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    58. 

  58. 

  59. 

  60. <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
    61. 

  61. <div class="collapse navbar-collapse">
    62. 

  62.     <ul class="navbar-nav">
    63. 

  63.         <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
    64. 

  64.         <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
    65. 

  65.         <li class="nav-item dropdown">
    66. 

  66.             <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
    67. 

  67.             <div class="dropdown-menu" aria-labelledby="navbarFunctional">
    68. 

  68.                     <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>
    69. 

  69.                     <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>
    70. 

  70.                     <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>
    71. 

  71.                     <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>
    72. 

  72.                     <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>
    73. 

  73.                     <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>
    74. 

  74.                     <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>
    75. 

  75.                     <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>
    76. 

  76.                     <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>
    77. 

  77.                     <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>
    78. 

  78.             </div>
    79. 

  79.         </li>
    80. 

  80.         <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
    81. 

  81.         <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
    82. 

  82.         <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
    83. 

  83.     </ul>
    84. 

  84. </div>
    85. 

  85. </nav>
    86. 

  86. <noscript>
    87. 

  87.     <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
    88. 

  88. </noscript>
    89. 

  89. 

  90.     <div id="Summary">
    91. 

  91.     <h1 class="sub-report-title">Summary</h1>
    92. 

  92.         <div id="datatype-figures_desc-summary_suffix-T1w">
    93. 

  93.                     <ul class="elem-desc">
    94. 

  94. 		<li>Subject ID: 09</li>
    95. 

  95. 		<li>Structural images: 2 T1-weighted </li>
    96. 

  96. 		<li>Functional series: 10</li>
    97. 

  97. 		<ul class="elem-desc">
    98. 

  98. 			<li>Task: covertverbgeneration (2 runs)</li>
    99. 

  99. 			<li>Task: fingerfootlips (2 runs)</li>
    100. 

  100. 			<li>Task: linebisection (2 runs)</li>
    101. 

  101. 			<li>Task: overtverbgeneration (2 runs)</li>
    102. 

  102. 			<li>Task: overtwordrepetition (2 runs)</li>
    103. 

  103. 		</ul>
    104. 

  104. 		<li>Standard output spaces: MNI152NLin2009cAsym, MNI152NLin6Asym, fsaverage</li>
    105. 

  105. 		<li>Non-standard output spaces: anat</li>
    106. 

  106. 		<li>FreeSurfer reconstruction: Run by fMRIPrep</li>
    107. 

  107. 	</ul>
    108. 

  108.         </div>
    109. 

  109.     </div>
    110. 

  110.     <div id="Anatomical">
    111. 

  111.     <h1 class="sub-report-title">Anatomical</h1>
    112. 

  112.         <div id="datatype-figures_desc-conform_extension-['.html']_suffix-T1w">
    113. 

  113.                     <h3 class="elem-title">Anatomical Conformation</h3>
    114. 

  114. 		<ul class="elem-desc">
    115. 

  115. 			<li>Input T1w images: 2</li>
    116. 

  116. 			<li>Output orientation: RAS</li>
    117. 

  117. 			<li>Output dimensions: 256x156x256</li>
    118. 

  118. 			<li>Output voxel size: 1mm x 1.3mm x 1mm</li>
    119. 

  119. 			<li>Discarded images: 0</li>
    120. 

  120. 

  121. 		</ul>
    122. 

  122.         </div>
    123. 

  123.         <div id="datatype-figures_suffix-dseg">
    124. 

  124. <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-09/figures/sub-09_dseg.svg" style="width: 100%" />
    125. 

  125. </div>
    126. 

  126. <div class="elem-filename">
    127. 

  127.     Get figure file: <a href="./sub-09/figures/sub-09_dseg.svg" target="_blank">sub-09/figures/sub-09_dseg.svg</a>
    128. 

  128. </div>
    129. 

  129. 

  130.         </div>
    131. 

  131.         <div id="datatype-figures_regex_search-True_space-.*_suffix-T1w">
    132. 

  132. <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-09/figures/sub-09_space-MNI152NLin6Asym_T1w.svg">
    133. 

  133. Problem loading figure sub-09/figures/sub-09_space-MNI152NLin6Asym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
    134. 

  134. </div>
    135. 

  135. <div class="elem-filename">
    136. 

  136.     Get figure file: <a href="./sub-09/figures/sub-09_space-MNI152NLin6Asym_T1w.svg" target="_blank">sub-09/figures/sub-09_space-MNI152NLin6Asym_T1w.svg</a>
    137. 

  137. </div>
    138. 

  138. 

  139. <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-09/figures/sub-09_space-MNI152NLin2009cAsym_T1w.svg">
    140. 

  140. Problem loading figure sub-09/figures/sub-09_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
    141. 

  141. </div>
    142. 

  142. <div class="elem-filename">
    143. 

  143.     Get figure file: <a href="./sub-09/figures/sub-09_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-09/figures/sub-09_space-MNI152NLin2009cAsym_T1w.svg</a>
    144. 

  144. </div>
    145. 

  145. 

  146.         </div>
    147. 

  147.         <div id="datatype-figures_desc-reconall_suffix-T1w">
    148. 

  148. <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-09/figures/sub-09_desc-reconall_T1w.svg" style="width: 100%" />
    149. 

  149. </div>
    150. 

  150. <div class="elem-filename">
    151. 

  151.     Get figure file: <a href="./sub-09/figures/sub-09_desc-reconall_T1w.svg" target="_blank">sub-09/figures/sub-09_desc-reconall_T1w.svg</a>
    152. 

  152. </div>
    153. 

  153. 

  154.         </div>
    155. 

  155.     </div>
    156. 

  156.     <div id="Functional">
    157. 

  157.     <h1 class="sub-report-title">Functional</h1>
    158. 

  158.         <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-covertverbgeneration">
    159. 

  159. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">covertverbgeneration</span>.</h2>                    <details open>
    160. 

  160. 		<summary>Summary</summary>
    161. 

  161. 		<ul class="elem-desc">
    162. 

  162. 			<li>Repetition time (TR): 2.5s</li>
    163. 

  163. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    164. 

  164. 			<li>Single-echo EPI sequence.</li>
    165. 

  165. 			<li>Slice timing correction: Applied</li>
    166. 

  166. 			<li>Susceptibility distortion correction: None</li>
    167. 

  167. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    168. 

  168. 			<li>Non-steady-state volumes: 3</li>
    169. 

  169. 		</ul>
    170. 

  170. 		</details>
    171. 

  171. 		<details>
    172. 

  172. 			<summary>Confounds collected</summary><br />
    173. 

  173. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, non_steady_state_outlier01, non_steady_state_outlier02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, aroma_motion_01, aroma_motion_02, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_07, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_13, aroma_motion_15, aroma_motion_17, aroma_motion_20, aroma_motion_21, aroma_motion_24, aroma_motion_25.</p>
    174. 

  174. 		</details>
    175. 

  175.         </div>
    176. 

  176.         <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-covertverbgeneration">
    177. 

  177. <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-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg">
    178. 

  178. Problem loading figure sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    179. 

  179. </div>
    180. 

  180. <div class="elem-filename">
    181. 

  181.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg</a>
    182. 

  182. </div>
    183. 

  183. 

  184.         </div>
    185. 

  185.         <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-covertverbgeneration">
    186. 

  186. <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-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-rois_bold.svg" style="width: 100%" />
    187. 

  187. </div>
    188. 

  188. <div class="elem-filename">
    189. 

  189.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-rois_bold.svg</a>
    190. 

  190. </div>
    191. 

  191. 

  192.         </div>
    193. 

  193.         <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-covertverbgeneration">
    194. 

  194. <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-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
    195. 

  195. </div>
    196. 

  196. <div class="elem-filename">
    197. 

  197.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-compcorvar_bold.svg</a>
    198. 

  198. </div>
    199. 

  199. 

  200.         </div>
    201. 

  201.         <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-covertverbgeneration">
    202. 

  202. <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-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
    203. 

  203. </div>
    204. 

  204. <div class="elem-filename">
    205. 

  205.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-carpetplot_bold.svg</a>
    206. 

  206. </div>
    207. 

  207. 

  208.         </div>
    209. 

  209.         <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-covertverbgeneration">
    210. 

  210. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    211. 

  211. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    212. 

  212. Right: magnitude of the correlation between each confound time series and the
    213. 

  213. mean global signal. Strong correlations might be indicative of partial volume
    214. 

  214. effects and can inform decisions about feature orthogonalization prior to
    215. 

  215. confound regression.
    216. 

  216. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
    217. 

  217. </div>
    218. 

