank">sub-06/figures/sub-06_dseg.svg</a>
  117. </div>
  118. </div>
  119. <div id="datatype-figures_regex_search-True_space-.*_suffix-T1w">
  120. <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>MNI152NLin6Asym</code> template.</p> <object class="svg-reportlet" type="image/svg+xml" data="./sub-06/figures/sub-06_space-MNI152NLin6Asym_T1w.svg">
  121. Problem loading figure sub-06/figures/sub-06_space-MNI152NLin6Asym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
  122. </div>
  123. <div class="elem-filename">
  124. Get figure file: <a href="./sub-06/figures/sub-06_space-MNI152NLin6Asym_T1w.svg" target="_blank">sub-06/figures/sub-06_space-MNI152NLin6Asym_T1w.svg</a>
  125. </div>
  126. <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-06/figures/sub-06_space-MNI152NLin2009cAsym_T1w.svg">
  127. Problem loading figure sub-06/figures/sub-06_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
  128. </div>
  129. <div class="elem-filename">
  130. Get figure file: <a href="./sub-06/figures/sub-06_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-06/figures/sub-06_space-MNI152NLin2009cAsym_T1w.svg</a>
  131. </div>
  132. </div>
  133. <div id="datatype-figures_desc-reconall_suffix-T1w">
  134. <h3 class="run-title">Surface reconstruction</h3><p class="elem-caption">Surfaces (white and pial) reconstructed with FreeSurfer (<code>recon-all</code>) overlaid on the participant's T1w template.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_desc-reconall_T1w.svg" style="width: 100%" />
  135. </div>
  136. <div class="elem-filename">
  137. Get figure file: <a href="./sub-06/figures/sub-06_desc-reconall_T1w.svg" target="_blank">sub-06/figures/sub-06_desc-reconall_T1w.svg</a>
  138. </div>
  139. </div>
  140. </div>
  141. <div id="Functional">
  142. <h1 class="sub-report-title">Functional</h1>
  143. <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-covertverbgeneration">
  144. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">covertverbgeneration</span>.</h2> <details open>
  145. <summary>Summary</summary>
  146. <ul class="elem-desc">
  147. <li>Repetition time (TR): 2.5s</li>
  148. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  149. <li>Single-echo EPI sequence.</li>
  150. <li>Slice timing correction: Applied</li>
  151. <li>Susceptibility distortion correction: None</li>
  152. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  153. <li>Non-steady-state volumes: 2</li>
  154. </ul>
  155. </details>
  156. <details>
  157. <summary>Confounds collected</summary><br />
  158. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, 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, 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, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, non_steady_state_outlier01, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_08, aroma_motion_09, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_17, aroma_motion_19, aroma_motion_20, aroma_motion_21, aroma_motion_22, aroma_motion_24, aroma_motion_26, aroma_motion_28, aroma_motion_29, aroma_motion_30, aroma_motion_31, aroma_motion_32, aroma_motion_33, aroma_motion_34, aroma_motion_36, aroma_motion_37, aroma_motion_41.</p>
  159. </details>
  160. </div>
  161. <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-covertverbgeneration">
  162. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg">
  163. Problem loading figure sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  164. </div>
  165. <div class="elem-filename">
  166. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg</a>
  167. </div>
  168. </div>
  169. <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-covertverbgeneration">
  170. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-rois_bold.svg" style="width: 100%" />
  171. </div>
  172. <div class="elem-filename">
  173. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-rois_bold.svg</a>
  174. </div>
  175. </div>
  176. <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-covertverbgeneration">
  177. <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-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
  178. </div>
  179. <div class="elem-filename">
  180. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-compcorvar_bold.svg</a>
  181. </div>
  182. </div>
  183. <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-covertverbgeneration">
  184. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
  185. </div>
  186. <div class="elem-filename">
  187. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-carpetplot_bold.svg</a>
  188. </div>
  189. </div>
  190. <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-covertverbgeneration">
  191. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  192. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  193. Right: magnitude of the correlation between each confound time series and the
  194. mean global signal. Strong correlations might be indicative of partial volume
  195. effects and can inform decisions about feature orthogonalization prior to
  196. confound regression.
  197. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
  198. </div>
  199. <div class="elem-filename">
  200. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-confoundcorr_bold.svg</a>
  201. </div>
  202. </div>
  203. <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-covertverbgeneration">
  204. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  205. black brain outline. Right hand side of each map: time series (top in seconds),
  206. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  207. in green; noise components in red.
  208. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
  209. </div>
  210. <div class="elem-filename">
  211. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-covertverbgeneration_desc-aroma_bold.svg</a>
  212. </div>
  213. </div>
  214. <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-fingerfootlips">
  215. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">fingerfootlips</span>.</h2> <details open>
  216. <summary>Summary</summary>
  217. <ul class="elem-desc">
  218. <li>Repetition time (TR): 2.5s</li>
  219. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  220. <li>Single-echo EPI sequence.</li>
  221. <li>Slice timing correction: Applied</li>
  222. <li>Susceptibility distortion correction: None</li>
  223. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  224. <li>Non-steady-state volumes: 1</li>
  225. </ul>
  226. </details>
  227. <details>
  228. <summary>Confounds collected</summary><br />
  229. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, 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, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_13, aroma_motion_19, aroma_motion_20, aroma_motion_22, aroma_motion_24, aroma_motion_25, aroma_motion_26, aroma_motion_29, aroma_motion_30, aroma_motion_32, aroma_motion_33, aroma_motion_34, aroma_motion_35, aroma_motion_36, aroma_motion_38, aroma_motion_39, aroma_motion_40, aroma_motion_43, aroma_motion_45.</p>
  230. </details>
  231. </div>
  232. <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-fingerfootlips">
  233. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg">
  234. Problem loading figure sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  235. </div>
  236. <div class="elem-filename">
  237. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg</a>
  238. </div>
  239. </div>
  240. <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-fingerfootlips">
  241. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-rois_bold.svg" style="width: 100%" />
  242. </div>
  243. <div class="elem-filename">
  244. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-rois_bold.svg</a>
  245. </div>
  246. </div>
  247. <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-fingerfootlips">
  248. <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-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-compcorvar_bold.svg" style="width: 100%" />
  249. </div>
  250. <div class="elem-filename">
  251. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-compcorvar_bold.svg</a>
  252. </div>
  253. </div>
  254. <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-fingerfootlips">
  255. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-carpetplot_bold.svg" style="width: 100%" />
  256. </div>
  257. <div class="elem-filename">
  258. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-carpetplot_bold.svg</a>
  259. </div>
  260. </div>
  261. <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-fingerfootlips">
  262. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  263. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  264. Right: magnitude of the correlation between each confound time series and the
  265. mean global signal. Strong correlations might be indicative of partial volume
  266. effects and can inform decisions about feature orthogonalization prior to
  267. confound regression.