  218. <div class="elem-filename">
    219. 

  219.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-confoundcorr_bold.svg</a>
    220. 

  220. </div>
    221. 

  221. 

  222.         </div>
    223. 

  223.         <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-covertverbgeneration">
    224. 

  224. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    225. 

  225. black brain outline. Right hand side of each map: time series (top in seconds),
    226. 

  226. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    227. 

  227. in green; noise components in red.
    228. 

  228. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
    229. 

  229. </div>
    230. 

  230. <div class="elem-filename">
    231. 

  231.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-covertverbgeneration_desc-aroma_bold.svg</a>
    232. 

  232. </div>
    233. 

  233. 

  234.         </div>
    235. 

  235.         <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-fingerfootlips">
    236. 

  236. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">fingerfootlips</span>.</h2>                    <details open>
    237. 

  237. 		<summary>Summary</summary>
    238. 

  238. 		<ul class="elem-desc">
    239. 

  239. 			<li>Repetition time (TR): 2.5s</li>
    240. 

  240. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    241. 

  241. 			<li>Single-echo EPI sequence.</li>
    242. 

  242. 			<li>Slice timing correction: Applied</li>
    243. 

  243. 			<li>Susceptibility distortion correction: None</li>
    244. 

  244. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    245. 

  245. 			<li>Non-steady-state volumes: 2</li>
    246. 

  246. 		</ul>
    247. 

  247. 		</details>
    248. 

  248. 		<details>
    249. 

  249. 			<summary>Confounds collected</summary><br />
    250. 

  250. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, non_steady_state_outlier00, non_steady_state_outlier01, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, 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_14, aroma_motion_15, aroma_motion_24, aroma_motion_25, aroma_motion_27, aroma_motion_31, aroma_motion_32, aroma_motion_35, aroma_motion_36, aroma_motion_37, aroma_motion_38, aroma_motion_40, aroma_motion_41.</p>
    251. 

  251. 		</details>
    252. 

  252.         </div>
    253. 

  253.         <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-fingerfootlips">
    254. 

  254. <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-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg">
    255. 

  255. Problem loading figure sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    256. 

  256. </div>
    257. 

  257. <div class="elem-filename">
    258. 

  258.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg</a>
    259. 

  259. </div>
    260. 

  260. 

  261.         </div>
    262. 

  262.         <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-fingerfootlips">
    263. 

  263. <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-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-rois_bold.svg" style="width: 100%" />
    264. 

  264. </div>
    265. 

  265. <div class="elem-filename">
    266. 

  266.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-rois_bold.svg</a>
    267. 

  267. </div>
    268. 

  268. 

  269.         </div>
    270. 

  270.         <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-fingerfootlips">
    271. 

  271. <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-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-compcorvar_bold.svg" style="width: 100%" />
    272. 

  272. </div>
    273. 

  273. <div class="elem-filename">
    274. 

  274.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-compcorvar_bold.svg</a>
    275. 

  275. </div>
    276. 

  276. 

  277.         </div>
    278. 

  278.         <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-fingerfootlips">
    279. 

  279. <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-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-carpetplot_bold.svg" style="width: 100%" />
    280. 

  280. </div>
    281. 

  281. <div class="elem-filename">
    282. 

  282.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-carpetplot_bold.svg</a>
    283. 

  283. </div>
    284. 

  284. 

  285.         </div>
    286. 

  286.         <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-fingerfootlips">
    287. 

  287. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    288. 

  288. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    289. 

  289. Right: magnitude of the correlation between each confound time series and the
    290. 

  290. mean global signal. Strong correlations might be indicative of partial volume
    291. 

  291. effects and can inform decisions about feature orthogonalization prior to
    292. 

  292. confound regression.
    293. 

  293. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-confoundcorr_bold.svg" style="width: 100%" />
    294. 

  294. </div>
    295. 

  295. <div class="elem-filename">
    296. 

  296.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-confoundcorr_bold.svg</a>
    297. 

  297. </div>
    298. 

  298. 

  299.         </div>
    300. 

  300.         <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-fingerfootlips">
    301. 

  301. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    302. 

  302. black brain outline. Right hand side of each map: time series (top in seconds),
    303. 

  303. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    304. 

  304. in green; noise components in red.
    305. 

  305. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-aroma_bold.svg" style="width: 100%" />
    306. 

  306. </div>
    307. 

  307. <div class="elem-filename">
    308. 

  308.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-fingerfootlips_desc-aroma_bold.svg</a>
    309. 

  309. </div>
    310. 

  310. 

  311.         </div>
    312. 

  312.         <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-linebisection">
    313. 

  313. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">linebisection</span>.</h2>                    <details open>
    314. 

  314. 		<summary>Summary</summary>
    315. 

  315. 		<ul class="elem-desc">
    316. 

  316. 			<li>Repetition time (TR): 2.5s</li>
    317. 

  317. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    318. 

  318. 			<li>Single-echo EPI sequence.</li>
    319. 

  319. 			<li>Slice timing correction: Applied</li>
    320. 

  320. 			<li>Susceptibility distortion correction: None</li>
    321. 

  321. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    322. 

  322. 			<li>Non-steady-state volumes: 3</li>
    323. 

  323. 		</ul>
    324. 

  324. 		</details>
    325. 

  325. 		<details>
    326. 

  326. 			<summary>Confounds collected</summary><br />
    327. 

  327. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, non_steady_state_outlier00, non_steady_state_outlier01, non_steady_state_outlier02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_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_08, aroma_motion_09, aroma_motion_10, aroma_motion_13, aroma_motion_14, aroma_motion_16, aroma_motion_18, aroma_motion_20, aroma_motion_23, aroma_motion_24, aroma_motion_25, aroma_motion_26, aroma_motion_27, aroma_motion_29, aroma_motion_31.</p>
    328. 

  328. 		</details>
    329. 

  329.         </div>
    330. 

  330.         <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-linebisection">
    331. 

  331. <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-09/figures/sub-09_ses-retest_task-linebisection_desc-bbregister_bold.svg">
    332. 

  332. Problem loading figure sub-09/figures/sub-09_ses-retest_task-linebisection_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    333. 

  333. </div>
    334. 

  334. <div class="elem-filename">
    335. 

  335.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-linebisection_desc-bbregister_bold.svg</a>
    336. 

  336. </div>
    337. 

  337. 

  338.         </div>
    339. 

  339.         <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-linebisection">
    340. 

  340. <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-09/figures/sub-09_ses-retest_task-linebisection_desc-rois_bold.svg" style="width: 100%" />
    341. 

  341. </div>
    342. 

  342. <div class="elem-filename">
    343. 

  343.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-linebisection_desc-rois_bold.svg</a>
    344. 

  344. </div>
    345. 

  345. 

  346.         </div>
    347. 

  347.         <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-linebisection">
    348. 

  348. <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-09/figures/sub-09_ses-retest_task-linebisection_desc-compcorvar_bold.svg" style="width: 100%" />
    349. 

  349. </div>
    350. 

  350. <div class="elem-filename">
    351. 

  351.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-linebisection_desc-compcorvar_bold.svg</a>
    352. 

  352. </div>
    353. 

  353. 

  354.         </div>
    355. 

  355.         <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-linebisection">
    356. 

  356. <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-09/figures/sub-09_ses-retest_task-linebisection_desc-carpetplot_bold.svg" style="width: 100%" />
    357. 

  357. </div>
    358. 

  358. <div class="elem-filename">
    359. 

  359.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-linebisection_desc-carpetplot_bold.svg</a>
    360. 

  360. </div>
    361. 

  361. 

  362.         </div>
    363. 

  363.         <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-linebisection">
    364. 

  364. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    365. 

  365. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    366. 

  366. Right: magnitude of the correlation between each confound time series and the
    367. 

  367. mean global signal. Strong correlations might be indicative of partial volume
    368. 

  368. effects and can inform decisions about feature orthogonalization prior to
    369. 

  369. confound regression.
    370. 

  370. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-confoundcorr_bold.svg" style="width: 100%" />
    371. 

  371. </div>
    372. 

  372. <div class="elem-filename">
    373. 

  373.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-linebisection_desc-confoundcorr_bold.svg</a>
    374. 

  374. </div>
    375. 

  375. 

  376.         </div>
    377. 

  377.         <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-linebisection">
    378. 

  378. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    379. 

  379. black brain outline. Right hand side of each map: time series (top in seconds),
    380. 

  380. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    381. 

  381. in green; noise components in red.
    382. 

  382. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-aroma_bold.svg" style="width: 100%" />
    383. 

  383. </div>
    384. 

  384. <div class="elem-filename">
    385. 