  268. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-confoundcorr_bold.svg" style="width: 100%" />
  269. </div>
  270. <div class="elem-filename">
  271. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-confoundcorr_bold.svg</a>
  272. </div>
  273. </div>
  274. <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-fingerfootlips">
  275. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  276. black brain outline. Right hand side of each map: time series (top in seconds),
  277. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  278. in green; noise components in red.
  279. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-aroma_bold.svg" style="width: 100%" />
  280. </div>
  281. <div class="elem-filename">
  282. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-fingerfootlips_desc-aroma_bold.svg</a>
  283. </div>
  284. </div>
  285. <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-linebisection">
  286. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">linebisection</span>.</h2> <details open>
  287. <summary>Summary</summary>
  288. <ul class="elem-desc">
  289. <li>Repetition time (TR): 2.5s</li>
  290. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  291. <li>Single-echo EPI sequence.</li>
  292. <li>Slice timing correction: Applied</li>
  293. <li>Susceptibility distortion correction: None</li>
  294. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  295. <li>Non-steady-state volumes: 1</li>
  296. </ul>
  297. </details>
  298. <details>
  299. <summary>Confounds collected</summary><br />
  300. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, t_comp_cor_07, 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, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_11, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_16, aroma_motion_17, aroma_motion_18, aroma_motion_19, aroma_motion_21, aroma_motion_22, aroma_motion_25, aroma_motion_27, aroma_motion_28, aroma_motion_30, aroma_motion_31, aroma_motion_32, aroma_motion_35, aroma_motion_36, aroma_motion_38, aroma_motion_39, aroma_motion_40, aroma_motion_41, aroma_motion_43, aroma_motion_44, aroma_motion_46, aroma_motion_47, aroma_motion_48, aroma_motion_50, aroma_motion_51.</p>
  301. </details>
  302. </div>
  303. <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-linebisection">
  304. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-retest_task-linebisection_desc-bbregister_bold.svg">
  305. Problem loading figure sub-06/figures/sub-06_ses-retest_task-linebisection_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  306. </div>
  307. <div class="elem-filename">
  308. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-linebisection_desc-bbregister_bold.svg</a>
  309. </div>
  310. </div>
  311. <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-linebisection">
  312. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-rois_bold.svg" style="width: 100%" />
  313. </div>
  314. <div class="elem-filename">
  315. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-linebisection_desc-rois_bold.svg</a>
  316. </div>
  317. </div>
  318. <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-linebisection">
  319. <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-06/figures/sub-06_ses-retest_task-linebisection_desc-compcorvar_bold.svg" style="width: 100%" />
  320. </div>
  321. <div class="elem-filename">
  322. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-linebisection_desc-compcorvar_bold.svg</a>
  323. </div>
  324. </div>
  325. <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-linebisection">
  326. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-retest_task-linebisection_desc-carpetplot_bold.svg" style="width: 100%" />
  327. </div>
  328. <div class="elem-filename">
  329. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-linebisection_desc-carpetplot_bold.svg</a>
  330. </div>
  331. </div>
  332. <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-linebisection">
  333. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  334. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  335. Right: magnitude of the correlation between each confound time series and the
  336. mean global signal. Strong correlations might be indicative of partial volume
  337. effects and can inform decisions about feature orthogonalization prior to
  338. confound regression.
  339. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-confoundcorr_bold.svg" style="width: 100%" />
  340. </div>
  341. <div class="elem-filename">
  342. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-linebisection_desc-confoundcorr_bold.svg</a>
  343. </div>
  344. </div>
  345. <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-linebisection">
  346. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  347. black brain outline. Right hand side of each map: time series (top in seconds),
  348. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  349. in green; noise components in red.
  350. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-aroma_bold.svg" style="width: 100%" />
  351. </div>
  352. <div class="elem-filename">
  353. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-linebisection_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-linebisection_desc-aroma_bold.svg</a>
  354. </div>
  355. </div>
  356. <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-overtverbgeneration">
  357. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">overtverbgeneration</span>.</h2> <details open>
  358. <summary>Summary</summary>
  359. <ul class="elem-desc">
  360. <li>Repetition time (TR): 5s</li>
  361. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  362. <li>Single-echo EPI sequence.</li>
  363. <li>Slice timing correction: Applied</li>
  364. <li>Susceptibility distortion correction: None</li>
  365. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  366. <li>Non-steady-state volumes: 1</li>
  367. </ul>
  368. </details>
  369. <details>
  370. <summary>Confounds collected</summary><br />
  371. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, 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, 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, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_20, aroma_motion_21, aroma_motion_22, aroma_motion_26, aroma_motion_27, aroma_motion_28, aroma_motion_29, aroma_motion_31, aroma_motion_32, aroma_motion_33.</p>
  372. </details>
  373. </div>
  374. <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-overtverbgeneration">
  375. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg">
  376. Problem loading figure sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  377. </div>
  378. <div class="elem-filename">
  379. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg</a>
  380. </div>
  381. </div>
  382. <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-overtverbgeneration">
  383. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-rois_bold.svg" style="width: 100%" />
  384. </div>
  385. <div class="elem-filename">
  386. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-rois_bold.svg</a>
  387. </div>
  388. </div>
  389. <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-overtverbgeneration">
  390. <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-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
  391. </div>
  392. <div class="elem-filename">
  393. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-compcorvar_bold.svg</a>
  394. </div>
  395. </div>
  396. <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-overtverbgeneration">
  397. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
  398. </div>
  399. <div class="elem-filename">
  400. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-carpetplot_bold.svg</a>
  401. </div>
  402. </div>
  403. <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-overtverbgeneration">
  404. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  405. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  406. Right: magnitude of the correlation between each confound time series and the
  407. mean global signal. Strong correlations might be indicative of partial volume
  408. effects and can inform decisions about feature orthogonalization prior to
  409. confound regression.
  410. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
  411. </div>
  412. <div class="elem-filename">
  413. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-confoundcorr_bold.svg</a>
  414. </div>
  415. </div>
  416. <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-overtverbgeneration">
  417. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  418. black brain outline. Right hand side of each map: time series (top in seconds),
  419. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  420. in green; noise components in red.