  385.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-linebisection_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-linebisection_desc-aroma_bold.svg</a>
    386. 

  386. </div>
    387. 

  387. 

  388.         </div>
    389. 

  389.         <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-overtverbgeneration">
    390. 

  390. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">overtverbgeneration</span>.</h2>                    <details open>
    391. 

  391. 		<summary>Summary</summary>
    392. 

  392. 		<ul class="elem-desc">
    393. 

  393. 			<li>Repetition time (TR): 5s</li>
    394. 

  394. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    395. 

  395. 			<li>Single-echo EPI sequence.</li>
    396. 

  396. 			<li>Slice timing correction: Applied</li>
    397. 

  397. 			<li>Susceptibility distortion correction: None</li>
    398. 

  398. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    399. 

  399. 			<li>Non-steady-state volumes: 1</li>
    400. 

  400. 		</ul>
    401. 

  401. 		</details>
    402. 

  402. 		<details>
    403. 

  403. 			<summary>Confounds collected</summary><br />
    404. 

  404. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, aroma_motion_04, aroma_motion_06, aroma_motion_08, aroma_motion_12, 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_23, aroma_motion_25, aroma_motion_26.</p>
    405. 

  405. 		</details>
    406. 

  406.         </div>
    407. 

  407.         <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-overtverbgeneration">
    408. 

  408. <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-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg">
    409. 

  409. Problem loading figure sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    410. 

  410. </div>
    411. 

  411. <div class="elem-filename">
    412. 

  412.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg</a>
    413. 

  413. </div>
    414. 

  414. 

  415.         </div>
    416. 

  416.         <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-overtverbgeneration">
    417. 

  417. <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-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-rois_bold.svg" style="width: 100%" />
    418. 

  418. </div>
    419. 

  419. <div class="elem-filename">
    420. 

  420.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-rois_bold.svg</a>
    421. 

  421. </div>
    422. 

  422. 

  423.         </div>
    424. 

  424.         <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-overtverbgeneration">
    425. 

  425. <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-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
    426. 

  426. </div>
    427. 

  427. <div class="elem-filename">
    428. 

  428.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-compcorvar_bold.svg</a>
    429. 

  429. </div>
    430. 

  430. 

  431.         </div>
    432. 

  432.         <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-overtverbgeneration">
    433. 

  433. <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-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
    434. 

  434. </div>
    435. 

  435. <div class="elem-filename">
    436. 

  436.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-carpetplot_bold.svg</a>
    437. 

  437. </div>
    438. 

  438. 

  439.         </div>
    440. 

  440.         <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-overtverbgeneration">
    441. 

  441. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    442. 

  442. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    443. 

  443. Right: magnitude of the correlation between each confound time series and the
    444. 

  444. mean global signal. Strong correlations might be indicative of partial volume
    445. 

  445. effects and can inform decisions about feature orthogonalization prior to
    446. 

  446. confound regression.
    447. 

  447. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
    448. 

  448. </div>
    449. 

  449. <div class="elem-filename">
    450. 

  450.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-confoundcorr_bold.svg</a>
    451. 

  451. </div>
    452. 

  452. 

  453.         </div>
    454. 

  454.         <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-overtverbgeneration">
    455. 

  455. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    456. 

  456. black brain outline. Right hand side of each map: time series (top in seconds),
    457. 

  457. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    458. 

  458. in green; noise components in red.
    459. 

  459. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
    460. 

  460. </div>
    461. 

  461. <div class="elem-filename">
    462. 

  462.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtverbgeneration_desc-aroma_bold.svg</a>
    463. 

  463. </div>
    464. 

  464. 

  465.         </div>
    466. 

  466.         <div id="datatype-figures_desc-summary_session-retest_suffix-bold_task-overtwordrepetition">
    467. 

  467. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">retest</span>, task <span class="bids-entity">overtwordrepetition</span>.</h2>                    <details open>
    468. 

  468. 		<summary>Summary</summary>
    469. 

  469. 		<ul class="elem-desc">
    470. 

  470. 			<li>Repetition time (TR): 5s</li>
    471. 

  471. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    472. 

  472. 			<li>Single-echo EPI sequence.</li>
    473. 

  473. 			<li>Slice timing correction: Applied</li>
    474. 

  474. 			<li>Susceptibility distortion correction: None</li>
    475. 

  475. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    476. 

  476. 			<li>Non-steady-state volumes: 1</li>
    477. 

  477. 		</ul>
    478. 

  478. 		</details>
    479. 

  479. 		<details>
    480. 

  480. 			<summary>Confounds collected</summary><br />
    481. 

  481. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, cosine00, cosine01, cosine02, cosine03, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, aroma_motion_04, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_12, aroma_motion_14, aroma_motion_17, aroma_motion_20.</p>
    482. 

  482. 		</details>
    483. 

  483.         </div>
    484. 

  484.         <div id="datatype-figures_desc-validation_session-retest_suffix-bold_task-overtwordrepetition">
    485. 

  485.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    486. 

  486. 			    <p class="elem-desc">
    487. 

  487. 			    The qform has been copied from sform.
    488. 

  488. 			    The difference in angle is 1.3e-07.
    489. 

  489. 			    The difference in translation is 2.7e-15.
    490. 

  490. 			    </p>
    491. 

  491.         </div>
    492. 

  492.         <div id="datatype-figures_desc-bbregister_session-retest_suffix-bold_task-overtwordrepetition">
    493. 

  493. <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-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg">
    494. 

  494. Problem loading figure sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    495. 

  495. </div>
    496. 

  496. <div class="elem-filename">
    497. 

  497.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg</a>
    498. 

  498. </div>
    499. 

  499. 

  500.         </div>
    501. 

  501.         <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-overtwordrepetition">
    502. 

  502. <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-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-rois_bold.svg" style="width: 100%" />
    503. 

  503. </div>
    504. 

  504. <div class="elem-filename">
    505. 

  505.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-rois_bold.svg</a>
    506. 

  506. </div>
    507. 

  507. 

  508.         </div>
    509. 

  509.         <div id="datatype-figures_desc-compcorvar_session-retest_suffix-bold_task-overtwordrepetition">
    510. 

  510. <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-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg" style="width: 100%" />
    511. 

  511. </div>
    512. 

  512. <div class="elem-filename">
    513. 

  513.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg</a>
    514. 

  514. </div>
    515. 

  515. 

  516.         </div>
    517. 

  517.         <div id="datatype-figures_desc-carpetplot_session-retest_suffix-bold_task-overtwordrepetition">
    518. 

  518. <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-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg" style="width: 100%" />
    519. 

  519. </div>
    520. 

  520. <div class="elem-filename">
    521. 

  521.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg</a>
    522. 

  522. </div>
    523. 

  523. 

  524.         </div>
    525. 

  525.         <div id="datatype-figures_desc-confoundcorr_session-retest_suffix-bold_task-overtwordrepetition">
    526. 

  526. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    527. 

  527. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    528. 

  528. Right: magnitude of the correlation between each confound time series and the
    529. 

  529. mean global signal. Strong correlations might be indicative of partial volume
    530. 

  530. effects and can inform decisions about feature orthogonalization prior to
    531. 

  531. confound regression.
    532. 

  532. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg" style="width: 100%" />
    533. 

  533. </div>
    534. 

  534. <div class="elem-filename">
    535. 

  535.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg</a>
    536. 

  536. </div>
    537. 

  537. 

  538.         </div>
    539. 

  539.         <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-overtwordrepetition">
    540. 

  540. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    541. 

  541. black brain outline. Right hand side of each map: time series (top in seconds),
    542. 

  542. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    543. 

  543. in green; noise components in red.
    544. 

  544. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg" style="width: 100%" />
    545. 

  545. </div>
    546. 

  546. <div class="elem-filename">
    547. 

  547.     Get figure file: <a href="./sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg</a>
    548. 

  548. </div>
    549. 

  549. 

  550.         </div>
    551. 

  551.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-covertverbgeneration">
    552. 

  552. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">covertverbgeneration</span>.</h2>                    <details open>
    553. 

  553. 		<summary>Summary</summary>
    554. 

  554. 		<ul class="elem-desc">
    555. 

  555. 			<li>Repetition time (TR): 2.5s</li>
    556. 

  556. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    557. 

  557. 			<li>Single-echo EPI sequence.</li>
    558. 

  558. 			<li>Slice timing correction: Applied</li>
    559. 

  559. 			<li>Susceptibility distortion correction: None</li>
    560. 

  560. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    561. 

  561. 			<li>Non-steady-state volumes: 1</li>
    562. 

  562. 		</ul>
    563. 

  563. 		</details>
    564. 

  564. 		<details>
    565. 

  565. 			<summary>Confounds collected</summary><br />
    566. 

  566. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_12, aroma_motion_14, aroma_motion_15, aroma_motion_16, aroma_motion_21, aroma_motion_22, aroma_motion_23, aroma_motion_25, aroma_motion_26, aroma_motion_28, aroma_motion_30.</p>
    567. 

  567. 		</details>
    568. 

  568.         </div>
    569. 

  569.         <div id="datatype-figures_desc-validation_session-test_suffix-bold_task-covertverbgeneration">
    570. 

  570.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    571. 

  571. 			    <p class="elem-desc">
    572. 

  572. 			    The qform has been copied from sform.
    573. 

  573. 			    The difference in angle is -0.00053.
    574. 

  574. 			    The difference in translation is 4.7e-15.
    575. 

  575. 			    </p>
    576. 

  576.         </div>
    577. 

  577.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-covertverbgeneration">
    578. 

  578. <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-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg">
    579. 

  579. Problem loading figure sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    580. 

  580. </div>
    581. 

  581. <div class="elem-filename">
    582. 

  582.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg</a>
    583. 

  583. </div>
    584. 

  584. 

  585.         </div>
    586. 

  586.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-covertverbgeneration">
    587. 

  587. <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-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-rois_bold.svg" style="width: 100%" />
    588. 

  588. </div>
    589. 

  589. <div class="elem-filename">
    590. 

  590.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-rois_bold.svg</a>
    591. 

  591. </div>
    592. 

  592. 

  593.         </div>
    594. 

  594.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-covertverbgeneration">
    595. 

  595. <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-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
    596. 

  596. </div>
    597. 

  597. <div class="elem-filename">
    598. 

  598.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg</a>
    599. 

  599. </div>
    600. 

  600. 

  601.         </div>
    602. 

  602.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-covertverbgeneration">
    603. 

  603. <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-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
    604. 

  604. </div>
    605. 

  605. <div class="elem-filename">
    606. 

  606.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg</a>
    607. 

  607. </div>
    608. 

  608. 

  609.         </div>
    610. 

  610.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-covertverbgeneration">
    611. 

  611. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    612. 

  612. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    613. 

  613. Right: magnitude of the correlation between each confound time series and the
    614. 

  614. mean global signal. Strong correlations might be indicative of partial volume
    615. 

  615. effects and can inform decisions about feature orthogonalization prior to
    616. 

  616. confound regression.
    617. 

  617. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
    618. 

  618. </div>
    619. 

  619. <div class="elem-filename">
    620. 

  620.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg</a>
    621. 

  621. </div>
    622. 

  622. 

  623.         </div>
    624. 

  624.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-covertverbgeneration">
    625. 

  625. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    626. 

  626. black brain outline. Right hand side of each map: time series (top in seconds),
    627. 

  627. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    628. 

  628. in green; noise components in red.
    629. 

  629. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
    630. 

  630. </div>
    631. 

  631. <div class="elem-filename">
    632. 

  632.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-covertverbgeneration_desc-aroma_bold.svg</a>
    633. 

  633. </div>
    634. 

  634. 

  635.         </div>
    636. 

  636.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-fingerfootlips">
    637. 

  637. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">fingerfootlips</span>.</h2>                    <details open>
    638. 

  638. 		<summary>Summary</summary>
    639. 

  639. 		<ul class="elem-desc">
    640. 

  640. 			<li>Repetition time (TR): 2.5s</li>
    641. 

  641. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    642. 

  642. 			<li>Single-echo EPI sequence.</li>
    643. 

  643. 			<li>Slice timing correction: Applied</li>
    644. 

  644. 			<li>Susceptibility distortion correction: None</li>
    645. 

  645. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    646. 

  646. 			<li>Non-steady-state volumes: 1</li>
    647. 

  647. 		</ul>
    648. 

  648. 		</details>
    649. 

  649. 		<details>
    650. 

  650. 			<summary>Confounds collected</summary><br />
    651. 

  651. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_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, 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_10, aroma_motion_11, aroma_motion_12, aroma_motion_14, aroma_motion_15, aroma_motion_16, aroma_motion_18, aroma_motion_20, aroma_motion_21, aroma_motion_23, aroma_motion_25, aroma_motion_26, aroma_motion_28, aroma_motion_30, aroma_motion_32, aroma_motion_36, aroma_motion_37, aroma_motion_38, aroma_motion_41, aroma_motion_42, aroma_motion_43.</p>
    652. 

  652. 		</details>
    653. 

  653.         </div>
    654. 

  654.         <div id="datatype-figures_desc-validation_session-test_suffix-bold_task-fingerfootlips">
    655. 

  655.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    656. 

  656. 			    <p class="elem-desc">
    657. 

  657. 			    The qform has been copied from sform.
    658. 

  658. 			    The difference in angle is -0.00053.
    659. 

  659. 			    The difference in translation is 4.7e-15.
    660. 

  660. 			    </p>
    661. 

  661.         </div>
    662. 

  662.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-fingerfootlips">
    663. 

  663. <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-09/figures/sub-09_ses-test_task-fingerfootlips_desc-bbregister_bold.svg">
    664. 

  664. Problem loading figure sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    665. 

  665. </div>
    666. 

  666. <div class="elem-filename">
    667. 

  667.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-bbregister_bold.svg</a>
    668. 

  668. </div>
    669. 

  669. 

  670.         </div>
    671. 

  671.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-fingerfootlips">
    672. 

  672. <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-09/figures/sub-09_ses-test_task-fingerfootlips_desc-rois_bold.svg" style="width: 100%" />
    673. 

  673. </div>
    674. 

  674. <div class="elem-filename">
    675. 

  675.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-rois_bold.svg</a>
    676. 

  676. </div>
    677. 

  677. 

  678.         </div>
    679. 

  679.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-fingerfootlips">
    680. 

  680. <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-09/figures/sub-09_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg" style="width: 100%" />
    681. 

  681. </div>
    682. 

  682. <div class="elem-filename">
    683. 

  683.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg</a>
    684. 

  684. </div>
    685. 

  685. 

  686.         </div>
    687. 

  687.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-fingerfootlips">
    688. 

  688. <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-09/figures/sub-09_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg" style="width: 100%" />
    689. 

  689. </div>
    690. 

  690. <div class="elem-filename">
    691. 

  691.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg</a>
    692. 

  692. </div>
    693. 

  693. 

  694.         </div>
    695. 

  695.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-fingerfootlips">
    696. 

  696. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    697. 

  697. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    698. 

  698. Right: magnitude of the correlation between each confound time series and the
    699. 

  699. mean global signal. Strong correlations might be indicative of partial volume
    700. 

  700. effects and can inform decisions about feature orthogonalization prior to
    701. 

  701. confound regression.
    702. 

  702. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg" style="width: 100%" />
    703. 

  703. </div>
    704. 

  704. <div class="elem-filename">
    705. 

  705.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg</a>
    706. 

  706. </div>
    707. 

  707. 

  708.         </div>
    709. 

  709.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-fingerfootlips">
    710. 

  710. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    711. 

  711. black brain outline. Right hand side of each map: time series (top in seconds),
    712. 

  712. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    713. 

  713. in green; noise components in red.
    714. 

  714. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-aroma_bold.svg" style="width: 100%" />
    715. 

  715. </div>
    716. 

  716. <div class="elem-filename">
    717. 

  717.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-fingerfootlips_desc-aroma_bold.svg</a>
    718. 

  718. </div>
    719. 

  719. 

  720.         </div>
    721. 

  721.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-linebisection">
    722. 

  722. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">linebisection</span>.</h2>                    <details open>
    723. 

  723. 		<summary>Summary</summary>
    724. 

  724. 		<ul class="elem-desc">
    725. 

  725. 			<li>Repetition time (TR): 2.5s</li>
    726. 

  726. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    727. 

  727. 			<li>Single-echo EPI sequence.</li>
    728. 

  728. 			<li>Slice timing correction: Applied</li>
    729. 

  729. 			<li>Susceptibility distortion correction: None</li>
    730. 

  730. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    731. 

  731. 			<li>Non-steady-state volumes: 1</li>
    732. 

  732. 		</ul>
    733. 

  733. 		</details>
    734. 

  734. 		<details>
    735. 

  735. 			<summary>Confounds collected</summary><br />
    736. 

  736. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_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_09, aroma_motion_10, aroma_motion_12, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_17, aroma_motion_18, aroma_motion_19, aroma_motion_20, aroma_motion_21, aroma_motion_22, aroma_motion_23, aroma_motion_26, aroma_motion_27, aroma_motion_28, aroma_motion_29, aroma_motion_30, aroma_motion_31, aroma_motion_32, aroma_motion_33.</p>
    737. 