  421. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
  422. </div>
  423. <div class="elem-filename">
  424. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtverbgeneration_desc-aroma_bold.svg</a>
  425. </div>
  426. </div>
  427. <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-overtwordrepetition">
  428. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">overtwordrepetition</span>.</h2> <details open>
  429. <summary>Summary</summary>
  430. <ul class="elem-desc">
  431. <li>Repetition time (TR): 5s</li>
  432. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  433. <li>Single-echo EPI sequence.</li>
  434. <li>Slice timing correction: Applied</li>
  435. <li>Susceptibility distortion correction: None</li>
  436. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  437. <li>Non-steady-state volumes: 1</li>
  438. </ul>
  439. </details>
  440. <details>
  441. <summary>Confounds collected</summary><br />
  442. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, 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, 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, cosine00, cosine01, cosine02, cosine03, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, aroma_motion_15, aroma_motion_18, aroma_motion_19, aroma_motion_20, aroma_motion_26, aroma_motion_27, aroma_motion_29.</p>
  443. </details>
  444. </div>
  445. <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-overtwordrepetition">
  446. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg">
  447. Problem loading figure sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  448. </div>
  449. <div class="elem-filename">
  450. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg</a>
  451. </div>
  452. </div>
  453. <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-overtwordrepetition">
  454. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-rois_bold.svg" style="width: 100%" />
  455. </div>
  456. <div class="elem-filename">
  457. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-rois_bold.svg</a>
  458. </div>
  459. </div>
  460. <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-overtwordrepetition">
  461. <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-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg" style="width: 100%" />
  462. </div>
  463. <div class="elem-filename">
  464. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg</a>
  465. </div>
  466. </div>
  467. <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-overtwordrepetition">
  468. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg" style="width: 100%" />
  469. </div>
  470. <div class="elem-filename">
  471. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg</a>
  472. </div>
  473. </div>
  474. <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-overtwordrepetition">
  475. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  476. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  477. Right: magnitude of the correlation between each confound time series and the
  478. mean global signal. Strong correlations might be indicative of partial volume
  479. effects and can inform decisions about feature orthogonalization prior to
  480. confound regression.
  481. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg" style="width: 100%" />
  482. </div>
  483. <div class="elem-filename">
  484. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg</a>
  485. </div>
  486. </div>
  487. <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-overtwordrepetition">
  488. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  489. black brain outline. Right hand side of each map: time series (top in seconds),
  490. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  491. in green; noise components in red.
  492. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg" style="width: 100%" />
  493. </div>
  494. <div class="elem-filename">
  495. Get figure file: <a href="./sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg</a>
  496. </div>
  497. </div>
  498. <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-covertverbgeneration">
  499. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">covertverbgeneration</span>.</h2> <details open>
  500. <summary>Summary</summary>
  501. <ul class="elem-desc">
  502. <li>Repetition time (TR): 2.5s</li>
  503. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  504. <li>Single-echo EPI sequence.</li>
  505. <li>Slice timing correction: Applied</li>
  506. <li>Susceptibility distortion correction: None</li>
  507. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  508. <li>Non-steady-state volumes: 1</li>
  509. </ul>
  510. </details>
  511. <details>
  512. <summary>Confounds collected</summary><br />
  513. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, 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, 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, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_11, aroma_motion_12, aroma_motion_14, aroma_motion_15, aroma_motion_19, aroma_motion_20, aroma_motion_21, aroma_motion_22, aroma_motion_25, aroma_motion_26, aroma_motion_27, aroma_motion_29, aroma_motion_31.</p>
  514. </details>
  515. </div>
  516. <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-covertverbgeneration">
  517. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg">
  518. Problem loading figure sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  519. </div>
  520. <div class="elem-filename">
  521. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg</a>
  522. </div>
  523. </div>
  524. <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-covertverbgeneration">
  525. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-rois_bold.svg" style="width: 100%" />
  526. </div>
  527. <div class="elem-filename">
  528. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-rois_bold.svg</a>
  529. </div>
  530. </div>
  531. <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-covertverbgeneration">
  532. <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-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
  533. </div>
  534. <div class="elem-filename">
  535. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg</a>
  536. </div>
  537. </div>
  538. <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-covertverbgeneration">
  539. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
  540. </div>
  541. <div class="elem-filename">
  542. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg</a>
  543. </div>
  544. </div>
  545. <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-covertverbgeneration">
  546. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  547. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  548. Right: magnitude of the correlation between each confound time series and the
  549. mean global signal. Strong correlations might be indicative of partial volume
  550. effects and can inform decisions about feature orthogonalization prior to
  551. confound regression.
  552. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
  553. </div>
  554. <div class="elem-filename">
  555. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg</a>
  556. </div>
  557. </div>
  558. <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-covertverbgeneration">
  559. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  560. black brain outline. Right hand side of each map: time series (top in seconds),
  561. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  562. in green; noise components in red.
  563. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
  564. </div>
  565. <div class="elem-filename">
  566. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-covertverbgeneration_desc-aroma_bold.svg</a>
  567. </div>
  568. </div>
  569. <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-fingerfootlips">
  570. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">fingerfootlips</span>.</h2> <details open>
  571. <summary>Summary</summary>
  572. <ul class="elem-desc">
  573. <li>Repetition time (TR): 2.5s</li>
  574. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  575. <li>Single-echo EPI sequence.</li>
  576. <li>Slice timing correction: Applied</li>
  577. <li>Susceptibility distortion correction: None</li>
  578. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  579. <li>Non-steady-state volumes: 1</li>
  580. </ul>
  581. </details>
  582. <details>
  583. <summary>Confounds collected</summary><br />
  584. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, 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, cosine03, cosine04, cosine05, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_07, aroma_motion_08, aroma_motion_11, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_17, aroma_motion_19, aroma_motion_21, aroma_motion_22, aroma_motion_24, aroma_motion_29, aroma_motion_30, aroma_motion_31, aroma_motion_32, aroma_motion_34, aroma_motion_35, aroma_motion_36, aroma_motion_37, aroma_motion_40.</p>
  585. </details>
  586. </div>
  587. <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-fingerfootlips">
  588. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-test_task-fingerfootlips_desc-bbregister_bold.svg">
  589. Problem loading figure sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  590. </div>
  591. <div class="elem-filename">
  592. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-bbregister_bold.svg</a>
  593. </div>
  594. </div>
  595. <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-fingerfootlips">
  596. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-rois_bold.svg" style="width: 100%" />
  597. </div>
  598. <div class="elem-filename">
  599. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-rois_bold.svg</a>
  600. </div>
  601. </div>
  602. <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-fingerfootlips">
  603. <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-06/figures/sub-06_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg" style="width: 100%" />
  604. </div>
  605. <div class="elem-filename">
  606. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg</a>
  607. </div>
  608. </div>
  609. <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-fingerfootlips">
  610. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg" style="width: 100%" />
  611. </div>
  612. <div class="elem-filename">
  613. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg</a>
  614. </div>
  615. </div>
  616. <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-fingerfootlips">
  617. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  618. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  619. Right: magnitude of the correlation between each confound time series and the
  620. mean global signal. Strong correlations might be indicative of partial volume
  621. effects and can inform decisions about feature orthogonalization prior to
  622. confound regression.
  623. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg" style="width: 100%" />
  624. </div>
  625. <div class="elem-filename">
  626. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg</a>
  627. </div>
  628. </div>
  629. <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-fingerfootlips">
  630. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  631. black brain outline. Right hand side of each map: time series (top in seconds),
  632. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  633. in green; noise components in red.
  634. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-aroma_bold.svg" style="width: 100%" />
  635. </div>
  636. <div class="elem-filename">
  637. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-fingerfootlips_desc-aroma_bold.svg</a>
  638. </div>
  639. </div>
  640. <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-linebisection">
  641. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">linebisection</span>.</h2> <details open>
  642. <summary>Summary</summary>
  643. <ul class="elem-desc">
  644. <li>Repetition time (TR): 2.5s</li>
  645. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  646. <li>Single-echo EPI sequence.</li>
  647. <li>Slice timing correction: Applied</li>
  648. <li>Susceptibility distortion correction: None</li>
  649. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  650. <li>Non-steady-state volumes: 1</li>
  651. </ul>
  652. </details>
  653. <details>
  654. <summary>Confounds collected</summary><br />
  655. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, 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, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17, motion_outlier18, motion_outlier19, motion_outlier20, motion_outlier21, motion_outlier22, motion_outlier23, motion_outlier24, motion_outlier25, motion_outlier26, motion_outlier27, motion_outlier28, motion_outlier29, motion_outlier30, motion_outlier31, motion_outlier32, motion_outlier33, motion_outlier34, motion_outlier35, motion_outlier36, motion_outlier37, motion_outlier38, motion_outlier39, aroma_motion_01, aroma_motion_02, aroma_motion_04, aroma_motion_05, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_16, aroma_motion_17, aroma_motion_20, aroma_motion_21, aroma_motion_22, aroma_motion_23, aroma_motion_24, aroma_motion_25, aroma_motion_26, aroma_motion_27, aroma_motion_28, aroma_motion_29, aroma_motion_30, aroma_motion_31, aroma_motion_38, aroma_motion_39, aroma_motion_40, aroma_motion_42, aroma_motion_43, aroma_motion_45, aroma_motion_46.</p>
  656. </details>
  657. </div>
  658. <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-linebisection">
  659. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-test_task-linebisection_desc-bbregister_bold.svg">
  660. Problem loading figure sub-06/figures/sub-06_ses-test_task-linebisection_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  661. </div>
  662. <div class="elem-filename">
  663. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-linebisection_desc-bbregister_bold.svg</a>
  664. </div>
  665. </div>
  666. <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-linebisection">
  667. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-rois_bold.svg" style="width: 100%" />
  668. </div>
  669. <div class="elem-filename">
  670. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-linebisection_desc-rois_bold.svg</a>
  671. </div>
  672. </div>
  673. <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-linebisection">
  674. <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-06/figures/sub-06_ses-test_task-linebisection_desc-compcorvar_bold.svg" style="width: 100%" />
  675. </div>
  676. <div class="elem-filename">
  677. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-linebisection_desc-compcorvar_bold.svg</a>
  678. </div>
  679. </div>
  680. <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-linebisection">
  681. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-test_task-linebisection_desc-carpetplot_bold.svg" style="width: 100%" />
  682. </div>
  683. <div class="elem-filename">
  684. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-linebisection_desc-carpetplot_bold.svg</a>
  685. </div>
  686. </div>
  687. <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-linebisection">
  688. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  689. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  690. Right: magnitude of the correlation between each confound time series and the
  691. mean global signal. Strong correlations might be indicative of partial volume
  692. effects and can inform decisions about feature orthogonalization prior to
  693. confound regression.
  694. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-confoundcorr_bold.svg" style="width: 100%" />
  695. </div>
  696. <div class="elem-filename">
  697. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-linebisection_desc-confoundcorr_bold.svg</a>
  698. </div>
  699. </div>
  700. <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-linebisection">
  701. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  702. black brain outline. Right hand side of each map: time series (top in seconds),
  703. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  704. in green; noise components in red.
  705. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-aroma_bold.svg" style="width: 100%" />
  706. </div>
  707. <div class="elem-filename">
  708. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-linebisection_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-linebisection_desc-aroma_bold.svg</a>
  709. </div>
  710. </div>
  711. <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-overtverbgeneration">
  712. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtverbgeneration</span>.</h2> <details open>
  713. <summary>Summary</summary>
  714. <ul class="elem-desc">
  715. <li>Repetition time (TR): 5s</li>
  716. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  717. <li>Single-echo EPI sequence.</li>
  718. <li>Slice timing correction: Applied</li>
  719. <li>Susceptibility distortion correction: None</li>
  720. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  721. <li>Non-steady-state volumes: 1</li>
  722. </ul>
  723. </details>
  724. <details>
  725. <summary>Confounds collected</summary><br />
  726. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, 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, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_12, aroma_motion_14, aroma_motion_16, aroma_motion_19, aroma_motion_20, aroma_motion_26, aroma_motion_27, aroma_motion_28, aroma_motion_29.</p>
  727. </details>
  728. </div>
  729. <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-overtverbgeneration">
  730. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg">
  731. Problem loading figure sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  732. </div>
  733. <div class="elem-filename">
  734. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg</a>
  735. </div>
  736. </div>
  737. <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-overtverbgeneration">
  738. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-rois_bold.svg" style="width: 100%" />
  739. </div>
  740. <div class="elem-filename">
  741. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-rois_bold.svg</a>
  742. </div>
  743. </div>
  744. <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-overtverbgeneration">
  745. <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-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
  746. </div>
  747. <div class="elem-filename">
  748. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg</a>
  749. </div>
  750. </div>
  751. <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-overtverbgeneration">
  752. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
  753. </div>
  754. <div class="elem-filename">
  755. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg</a>
  756. </div>
  757. </div>
  758. <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-overtverbgeneration">
  759. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  760. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  761. Right: magnitude of the correlation between each confound time series and the
  762. mean global signal. Strong correlations might be indicative of partial volume
  763. effects and can inform decisions about feature orthogonalization prior to
  764. confound regression.
  765. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
  766. </div>
  767. <div class="elem-filename">
  768. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg</a>
  769. </div>
  770. </div>
  771. <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-overtverbgeneration">
  772. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  773. black brain outline. Right hand side of each map: time series (top in seconds),
  774. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  775. in green; noise components in red.