  737. 		</details>
    738. 

  738.         </div>
    739. 

  739.         <div id="datatype-figures_desc-validation_session-test_suffix-bold_task-linebisection">
    740. 

  740.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    741. 

  741. 			    <p class="elem-desc">
    742. 

  742. 			    The qform has been copied from sform.
    743. 

  743. 			    The difference in angle is -0.00053.
    744. 

  744. 			    The difference in translation is 4.7e-15.
    745. 

  745. 			    </p>
    746. 

  746.         </div>
    747. 

  747.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-linebisection">
    748. 

  748. <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-09/figures/sub-09_ses-test_task-linebisection_desc-bbregister_bold.svg">
    749. 

  749. Problem loading figure sub-09/figures/sub-09_ses-test_task-linebisection_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    750. 

  750. </div>
    751. 

  751. <div class="elem-filename">
    752. 

  752.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-linebisection_desc-bbregister_bold.svg</a>
    753. 

  753. </div>
    754. 

  754. 

  755.         </div>
    756. 

  756.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-linebisection">
    757. 

  757. <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-09/figures/sub-09_ses-test_task-linebisection_desc-rois_bold.svg" style="width: 100%" />
    758. 

  758. </div>
    759. 

  759. <div class="elem-filename">
    760. 

  760.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-linebisection_desc-rois_bold.svg</a>
    761. 

  761. </div>
    762. 

  762. 

  763.         </div>
    764. 

  764.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-linebisection">
    765. 

  765. <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-09/figures/sub-09_ses-test_task-linebisection_desc-compcorvar_bold.svg" style="width: 100%" />
    766. 

  766. </div>
    767. 

  767. <div class="elem-filename">
    768. 

  768.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-linebisection_desc-compcorvar_bold.svg</a>
    769. 

  769. </div>
    770. 

  770. 

  771.         </div>
    772. 

  772.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-linebisection">
    773. 

  773. <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-09/figures/sub-09_ses-test_task-linebisection_desc-carpetplot_bold.svg" style="width: 100%" />
    774. 

  774. </div>
    775. 

  775. <div class="elem-filename">
    776. 

  776.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-linebisection_desc-carpetplot_bold.svg</a>
    777. 

  777. </div>
    778. 

  778. 

  779.         </div>
    780. 

  780.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-linebisection">
    781. 

  781. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    782. 

  782. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    783. 

  783. Right: magnitude of the correlation between each confound time series and the
    784. 

  784. mean global signal. Strong correlations might be indicative of partial volume
    785. 

  785. effects and can inform decisions about feature orthogonalization prior to
    786. 

  786. confound regression.
    787. 

  787. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-confoundcorr_bold.svg" style="width: 100%" />
    788. 

  788. </div>
    789. 

  789. <div class="elem-filename">
    790. 

  790.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-linebisection_desc-confoundcorr_bold.svg</a>
    791. 

  791. </div>
    792. 

  792. 

  793.         </div>
    794. 

  794.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-linebisection">
    795. 

  795. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    796. 

  796. black brain outline. Right hand side of each map: time series (top in seconds),
    797. 

  797. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    798. 

  798. in green; noise components in red.
    799. 

  799. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-aroma_bold.svg" style="width: 100%" />
    800. 

  800. </div>
    801. 

  801. <div class="elem-filename">
    802. 

  802.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-linebisection_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-linebisection_desc-aroma_bold.svg</a>
    803. 

  803. </div>
    804. 

  804. 

  805.         </div>
    806. 

  806.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-overtverbgeneration">
    807. 

  807. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtverbgeneration</span>.</h2>                    <details open>
    808. 

  808. 		<summary>Summary</summary>
    809. 

  809. 		<ul class="elem-desc">
    810. 

  810. 			<li>Repetition time (TR): 5s</li>
    811. 

  811. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    812. 

  812. 			<li>Single-echo EPI sequence.</li>
    813. 

  813. 			<li>Slice timing correction: Applied</li>
    814. 

  814. 			<li>Susceptibility distortion correction: None</li>
    815. 

  815. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    816. 

  816. 			<li>Non-steady-state volumes: 1</li>
    817. 

  817. 		</ul>
    818. 

  818. 		</details>
    819. 

  819. 		<details>
    820. 

  820. 			<summary>Confounds collected</summary><br />
    821. 

  821. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, cosine00, cosine01, cosine02, cosine03, cosine04, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_05, aroma_motion_06, aroma_motion_07, aroma_motion_08, aroma_motion_10, aroma_motion_12, aroma_motion_14, aroma_motion_16, aroma_motion_17, aroma_motion_20, aroma_motion_22, aroma_motion_23, aroma_motion_24, aroma_motion_25, aroma_motion_26, aroma_motion_27.</p>
    822. 

  822. 		</details>
    823. 

  823.         </div>
    824. 

  824.         <div id="datatype-figures_desc-validation_session-test_suffix-bold_task-overtverbgeneration">
    825. 

  825.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    826. 

  826. 			    <p class="elem-desc">
    827. 

  827. 			    The qform has been copied from sform.
    828. 

  828. 			    The difference in angle is -0.00053.
    829. 

  829. 			    The difference in translation is 4.7e-15.
    830. 

  830. 			    </p>
    831. 

  831.         </div>
    832. 

  832.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-overtverbgeneration">
    833. 

  833. <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-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg">
    834. 

  834. Problem loading figure sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    835. 

  835. </div>
    836. 

  836. <div class="elem-filename">
    837. 

  837.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg</a>
    838. 

  838. </div>
    839. 

  839. 

  840.         </div>
    841. 

  841.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-overtverbgeneration">
    842. 

  842. <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-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-rois_bold.svg" style="width: 100%" />
    843. 

  843. </div>
    844. 

  844. <div class="elem-filename">
    845. 

  845.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-rois_bold.svg</a>
    846. 

  846. </div>
    847. 

  847. 

  848.         </div>
    849. 

  849.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-overtverbgeneration">
    850. 

  850. <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-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
    851. 

  851. </div>
    852. 

  852. <div class="elem-filename">
    853. 

  853.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg</a>
    854. 

  854. </div>
    855. 

  855. 

  856.         </div>
    857. 

  857.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-overtverbgeneration">
    858. 

  858. <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-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
    859. 

  859. </div>
    860. 

  860. <div class="elem-filename">
    861. 

  861.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg</a>
    862. 

  862. </div>
    863. 

  863. 

  864.         </div>
    865. 

  865.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-overtverbgeneration">
    866. 

  866. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    867. 

  867. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    868. 

  868. Right: magnitude of the correlation between each confound time series and the
    869. 

  869. mean global signal. Strong correlations might be indicative of partial volume
    870. 

  870. effects and can inform decisions about feature orthogonalization prior to
    871. 

  871. confound regression.
    872. 

  872. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
    873. 

  873. </div>
    874. 

  874. <div class="elem-filename">
    875. 

  875.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg</a>
    876. 

  876. </div>
    877. 

  877. 

  878.         </div>
    879. 

  879.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-overtverbgeneration">
    880. 

  880. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    881. 

  881. black brain outline. Right hand side of each map: time series (top in seconds),
    882. 

  882. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    883. 

  883. in green; noise components in red.
    884. 

  884. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
    885. 

  885. </div>
    886. 

  886. <div class="elem-filename">
    887. 

  887.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtverbgeneration_desc-aroma_bold.svg</a>
    888. 

  888. </div>
    889. 

  889. 

  890.         </div>
    891. 

  891.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-overtwordrepetition">
    892. 

  892. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtwordrepetition</span>.</h2>                    <details open>
    893. 

  893. 		<summary>Summary</summary>
    894. 

  894. 		<ul class="elem-desc">
    895. 

  895. 			<li>Repetition time (TR): 5s</li>
    896. 

  896. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    897. 

  897. 			<li>Single-echo EPI sequence.</li>
    898. 

  898. 			<li>Slice timing correction: Applied</li>
    899. 

  899. 			<li>Susceptibility distortion correction: None</li>
    900. 

  900. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    901. 

  901. 			<li>Non-steady-state volumes: 1</li>
    902. 

  902. 		</ul>
    903. 

  903. 		</details>
    904. 

  904. 		<details>
    905. 

  905. 			<summary>Confounds collected</summary><br />
    906. 