  776. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
  777. </div>
  778. <div class="elem-filename">
  779. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtverbgeneration_desc-aroma_bold.svg</a>
  780. </div>
  781. </div>
  782. <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-overtwordrepetition">
  783. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtwordrepetition</span>.</h2> <details open>
  784. <summary>Summary</summary>
  785. <ul class="elem-desc">
  786. <li>Repetition time (TR): 5s</li>
  787. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  788. <li>Single-echo EPI sequence.</li>
  789. <li>Slice timing correction: Applied</li>
  790. <li>Susceptibility distortion correction: None</li>
  791. <li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
  792. <li>Non-steady-state volumes: 1</li>
  793. </ul>
  794. </details>
  795. <details>
  796. <summary>Confounds collected</summary><br />
  797. <p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, 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, cosine00, cosine01, cosine02, cosine03, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_derivative1_power2, trans_x_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, aroma_motion_12, aroma_motion_13, aroma_motion_21, aroma_motion_22, aroma_motion_23, aroma_motion_24, aroma_motion_27.</p>
  798. </details>
  799. </div>
  800. <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-overtwordrepetition">
  801. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption"><code>bbregister</code> was used to generate transformations from EPI-space to T1w-space. 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-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg">
  802. Problem loading figure sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  803. </div>
  804. <div class="elem-filename">
  805. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg</a>
  806. </div>
  807. </div>
  808. <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-overtwordrepetition">
  809. <h3 class="run-title">Brain mask and (anatomical/temporal) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the regions of interest (ROIs) used in <em>a/tCompCor</em> for extracting physiological and movement confounding components.<br /> The <em>anatomical CompCor</em> ROI (magenta contour) is a mask combining CSF and WM (white-matter), where voxels containing a minimal partial volume of GM have been removed.<br /> The <em>temporal CompCor</em> ROI (blue contour) contains the top 2% most variable voxels within the brain mask.</p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-rois_bold.svg" style="width: 100%" />
  810. </div>
  811. <div class="elem-filename">
  812. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-rois_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-rois_bold.svg</a>
  813. </div>
  814. </div>
  815. <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-overtwordrepetition">
  816. <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-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg" style="width: 100%" />
  817. </div>
  818. <div class="elem-filename">
  819. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg</a>
  820. </div>
  821. </div>
  822. <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-overtwordrepetition">
  823. <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, or if <code>--cifti-output</code> was enabled, all grayordinates. Voxels are grouped into cortical (dark/light 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-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg" style="width: 100%" />
  824. </div>
  825. <div class="elem-filename">
  826. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg</a>
  827. </div>
  828. </div>
  829. <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-overtwordrepetition">
  830. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  831. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  832. Right: magnitude of the correlation between each confound time series and the
  833. mean global signal. Strong correlations might be indicative of partial volume
  834. effects and can inform decisions about feature orthogonalization prior to
  835. confound regression.
  836. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg" style="width: 100%" />
  837. </div>
  838. <div class="elem-filename">
  839. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg</a>
  840. </div>
  841. </div>
  842. <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-overtwordrepetition">
  843. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
  844. black brain outline. Right hand side of each map: time series (top in seconds),
  845. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
  846. in green; noise components in red.
  847. </p> <img class="svg-reportlet" src="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-aroma_bold.svg" style="width: 100%" />
  848. </div>
  849. <div class="elem-filename">
  850. Get figure file: <a href="./sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-aroma_bold.svg" target="_blank">sub-06/figures/sub-06_ses-test_task-overtwordrepetition_desc-aroma_bold.svg</a>
  851. </div>
  852. </div>
  853. </div>
  854. <div id="About">
  855. <h1 class="sub-report-title">About</h1>
  856. <div id="datatype-figures_desc-about_suffix-T1w">
  857. <ul>
  858. <li>fMRIPrep version: 20.1.1+79.g1a72777b</li>
  859. <li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /oak/stanford/groups/russpold/data/openneuro.org/ds000114 /oak/stanford/groups/russpold/data/openneuro.org/derivatives/ds000114/fmriprep-20.2.0rc0 participant --participant-label 06 -w /lscratch/work/ --skip_bids_validation --omp-nthreads 8 --nthreads 12 --mem_mb 30000 --output-spaces anat --cifti-output --use-aroma --fs-subjects-dir /oak/stanford/groups/russpold/data/openneuro.org/derivatives/ds000114/freesurfer-6.0.1 --notrack -vv</code></li>
  860. <li>Date preprocessed: 2020-07-24 23:39:56 -0700</li>
  861. </ul>
  862. </div>
  863. </div>
  864. </div>
  865. <div id="boilerplate">
  866. <h1 class="sub-report-title">Methods</h1>
  867. <p>We kindly ask to report results preprocessed with this tool using the following
  868. boilerplate.</p>
  869. <ul class="nav nav-tabs" id="myTab" role="tablist">
  870. <li class="nav-item">
  871. <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
  872. </li>
  873. <li class="nav-item">
  874. <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
  875. </li>
  876. <li class="nav-item">
  877. <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
  878. </li>
  879. </ul>
  880. <div class="tab-content" id="myTabContent">
  881. <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> 20.1.1+79.g1a72777b (<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> 1.5.0 (<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>
  882. <dl>
  883. <dt>Anatomical data preprocessing</dt>
  884. <dd><p>A total of 2 T1-weighted (T1w) images were found within the input BIDS dataset. All of them were 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>. 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>. A T1w-reference map was computed after registration of 2 T1w images (after INU-correction) using <code>mri_robust_template</code> <span class="citation" data-cites="fs_template">(FreeSurfer 6.0.1, Reuter, Rosas, and Fischl 2010)</span>. Brain surfaces were reconstructed using <code>recon-all</code> <span class="citation" data-cites="fs_reconall">(FreeSurfer 6.0.1, RRID:SCR_001847, Dale, Fischl, and Sereno 1999)</span>, and the brain mask estimated previously was refined with a custom variation of the method to reconcile ANTs-derived and FreeSurfer-derived segmentations of the cortical gray-matter of Mindboggle <span class="citation" data-cites="mindboggle">(RRID:SCR_002438, Klein et al. 2017)</span>. Volume-based spatial normalization to two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) 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 templates were 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], <em>FSL’s MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model</em> [<span class="citation" data-cites="mni152nlin6asym">Evans et al. (2012)</span>, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym],</p>
  885. </dd>
  886. <dt>Functional data preprocessing</dt>
  887. <dd><p>For each of the 10 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>bbregister</code> (FreeSurfer) which implements boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span>. Co-registration was configured with six degrees of freedom. 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>. BOLD runs were slice-time corrected using <code>3dTshift</code> from AFNI 20160207 <span class="citation" data-cites="afni">(Cox and Hyde 1997, RRID:SCR_005927)</span>. The BOLD time-series were resampled onto the following surfaces (FreeSurfer reconstruction nomenclature): <em>fsaverage</em>. 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>. <em>Grayordinates</em> files <span class="citation" data-cites="hcppipelines">(Glasser et al. 2013)</span> containing 91k samples were also generated using the highest-resolution <code>fsaverage</code> as intermediate standardized surface space. Automatic removal of motion artifacts using independent component analysis <span class="citation" data-cites="aroma">(ICA-AROMA, Pruim et al. 2015)</span> was performed on the <em>preprocessed BOLD on MNI space</em> time-series after removal of non-steady state volumes and spatial smoothing with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum). Corresponding “non-aggresively” denoised runs were produced after such smoothing. Additionally, the “aggressive” noise-regressors were collected and placed in the corresponding confounds file. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD was computed using two formulations following Power (absolute sum of relative motions, <span class="citation" data-cites="power_fd_dvars">Power et al. (2014)</span>) and Jenkinson (relative root mean square displacement between affines, <span class="citation" data-cites="mcflirt">Jenkinson et al. (2002)</span>). 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 2% variable voxels within the brain mask. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM) are generated in anatomical space. The implementation differs from that of Behzadi et al. in that instead of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are subtracted a mask of pixels that likely contain a volume fraction of GM. This mask is obtained by dilating a GM mask extracted from the FreeSurfer’s <em>aseg</em> segmentation, and it ensures components are not extracted from voxels containing a minimal fraction of GM. Finally, these masks are resampled into BOLD space and binarized by thresholding at 0.99 (as in the original implementation). 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>
  888. </dd>
  889. </dl>
  890. <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>
  891. <h3 id="copyright-waiver">Copyright Waiver</h3>
  892. <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>
  893. <h3 id="references" class="unnumbered">References</h3>
  894. <div id="refs" class="references">
  895. <div id="ref-nilearn">
  896. <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>
  897. </div>
  898. <div id="ref-ants">
  899. <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>
  900. </div>
  901. <div id="ref-compcor">
  902. <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>
  903. </div>
  904. <div id="ref-afni">
  905. <p>Cox, Robert W., and James S. Hyde. 1997. “Software Tools for Analysis and Visualization of fMRI Data.” <em>NMR in Biomedicine</em> 10 (4-5): 171–78. <a href="https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5&lt;171::AID-NBM453&gt;3.0.CO;2-L" class="uri">https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5&lt;171::AID-NBM453&gt;3.0.CO;2-L</a>.</p>
  906. </div>
  907. <div id="ref-fs_reconall">
  908. <p>Dale, Anders M., Bruce Fischl, and Martin I. Sereno. 1999. “Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction.” <em>NeuroImage</em> 9 (2): 179–94. <a href="https://doi.org/10.1006/nimg.1998.0395" class="uri">https://doi.org/10.1006/nimg.1998.0395</a>.</p>
  909. </div>
  910. <div id="ref-fmriprep2">
  911. <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>
  912. </div>
  913. <div id="ref-fmriprep1">
  914. <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>
  915. </div>
  916. <div id="ref-mni152nlin6asym">
  917. <p>Evans, AC, AL Janke, DL Collins, and S Baillet. 2012. “Brain Templates and Atlases.” <em>NeuroImage</em> 62 (2): 911–22. <a href="https://doi.org/10.1016/j.neuroimage.2012.01.024" class="uri">https://doi.org/10.1016/j.neuroimage.2012.01.024</a>.</p>
  918. </div>
  919. <div id="ref-mni152nlin2009casym">
  920. <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>
  921. </div>
  922. <div id="ref-hcppipelines">
  923. <p>Glasser, Matthew F., Stamatios N. Sotiropoulos, J. Anthony Wilson, Timothy S. Coalson, Bruce Fischl, Jesper L. Andersson, Junqian Xu, et al. 2013. “The Minimal Preprocessing Pipelines for the Human Connectome Project.” <em>NeuroImage</em>, Mapping the connectome, 80: 105–24. <a href="https://doi.org/10.1016/j.neuroimage.2013.04.127" class="uri">https://doi.org/10.1016/j.neuroimage.2013.04.127</a>.</p>
  924. </div>
  925. <div id="ref-nipype1">
  926. <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>
  927. </div>
  928. <div id="ref-nipype2">
  929. <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>
  930. </div>
  931. <div id="ref-bbr">
  932. <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>
  933. </div>
  934. <div id="ref-mcflirt">
  935. <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>
  936. </div>
  937. <div id="ref-mindboggle">
  938. <p>Klein, Arno, Satrajit S. Ghosh, Forrest S. Bao, Joachim Giard, Yrjö Häme, Eliezer Stavsky, Noah Lee, et al. 2017. “Mindboggling Morphometry of Human Brains.” <em>PLOS Computational Biology</em> 13 (2): e1005350. <a href="https://doi.org/10.1371/journal.pcbi.1005350" class="uri">https://doi.org/10.1371/journal.pcbi.1005350</a>.</p>
  939. </div>
  940. <div id="ref-lanczos">
  941. <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>
  942. </div>
  943. <div id="ref-power_fd_dvars">
  944. <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>
  945. </div>
  946. <div id="ref-aroma">
  947. <p>Pruim, Raimon H. R., Maarten Mennes, Daan van Rooij, Alberto Llera, Jan K. Buitelaar, and Christian F. Beckmann. 2015. “ICA-AROMA: A Robust ICA-Based Strategy for Removing Motion Artifacts from fMRI Data.” <em>NeuroImage</em> 112 (Supplement C): 267–77. <a href="https://doi.org/10.1016/j.neuroimage.2015.02.064" class="uri">https://doi.org/10.1016/j.neuroimage.2015.02.064</a>.</p>
  948. </div>
  949. <div id="ref-fs_template">
  950. <p>Reuter, Martin, Herminia Diana Rosas, and Bruce Fischl. 2010. “Highly Accurate Inverse Consistent Registration: A Robust Approach.” <em>NeuroImage</em> 53 (4): 1181–96. <a href="https://doi.org/10.1016/j.neuroimage.2010.07.020" class="uri">https://doi.org/10.1016/j.neuroimage.2010.07.020</a>.</p>
  951. </div>
  952. <div id="ref-confounds_satterthwaite_2013">
  953. <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>
  954. </div>
  955. <div id="ref-n4">
  956. <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>
  957. </div>
  958. <div id="ref-fsl_fast">
  959. <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>
  960. </div>
  961. </div></div></div>
  962. <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
  963. Results included in this manuscript come from preprocessing
  964. performed using *fMRIPrep* 20.1.1+79.g1a72777b
  965. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
  966. which is based on *Nipype* 1.5.0
  967. (@nipype1; @nipype2; RRID:SCR_002502).
  968. Anatomical data preprocessing
  969. : A total of 2 T1-weighted (T1w) images were found within the input
  970. BIDS dataset.
  971. All of them were corrected for intensity non-uniformity (INU)
  972. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757].
  973. The T1w-reference was then skull-stripped with a *Nipype* implementation of
  974. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
  975. as target template.
  976. Brain tissue segmentation of cerebrospinal fluid (CSF),
  977. white-matter (WM) and gray-matter (GM) was performed on
  978. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
  979. @fsl_fast].
  980. A T1w-reference map was computed after registration of
  981. 2 T1w images (after INU-correction) using
  982. `mri_robust_template` [FreeSurfer 6.0.1, @fs_template].
  983. Brain surfaces were reconstructed using `recon-all` [FreeSurfer 6.0.1,
  984. RRID:SCR_001847, @fs_reconall], and the brain mask estimated
  985. previously was refined with a custom variation of the method to reconcile
  986. ANTs-derived and FreeSurfer-derived segmentations of the cortical
  987. gray-matter of Mindboggle [RRID:SCR_002438, @mindboggle].