  906. 			<p>csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_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, 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, cosine00, cosine01, cosine02, cosine03, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_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_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, aroma_motion_06, aroma_motion_09, aroma_motion_10, aroma_motion_12, aroma_motion_13, aroma_motion_15, aroma_motion_17, aroma_motion_21, aroma_motion_23.</p>
    907. 

  907. 		</details>
    908. 

  908.         </div>
    909. 

  909.         <div id="datatype-figures_desc-validation_session-test_suffix-bold_task-overtwordrepetition">
    910. 

  910.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    911. 

  911. 			    <p class="elem-desc">
    912. 

  912. 			    The qform has been copied from sform.
    913. 

  913. 			    The difference in angle is 2.4e-07.
    914. 

  914. 			    The difference in translation is 1.8e-15.
    915. 

  915. 			    </p>
    916. 

  916.         </div>
    917. 

  917.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-overtwordrepetition">
    918. 

  918. <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-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg">
    919. 

  919. Problem loading figure sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    920. 

  920. </div>
    921. 

  921. <div class="elem-filename">
    922. 

  922.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg</a>
    923. 

  923. </div>
    924. 

  924. 

  925.         </div>
    926. 

  926.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-overtwordrepetition">
    927. 

  927. <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-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-rois_bold.svg" style="width: 100%" />
    928. 

  928. </div>
    929. 

  929. <div class="elem-filename">
    930. 

  930.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-rois_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-rois_bold.svg</a>
    931. 

  931. </div>
    932. 

  932. 

  933.         </div>
    934. 

  934.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-overtwordrepetition">
    935. 

  935. <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-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg" style="width: 100%" />
    936. 

  936. </div>
    937. 

  937. <div class="elem-filename">
    938. 

  938.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg</a>
    939. 

  939. </div>
    940. 

  940. 

  941.         </div>
    942. 

  942.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-overtwordrepetition">
    943. 

  943. <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-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg" style="width: 100%" />
    944. 

  944. </div>
    945. 

  945. <div class="elem-filename">
    946. 

  946.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg</a>
    947. 

  947. </div>
    948. 

  948. 

  949.         </div>
    950. 

  950.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-overtwordrepetition">
    951. 

  951. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    952. 

  952. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    953. 

  953. Right: magnitude of the correlation between each confound time series and the
    954. 

  954. mean global signal. Strong correlations might be indicative of partial volume
    955. 

  955. effects and can inform decisions about feature orthogonalization prior to
    956. 

  956. confound regression.
    957. 

  957. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg" style="width: 100%" />
    958. 

  958. </div>
    959. 

  959. <div class="elem-filename">
    960. 

  960.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg</a>
    961. 

  961. </div>
    962. 

  962. 

  963.         </div>
    964. 

  964.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-overtwordrepetition">
    965. 

  965. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    966. 

  966. black brain outline. Right hand side of each map: time series (top in seconds),
    967. 

  967. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    968. 

  968. in green; noise components in red.
    969. 

  969. </p>                    <img class="svg-reportlet" src="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-aroma_bold.svg" style="width: 100%" />
    970. 

  970. </div>
    971. 

  971. <div class="elem-filename">
    972. 

  972.     Get figure file: <a href="./sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-aroma_bold.svg" target="_blank">sub-09/figures/sub-09_ses-test_task-overtwordrepetition_desc-aroma_bold.svg</a>
    973. 

  973. </div>
    974. 

  974. 

  975.         </div>
    976. 

  976.     </div>
    977. 

  977.     <div id="About">
    978. 

  978.     <h1 class="sub-report-title">About</h1>
    979. 

  979.         <div id="datatype-figures_desc-about_suffix-T1w">
    980. 

  980.                     <ul>
    981. 

  981. 		<li>fMRIPrep version: 20.1.1+79.g1a72777b</li>
    982. 

  982. 		<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 09 -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>
    983. 

  983. 		<li>Date preprocessed: 2020-07-24 23:39:47 -0700</li>
    984. 

  984. 	</ul>
    985. 

  985. </div>
    986. 

  986.         </div>
    987. 

  987.     </div>
    988. 

  988. 

  989. <div id="boilerplate">
    990. 

  990.     <h1 class="sub-report-title">Methods</h1>
    991. 

  991.     <p>We kindly ask to report results preprocessed with this tool using the following
    992. 

  992.        boilerplate.</p>
    993. 

  993.     <ul class="nav nav-tabs" id="myTab" role="tablist">
    994. 

  994.         <li class="nav-item">
    995. 

  995.             <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
    996. 

  996.         </li>
    997. 

  997.         <li class="nav-item">
    998. 

  998.             <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
    999. 

  999.         </li>
    1000. 

  1000.         <li class="nav-item">
    1001. 

  1001.             <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
    1002. 

  1002.         </li>
    1003. 

  1003.     </ul>
    1004. 

  1004.     <div class="tab-content" id="myTabContent">
    1005. 

  1005.       <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>
    1006. 

  1006. <dl>
    1007. 

  1007. <dt>Anatomical data preprocessing</dt>
    1008. 

  1008. <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>
    1009. 

  1009. </dd>
    1010. 

  1010. <dt>Functional data preprocessing</dt>
    1011. 

  1011. <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>
    1012. 

  1012. </dd>
    1013. 

  1013. </dl>
    1014. 

  1014. <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>
    1015. 

  1015. <h3 id="copyright-waiver">Copyright Waiver</h3>
    1016. 

  1016. <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>
    1017. 

  1017. <h3 id="references" class="unnumbered">References</h3>
    1018. 

  1018. <div id="refs" class="references">
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  1019. <div id="ref-nilearn">
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    1021. 

  1021. </div>
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  1022. <div id="ref-ants">
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  1045. </div>
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  1051. </div>
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  1081. </div>
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  1082. <div id="ref-fsl_fast">
    1083. 

  1083. <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>
    1084. 

  1084. </div>
    1085. 

  1085. </div></div></div>
    1086. 

  1086.       <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
    1087. 

  1087. Results included in this manuscript come from preprocessing
    1088. 

  1088. performed using *fMRIPrep* 20.1.1+79.g1a72777b
    1089. 

  1089. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    1090. 

  1090. which is based on *Nipype* 1.5.0
    1091. 

  1091. (@nipype1; @nipype2; RRID:SCR_002502).
    1092. 

  1092. 

  1093. Anatomical data preprocessing
    1094. 

  1094. 

  1095. : A total of 2 T1-weighted (T1w) images were found within the input
    1096. 

  1096. BIDS dataset.
    1097. 

  1097. All of them were corrected for intensity non-uniformity (INU)
    1098. 

  1098. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757].
    1099. 

  1099. The T1w-reference was then skull-stripped with a *Nipype* implementation of
    1100. 

  1100. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
    1101. 

  1101. as target template.
    1102. 

  1102. Brain tissue segmentation of cerebrospinal fluid (CSF),
    1103. 

  1103. white-matter (WM) and gray-matter (GM) was performed on
    1104. 

  1104. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
    1105. 

  1105. @fsl_fast].
    1106. 

  1106. A T1w-reference map was computed after registration of
    1107. 

  1107. 2 T1w images (after INU-correction) using
    1108. 

  1108. `mri_robust_template` [FreeSurfer 6.0.1, @fs_template].
    1109. 

  1109. Brain surfaces were reconstructed using `recon-all` [FreeSurfer 6.0.1,
    1110. 

  1110. RRID:SCR_001847, @fs_reconall], and the brain mask estimated
    1111. 

  1111. previously was refined with a custom variation of the method to reconcile
    1112. 

  1112. ANTs-derived and FreeSurfer-derived segmentations of the cortical
    1113. 

  1113. gray-matter of Mindboggle [RRID:SCR_002438, @mindboggle].
    1114. 

  1114. Volume-based spatial normalization to two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was performed through
    1115. 

  1115. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
    1116. 

  1116. using brain-extracted versions of both T1w reference and the T1w template.
    1117. 

  1117. The following templates were selected for spatial normalization:
    1118. 

  1118. *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], 
    1119. 

  1119. 

  1120. Functional data preprocessing
    1121. 

  1121. 

  1122. : For each of the 10 BOLD runs found per subject (across all
    1123. 

  1123. tasks and sessions), the following preprocessing was performed.
    1124. 

  1124. First, a reference volume and its skull-stripped version were generated
    1125. 

  1125.  using a custom
    1126. 

  1126. methodology of *fMRIPrep*.
    1127. 

  1127. Susceptibility distortion correction (SDC) was omitted.
    1128. 

  1128. The BOLD reference was then co-registered to the T1w reference using
    1129. 

  1129. `bbregister` (FreeSurfer) which implements boundary-based registration [@bbr].
    1130. 

  1130. Co-registration was configured with six degrees of freedom.
    1131. 

  1131. Head-motion parameters with respect to the BOLD reference
    1132. 

  1132. (transformation matrices, and six corresponding rotation and translation
    1133. 

  1133. parameters) are estimated before any spatiotemporal filtering using
    1134. 

  1134. `mcflirt` [FSL 5.0.9, @mcflirt].
    1135. 

  1135. BOLD runs were slice-time corrected using `3dTshift` from
    1136. 

  1136. AFNI 20160207 [@afni, RRID:SCR_005927].
    1137. 

  1137. The BOLD time-series were resampled onto the following surfaces
    1138. 

  1138. (FreeSurfer reconstruction nomenclature):
    1139. 

  1139. *fsaverage*.
    1140. 

  1140. The BOLD time-series (including slice-timing correction when applied)
    1141. 

  1141. were resampled onto their original, native space by applying
    1142. 

  1142. the transforms to correct for head-motion.
    1143. 

  1143. These resampled BOLD time-series will be referred to as *preprocessed
    1144. 

  1144. BOLD in original space*, or just *preprocessed BOLD*.
    1145. 

  1145. *Grayordinates* files [@hcppipelines] containing 91k samples were also
    1146. 

  1146. generated using the highest-resolution ``fsaverage`` as intermediate standardized
    1147. 

  1147. surface space.
    1148. 

  1148. Automatic removal of motion artifacts using independent component analysis
    1149. 

  1149. [ICA-AROMA, @aroma] was performed on the *preprocessed BOLD on MNI space*
    1150. 

  1150. time-series after removal of non-steady state volumes and spatial smoothing
    1151. 

  1151. with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum).
    1152. 

  1152. Corresponding "non-aggresively" denoised runs were produced after such
    1153. 

  1153. smoothing.
    1154. 

  1154. Additionally, the "aggressive" noise-regressors were collected and placed
    1155. 

  1155. in the corresponding confounds file.
    1156. 

  1156. Several confounding time-series were calculated based on the
    1157. 

  1157. *preprocessed BOLD*: framewise displacement (FD), DVARS and
    1158. 

  1158. three region-wise global signals.
    1159. 

  1159. FD was computed using two formulations following Power (absolute sum of
    1160. 

  1160. relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
    1161. 

  1161. displacement between affines, @mcflirt).
    1162. 

  1162. FD and DVARS are calculated for each functional run, both using their
    1163. 

  1163. implementations in *Nipype* [following the definitions by @power_fd_dvars].
    1164. 

  1164. The three global signals are extracted within the CSF, the WM, and
    1165. 

  1165. the whole-brain masks.
    1166. 

  1166. Additionally, a set of physiological regressors were extracted to
    1167. 

  1167. allow for component-based noise correction [*CompCor*, @compcor].
    1168. 

  1168. Principal components are estimated after high-pass filtering the
    1169. 

  1169. *preprocessed BOLD* time-series (using a discrete cosine filter with
    1170. 

  1170. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    1171. 

  1171. and anatomical (aCompCor).
    1172. 

  1172. tCompCor components are then calculated from the top 2% variable
    1173. 

  1173. voxels within the brain mask.
    1174. 

  1174. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
    1175. 

  1175. are generated in anatomical space.
    1176. 

  1176. The implementation differs from that of Behzadi et al. in that instead
    1177. 

  1177. of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
    1178. 

  1178. subtracted a mask of pixels that likely contain a volume fraction of GM.
    1179. 

  1179. This mask is obtained by dilating a GM mask extracted from the FreeSurfer's *aseg* segmentation, and it ensures components are not extracted
    1180. 

  1180. from voxels containing a minimal fraction of GM.
    1181. 

  1181. Finally, these masks are resampled into BOLD space and binarized by
    1182. 

  1182. thresholding at 0.99 (as in the original implementation).
    1183. 

  1183. Components are also calculated separately within the WM and CSF masks.
    1184. 

  1184. For each CompCor decomposition, the *k* components with the largest singular
    1185. 

  1185. values are retained, such that the retained components' time series are
    1186. 

  1186. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
    1187. 

  1187. WM, combined, or temporal). The remaining components are dropped from
    1188. 

  1188. consideration.
    1189. 

  1189. The head-motion estimates calculated in the correction step were also
    1190. 

  1190. placed within the corresponding confounds file.
    1191. 

  1191. The confound time series derived from head motion estimates and global
    1192. 

  1192. signals were expanded with the inclusion of temporal derivatives and
    1193. 

  1193. quadratic terms for each [@confounds_satterthwaite_2013].
    1194. 

  1194. Frames that exceeded a threshold of 0.5 mm FD or
    1195. 

  1195. 1.5 standardised DVARS were annotated as motion outliers.
    1196. 

  1196. All resamplings can be performed with *a single interpolation
    1197. 

  1197. step* by composing all the pertinent transformations (i.e. head-motion
    1198. 

  1198. transform matrices, susceptibility distortion correction when available,
    1199. 

  1199. and co-registrations to anatomical and output spaces).
    1200. 

  1200. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
    1201. 

  1201. configured with Lanczos interpolation to minimize the smoothing
    1202. 

  1202. effects of other kernels [@lanczos].
    1203. 

  1203. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
    1204. 

  1204. (FreeSurfer).
    1205. 

  1205. 

  1206. 

  1207. Many internal operations of *fMRIPrep* use
    1208. 

  1208. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
    1209. 

  1209. mostly within the functional processing workflow.
    1210. 

  1210. For more details of the pipeline, see [the section corresponding
    1211. 

  1211. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
    1212. 

  1212. 

  1213. 

  1214. ### Copyright Waiver
    1215. 

  1215. 

  1216. The above boilerplate text was automatically generated by fMRIPrep
    1217. 

  1217. with the express intention that users should copy and paste this
    1218. 

  1218. text into their manuscripts *unchanged*.
    1219. 

  1219. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
    1220. 

  1220. 

  1221. ### References
    1222. 

  1222. 

  1223. </pre>
    1224. 

  1224. </div>
    1225. 

  1225.       <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
    1226. 

  1226. using \emph{fMRIPrep} 20.1.1+79.g1a72777b (\citet{fmriprep1};
    1227. 

  1227. \citet{fmriprep2}; RRID:SCR\_016216), which is based on \emph{Nipype}
    1228. 

  1228. 1.5.0 (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
    1229. 

  1229. 

  1230. \begin{description}
    1231. 

  1231. \item[Anatomical data preprocessing]
    1232. 

  1232. A total of 2 T1-weighted (T1w) images were found within the input BIDS
    1233. 

  1233. dataset. All of them were corrected for intensity non-uniformity (INU)
    1234. 

  1234. with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with ANTs
    1235. 

  1235. 2.2.0 \citep[RRID:SCR\_004757]{ants}. The T1w-reference was then
    1236. 

  1236. skull-stripped with a \emph{Nipype} implementation of the
    1237. 

  1237. \texttt{antsBrainExtraction.sh} workflow (from ANTs), using OASIS30ANTs
    1238. 

  1238. as target template. Brain tissue segmentation of cerebrospinal fluid
    1239. 

  1239. (CSF), white-matter (WM) and gray-matter (GM) was performed on the
    1240. 

  1240. brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
    1241. 

  1241. RRID:SCR\_002823,][]{fsl_fast}. A T1w-reference map was computed after
    1242. 

  1242. registration of 2 T1w images (after INU-correction) using
    1243. 

  1243. \texttt{mri\_robust\_template} \citep[FreeSurfer 6.0.1,][]{fs_template}.
    1244. 

  1244. Brain surfaces were reconstructed using \texttt{recon-all}
    1245. 

  1245. \citep[FreeSurfer 6.0.1, RRID:SCR\_001847,][]{fs_reconall}, and the
    1246. 

  1246. brain mask estimated previously was refined with a custom variation of
    1247. 

  1247. the method to reconcile ANTs-derived and FreeSurfer-derived
    1248. 

  1248. segmentations of the cortical gray-matter of Mindboggle
    1249. 

  1249. \citep[RRID:SCR\_002438,][]{mindboggle}. Volume-based spatial
    1250. 

  1250. normalization to two standard spaces (MNI152NLin2009cAsym,
    1251. 

  1251. MNI152NLin6Asym) was performed through nonlinear registration with
    1252. 

  1252. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
    1253. 

  1253. of both T1w reference and the T1w template. The following templates were
    1254. 

  1254. selected for spatial normalization: \emph{ICBM 152 Nonlinear
    1255. 

  1255. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
    1256. 

  1256. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]}, \emph{FSL's
    1257. 