  988. Volume-based spatial normalization to two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was performed through
  989. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
  990. using brain-extracted versions of both T1w reference and the T1w template.
  991. The following templates were selected for spatial normalization:
  992. *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], *FSL's MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain Stereotaxic Registration Model* [@mni152nlin6asym, RRID:SCR_002823; TemplateFlow ID: MNI152NLin6Asym],
  993. Functional data preprocessing
  994. : For each of the 10 BOLD runs found per subject (across all
  995. tasks and sessions), the following preprocessing was performed.
  996. First, a reference volume and its skull-stripped version were generated
  997. using a custom
  998. methodology of *fMRIPrep*.
  999. Susceptibility distortion correction (SDC) was omitted.
  1000. The BOLD reference was then co-registered to the T1w reference using
  1001. `bbregister` (FreeSurfer) which implements boundary-based registration [@bbr].
  1002. Co-registration was configured with six degrees of freedom.
  1003. Head-motion parameters with respect to the BOLD reference
  1004. (transformation matrices, and six corresponding rotation and translation
  1005. parameters) are estimated before any spatiotemporal filtering using
  1006. `mcflirt` [FSL 5.0.9, @mcflirt].
  1007. BOLD runs were slice-time corrected using `3dTshift` from
  1008. AFNI 20160207 [@afni, RRID:SCR_005927].
  1009. The BOLD time-series were resampled onto the following surfaces
  1010. (FreeSurfer reconstruction nomenclature):
  1011. *fsaverage*.
  1012. The BOLD time-series (including slice-timing correction when applied)
  1013. were resampled onto their original, native space by applying
  1014. the transforms to correct for head-motion.
  1015. These resampled BOLD time-series will be referred to as *preprocessed
  1016. BOLD in original space*, or just *preprocessed BOLD*.
  1017. *Grayordinates* files [@hcppipelines] containing 91k samples were also
  1018. generated using the highest-resolution ``fsaverage`` as intermediate standardized
  1019. surface space.
  1020. Automatic removal of motion artifacts using independent component analysis
  1021. [ICA-AROMA, @aroma] was performed on the *preprocessed BOLD on MNI space*
  1022. time-series after removal of non-steady state volumes and spatial smoothing
  1023. with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum).
  1024. Corresponding "non-aggresively" denoised runs were produced after such
  1025. smoothing.
  1026. Additionally, the "aggressive" noise-regressors were collected and placed
  1027. in the corresponding confounds file.
  1028. Several confounding time-series were calculated based on the
  1029. *preprocessed BOLD*: framewise displacement (FD), DVARS and
  1030. three region-wise global signals.
  1031. FD was computed using two formulations following Power (absolute sum of
  1032. relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
  1033. displacement between affines, @mcflirt).
  1034. FD and DVARS are calculated for each functional run, both using their
  1035. implementations in *Nipype* [following the definitions by @power_fd_dvars].
  1036. The three global signals are extracted within the CSF, the WM, and
  1037. the whole-brain masks.
  1038. Additionally, a set of physiological regressors were extracted to
  1039. allow for component-based noise correction [*CompCor*, @compcor].
  1040. Principal components are estimated after high-pass filtering the
  1041. *preprocessed BOLD* time-series (using a discrete cosine filter with
  1042. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
  1043. and anatomical (aCompCor).
  1044. tCompCor components are then calculated from the top 2% variable
  1045. voxels within the brain mask.
  1046. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
  1047. are generated in anatomical space.
  1048. The implementation differs from that of Behzadi et al. in that instead
  1049. of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
  1050. subtracted a mask of pixels that likely contain a volume fraction of GM.
  1051. This mask is obtained by dilating a GM mask extracted from the FreeSurfer's *aseg* segmentation, and it ensures components are not extracted
  1052. from voxels containing a minimal fraction of GM.
  1053. Finally, these masks are resampled into BOLD space and binarized by
  1054. thresholding at 0.99 (as in the original implementation).
  1055. Components are also calculated separately within the WM and CSF masks.
  1056. For each CompCor decomposition, the *k* components with the largest singular
  1057. values are retained, such that the retained components' time series are
  1058. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
  1059. WM, combined, or temporal). The remaining components are dropped from
  1060. consideration.
  1061. The head-motion estimates calculated in the correction step were also
  1062. placed within the corresponding confounds file.
  1063. The confound time series derived from head motion estimates and global
  1064. signals were expanded with the inclusion of temporal derivatives and
  1065. quadratic terms for each [@confounds_satterthwaite_2013].
  1066. Frames that exceeded a threshold of 0.5 mm FD or
  1067. 1.5 standardised DVARS were annotated as motion outliers.
  1068. All resamplings can be performed with *a single interpolation
  1069. step* by composing all the pertinent transformations (i.e. head-motion
  1070. transform matrices, susceptibility distortion correction when available,
  1071. and co-registrations to anatomical and output spaces).
  1072. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
  1073. configured with Lanczos interpolation to minimize the smoothing
  1074. effects of other kernels [@lanczos].
  1075. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
  1076. (FreeSurfer).
  1077. Many internal operations of *fMRIPrep* use
  1078. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
  1079. mostly within the functional processing workflow.
  1080. For more details of the pipeline, see [the section corresponding
  1081. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
  1082. ### Copyright Waiver
  1083. The above boilerplate text was automatically generated by fMRIPrep
  1084. with the express intention that users should copy and paste this
  1085. text into their manuscripts *unchanged*.
  1086. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
  1087. ### References
  1088. </pre>
  1089. </div>
  1090. <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
  1091. using \emph{fMRIPrep} 20.1.1+79.g1a72777b (\citet{fmriprep1};
  1092. \citet{fmriprep2}; RRID:SCR\_016216), which is based on \emph{Nipype}
  1093. 1.5.0 (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
  1094. \begin{description}
  1095. \item[Anatomical data preprocessing]
  1096. A total of 2 T1-weighted (T1w) images were found within the input BIDS
  1097. dataset. All of them were corrected for intensity non-uniformity (INU)
  1098. with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with ANTs
  1099. 2.2.0 \citep[RRID:SCR\_004757]{ants}. The T1w-reference was then
  1100. skull-stripped with a \emph{Nipype} implementation of the
  1101. \texttt{antsBrainExtraction.sh} workflow (from ANTs), using OASIS30ANTs
  1102. as target template. Brain tissue segmentation of cerebrospinal fluid
  1103. (CSF), white-matter (WM) and gray-matter (GM) was performed on the
  1104. brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
  1105. RRID:SCR\_002823,][]{fsl_fast}. A T1w-reference map was computed after
  1106. registration of 2 T1w images (after INU-correction) using
  1107. \texttt{mri\_robust\_template} \citep[FreeSurfer 6.0.1,][]{fs_template}.
  1108. Brain surfaces were reconstructed using \texttt{recon-all}
  1109. \citep[FreeSurfer 6.0.1, RRID:SCR\_001847,][]{fs_reconall}, and the
  1110. brain mask estimated previously was refined with a custom variation of
  1111. the method to reconcile ANTs-derived and FreeSurfer-derived
  1112. segmentations of the cortical gray-matter of Mindboggle
  1113. \citep[RRID:SCR\_002438,][]{mindboggle}. Volume-based spatial
  1114. normalization to two standard spaces (MNI152NLin2009cAsym,
  1115. MNI152NLin6Asym) was performed through nonlinear registration with
  1116. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
  1117. of both T1w reference and the T1w template. The following templates were
  1118. selected for spatial normalization: \emph{ICBM 152 Nonlinear
  1119. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
  1120. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]}, \emph{FSL's
  1121. MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain
  1122. Stereotaxic Registration Model} {[}\citet{mni152nlin6asym},
  1123. RRID:SCR\_002823; TemplateFlow ID: MNI152NLin6Asym{]},
  1124. \item[Functional data preprocessing]
  1125. For each of the 10 BOLD runs found per subject (across all tasks and
  1126. sessions), the following preprocessing was performed. First, a reference
  1127. volume and its skull-stripped version were generated using a custom
  1128. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
  1129. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
  1130. reference using \texttt{bbregister} (FreeSurfer) which implements
  1131. boundary-based registration \citep{bbr}. Co-registration was configured
  1132. with six degrees of freedom. Head-motion parameters with respect to the
  1133. BOLD reference (transformation matrices, and six corresponding rotation
  1134. and translation parameters) are estimated before any spatiotemporal
  1135. filtering using \texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. BOLD
  1136. runs were slice-time corrected using \texttt{3dTshift} from AFNI
  1137. 20160207 \citep[RRID:SCR\_005927]{afni}. The BOLD time-series were
  1138. resampled onto the following surfaces (FreeSurfer reconstruction
  1139. nomenclature): \emph{fsaverage}. The BOLD time-series (including
  1140. slice-timing correction when applied) were resampled onto their
  1141. original, native space by applying the transforms to correct for
  1142. head-motion. These resampled BOLD time-series will be referred to as
  1143. \emph{preprocessed BOLD in original space}, or just \emph{preprocessed
  1144. BOLD}. \emph{Grayordinates} files \citep{hcppipelines} containing 91k
  1145. samples were also generated using the highest-resolution
  1146. \texttt{fsaverage} as intermediate standardized surface space. Automatic
  1147. removal of motion artifacts using independent component analysis
  1148. \citep[ICA-AROMA,][]{aroma} was performed on the \emph{preprocessed BOLD
  1149. on MNI space} time-series after removal of non-steady state volumes and
  1150. spatial smoothing with an isotropic, Gaussian kernel of 6mm FWHM
  1151. (full-width half-maximum). Corresponding ``non-aggresively'' denoised
  1152. runs were produced after such smoothing. Additionally, the
  1153. ``aggressive'' noise-regressors were collected and placed in the
  1154. corresponding confounds file. Several confounding time-series were
  1155. calculated based on the \emph{preprocessed BOLD}: framewise displacement
  1156. (FD), DVARS and three region-wise global signals. FD was computed using
  1157. two formulations following Power (absolute sum of relative motions,
  1158. \citet{power_fd_dvars}) and Jenkinson (relative root mean square
  1159. displacement between affines, \citet{mcflirt}). FD and DVARS are
  1160. calculated for each functional run, both using their implementations in
  1161. \emph{Nipype} \citep[following the definitions by][]{power_fd_dvars}.
  1162. The three global signals are extracted within the CSF, the WM, and the
  1163. whole-brain masks. Additionally, a set of physiological regressors were
  1164. extracted to allow for component-based noise correction
  1165. \citep[\emph{CompCor},][]{compcor}. Principal components are estimated
  1166. after high-pass filtering the \emph{preprocessed BOLD} time-series
  1167. (using a discrete cosine filter with 128s cut-off) for the two
  1168. \emph{CompCor} variants: temporal (tCompCor) and anatomical (aCompCor).
  1169. tCompCor components are then calculated from the top 2\% variable voxels
  1170. within the brain mask. For aCompCor, three probabilistic masks (CSF, WM
  1171. and combined CSF+WM) are generated in anatomical space. The
  1172. implementation differs from that of Behzadi et al.~in that instead of
  1173. eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
  1174. subtracted a mask of pixels that likely contain a volume fraction of GM.
  1175. This mask is obtained by dilating a GM mask extracted from the
  1176. FreeSurfer's \emph{aseg} segmentation, and it ensures components are not
  1177. extracted from voxels containing a minimal fraction of GM. Finally,
  1178. these masks are resampled into BOLD space and binarized by thresholding
  1179. at 0.99 (as in the original implementation). Components are also
  1180. calculated separately within the WM and CSF masks. For each CompCor
  1181. decomposition, the \emph{k} components with the largest singular values
  1182. are retained, such that the retained components' time series are
  1183. sufficient to explain 50 percent of variance across the nuisance mask
  1184. (CSF, WM, combined, or temporal). The remaining components are dropped
  1185. from consideration. The head-motion estimates calculated in the
  1186. correction step were also placed within the corresponding confounds
  1187. file. The confound time series derived from head motion estimates and
  1188. global signals were expanded with the inclusion of temporal derivatives
  1189. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
  1190. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
  1191. were annotated as motion outliers. All resamplings can be performed with
  1192. \emph{a single interpolation step} by composing all the pertinent
  1193. transformations (i.e.~head-motion transform matrices, susceptibility
  1194. distortion correction when available, and co-registrations to anatomical
  1195. and output spaces). Gridded (volumetric) resamplings were performed
  1196. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
  1197. interpolation to minimize the smoothing effects of other kernels
  1198. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
  1199. \texttt{mri\_vol2surf} (FreeSurfer).
  1200. \end{description}
  1201. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
  1202. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
  1203. processing workflow. For more details of the pipeline, see
  1204. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
  1205. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
  1206. \hypertarget{copyright-waiver}{%
  1207. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
  1208. The above boilerplate text was automatically generated by fMRIPrep with
  1209. the express intention that users should copy and paste this text into
  1210. their manuscripts \emph{unchanged}. It is released under the
  1211. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
  1212. \hypertarget{references}{%
  1213. \subsubsection{References}\label{references}}
  1214. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
  1215. <h3>Bibliography</h3>
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  1525. </pre>
  1526. </div>
  1527. </div>
  1528. </div>
  1529. <div id="errors">
  1530. <h1 class="sub-report-title">Errors</h1>
  1531. <p>No errors to report!</p>
  1532. </div>
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