  1257. MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain
    1258. 

  1258. Stereotaxic Registration Model} {[}\citet{mni152nlin6asym},
    1259. 

  1259. RRID:SCR\_002823; TemplateFlow ID: MNI152NLin6Asym{]},
    1260. 

  1260. \item[Functional data preprocessing]
    1261. 

  1261. For each of the 10 BOLD runs found per subject (across all tasks and
    1262. 

  1262. sessions), the following preprocessing was performed. First, a reference
    1263. 

  1263. volume and its skull-stripped version were generated using a custom
    1264. 

  1264. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
    1265. 

  1265. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
    1266. 

  1266. reference using \texttt{bbregister} (FreeSurfer) which implements
    1267. 

  1267. boundary-based registration \citep{bbr}. Co-registration was configured
    1268. 

  1268. with six degrees of freedom. Head-motion parameters with respect to the
    1269. 

  1269. BOLD reference (transformation matrices, and six corresponding rotation
    1270. 

  1270. and translation parameters) are estimated before any spatiotemporal
    1271. 

  1271. filtering using \texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. BOLD
    1272. 

  1272. runs were slice-time corrected using \texttt{3dTshift} from AFNI
    1273. 

  1273. 20160207 \citep[RRID:SCR\_005927]{afni}. The BOLD time-series were
    1274. 

  1274. resampled onto the following surfaces (FreeSurfer reconstruction
    1275. 

  1275. nomenclature): \emph{fsaverage}. The BOLD time-series (including
    1276. 

  1276. slice-timing correction when applied) were resampled onto their
    1277. 

  1277. original, native space by applying the transforms to correct for
    1278. 

  1278. head-motion. These resampled BOLD time-series will be referred to as
    1279. 

  1279. \emph{preprocessed BOLD in original space}, or just \emph{preprocessed
    1280. 

  1280. BOLD}. \emph{Grayordinates} files \citep{hcppipelines} containing 91k
    1281. 

  1281. samples were also generated using the highest-resolution
    1282. 

  1282. \texttt{fsaverage} as intermediate standardized surface space. Automatic
    1283. 

  1283. removal of motion artifacts using independent component analysis
    1284. 

  1284. \citep[ICA-AROMA,][]{aroma} was performed on the \emph{preprocessed BOLD
    1285. 

  1285. on MNI space} time-series after removal of non-steady state volumes and
    1286. 

  1286. spatial smoothing with an isotropic, Gaussian kernel of 6mm FWHM
    1287. 

  1287. (full-width half-maximum). Corresponding ``non-aggresively'' denoised
    1288. 

  1288. runs were produced after such smoothing. Additionally, the
    1289. 

  1289. ``aggressive'' noise-regressors were collected and placed in the
    1290. 

  1290. corresponding confounds file. Several confounding time-series were
    1291. 

  1291. calculated based on the \emph{preprocessed BOLD}: framewise displacement
    1292. 

  1292. (FD), DVARS and three region-wise global signals. FD was computed using
    1293. 

  1293. two formulations following Power (absolute sum of relative motions,
    1294. 

  1294. \citet{power_fd_dvars}) and Jenkinson (relative root mean square
    1295. 

  1295. displacement between affines, \citet{mcflirt}). FD and DVARS are
    1296. 

  1296. calculated for each functional run, both using their implementations in
    1297. 

  1297. \emph{Nipype} \citep[following the definitions by][]{power_fd_dvars}.
    1298. 

  1298. The three global signals are extracted within the CSF, the WM, and the
    1299. 

  1299. whole-brain masks. Additionally, a set of physiological regressors were
    1300. 

  1300. extracted to allow for component-based noise correction
    1301. 

  1301. \citep[\emph{CompCor},][]{compcor}. Principal components are estimated
    1302. 

  1302. after high-pass filtering the \emph{preprocessed BOLD} time-series
    1303. 

  1303. (using a discrete cosine filter with 128s cut-off) for the two
    1304. 

  1304. \emph{CompCor} variants: temporal (tCompCor) and anatomical (aCompCor).
    1305. 

  1305. tCompCor components are then calculated from the top 2\% variable voxels
    1306. 

  1306. within the brain mask. For aCompCor, three probabilistic masks (CSF, WM
    1307. 

  1307. and combined CSF+WM) are generated in anatomical space. The
    1308. 

  1308. implementation differs from that of Behzadi et al.~in that instead of
    1309. 

  1309. eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
    1310. 

  1310. subtracted a mask of pixels that likely contain a volume fraction of GM.
    1311. 

  1311. This mask is obtained by dilating a GM mask extracted from the
    1312. 

  1312. FreeSurfer's \emph{aseg} segmentation, and it ensures components are not
    1313. 

  1313. extracted from voxels containing a minimal fraction of GM. Finally,
    1314. 

  1314. these masks are resampled into BOLD space and binarized by thresholding
    1315. 

  1315. at 0.99 (as in the original implementation). Components are also
    1316. 

  1316. calculated separately within the WM and CSF masks. For each CompCor
    1317. 

  1317. decomposition, the \emph{k} components with the largest singular values
    1318. 

  1318. are retained, such that the retained components' time series are
    1319. 

  1319. sufficient to explain 50 percent of variance across the nuisance mask
    1320. 

  1320. (CSF, WM, combined, or temporal). The remaining components are dropped
    1321. 

  1321. from consideration. The head-motion estimates calculated in the
    1322. 

  1322. correction step were also placed within the corresponding confounds
    1323. 

  1323. file. The confound time series derived from head motion estimates and
    1324. 

  1324. global signals were expanded with the inclusion of temporal derivatives
    1325. 

  1325. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
    1326. 

  1326. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    1327. 

  1327. were annotated as motion outliers. All resamplings can be performed with
    1328. 

  1328. \emph{a single interpolation step} by composing all the pertinent
    1329. 

  1329. transformations (i.e.~head-motion transform matrices, susceptibility
    1330. 

  1330. distortion correction when available, and co-registrations to anatomical
    1331. 

  1331. and output spaces). Gridded (volumetric) resamplings were performed
    1332. 

  1332. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
    1333. 

  1333. interpolation to minimize the smoothing effects of other kernels
    1334. 

  1334. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
    1335. 

  1335. \texttt{mri\_vol2surf} (FreeSurfer).
    1336. 

  1336. \end{description}
    1337. 

  1337. 

  1338. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
    1339. 

  1339. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
    1340. 

  1340. processing workflow. For more details of the pipeline, see
    1341. 

  1341. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
    1342. 

  1342. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
    1343. 

  1343. 

  1344. \hypertarget{copyright-waiver}{%
    1345. 

  1345. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
    1346. 

  1346. 

  1347. The above boilerplate text was automatically generated by fMRIPrep with
    1348. 

  1348. the express intention that users should copy and paste this text into
    1349. 

  1349. their manuscripts \emph{unchanged}. It is released under the
    1350. 

  1350. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
    1351. 

  1351. 

  1352. \hypertarget{references}{%
    1353. 

  1353. \subsubsection{References}\label{references}}
    1354. 

  1354. 

  1355. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
    1356. 

  1356. <h3>Bibliography</h3>
    1357. 

  1357. <pre>@article{fmriprep1,
    1358. 

  1358.     author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
    1359. 

  1359.     title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
    1360. 

  1360.     year = {2018},
    1361. 

  1361.     doi = {10.1038/s41592-018-0235-4},
    1362. 

  1362.     journal = {Nature Methods}
    1363. 

  1363. }
    1364. 

  1364. 

  1365. @article{fmriprep2,
    1366. 

  1366.     author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
    1367. 

  1367.     title = {fMRIPrep},
    1368. 

  1368.     year = 2018,
    1369. 

  1369.     doi = {10.5281/zenodo.852659},
    1370. 

  1370.     publisher = {Zenodo},
    1371. 

  1371.     journal = {Software}
    1372. 

  1372. }
    1373. 

  1373. 

  1374. @article{nipype1,
    1375. 

  1375.     author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
    1376. 

  1376.     doi = {10.3389/fninf.2011.00013},
    1377. 

  1377.     journal = {Frontiers in Neuroinformatics},
    1378. 

  1378.     pages = 13,
    1379. 

  1379.     shorttitle = {Nipype},
    1380. 

  1380.     title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
    1381. 

  1381.     volume = 5,
    1382. 

  1382.     year = 2011
    1383. 

  1383. }
    1384. 

  1384. 

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  1693.     title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
    1694. 

  1694.     volume = 42,
    1695. 

  1695.     year = 1999
    1696. 

  1696. }
    1697. 

  1697. </pre>
    1698. 

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    1699. 

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