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  56. </head>
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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: 02</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: 256x256x256</li>
    118. 

  118. 			<li>Output voxel size: 1mm x 1mm 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-02/figures/sub-02_dseg.svg" style="width: 100%" />
    125. 

  125. </div>
    126. 

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

  127.     Get figure file: <a href="./sub-02/figures/sub-02_dseg.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_space-MNI152NLin6Asym_T1w.svg">
    133. 

  133. Problem loading figure sub-02/figures/sub-02_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-02/figures/sub-02_space-MNI152NLin6Asym_T1w.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_space-MNI152NLin2009cAsym_T1w.svg">
    140. 

  140. Problem loading figure sub-02/figures/sub-02_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-02/figures/sub-02_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_desc-reconall_T1w.svg" style="width: 100%" />
    149. 

  149. </div>
    150. 

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

  151.     Get figure file: <a href="./sub-02/figures/sub-02_desc-reconall_T1w.svg" target="_blank">sub-02/figures/sub-02_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: 1</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_power2, global_signal_derivative1_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, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_16, aroma_motion_17, aroma_motion_18, aroma_motion_19, aroma_motion_20, aroma_motion_21, aroma_motion_22, aroma_motion_23, aroma_motion_24, aroma_motion_25, aroma_motion_29, aroma_motion_31, aroma_motion_34.</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-02/figures/sub-02_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg">
    178. 

  178. Problem loading figure sub-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-covertverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-covertverbgeneration_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-covertverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-covertverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-covertverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-covertverbgeneration_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_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: 1</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_power2, global_signal_derivative1_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, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, aroma_motion_04, aroma_motion_05, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_13, aroma_motion_14, aroma_motion_15, aroma_motion_18, aroma_motion_19, aroma_motion_21, aroma_motion_22, aroma_motion_23, aroma_motion_24, aroma_motion_25, aroma_motion_26, aroma_motion_27, aroma_motion_28, aroma_motion_29, aroma_motion_33, aroma_motion_34, aroma_motion_35, aroma_motion_36, aroma_motion_37, aroma_motion_39, aroma_motion_40, aroma_motion_42, aroma_motion_47.</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-02/figures/sub-02_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg">
    255. 

  255. Problem loading figure sub-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-fingerfootlips_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-fingerfootlips_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-fingerfootlips_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-fingerfootlips_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-fingerfootlips_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-fingerfootlips_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_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: 2</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_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, 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, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_11, 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_24, aroma_motion_25, aroma_motion_26, aroma_motion_27, aroma_motion_28, aroma_motion_29, aroma_motion_30, aroma_motion_32, aroma_motion_33, aroma_motion_35, aroma_motion_36, aroma_motion_37, aroma_motion_38, aroma_motion_39, aroma_motion_40, aroma_motion_41, aroma_motion_43, aroma_motion_45, aroma_motion_46, aroma_motion_49, aroma_motion_51.</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-02/figures/sub-02_ses-retest_task-linebisection_desc-bbregister_bold.svg">
    332. 

  332. Problem loading figure sub-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-linebisection_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-linebisection_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-linebisection_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-linebisection_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-linebisection_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-linebisection_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_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_power2, global_signal_derivative1_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, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, aroma_motion_01, aroma_motion_02, aroma_motion_06, aroma_motion_07, aroma_motion_08, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_13, aroma_motion_16, aroma_motion_22, aroma_motion_26, aroma_motion_28, aroma_motion_32.</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-02/figures/sub-02_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg">
    409. 

  409. Problem loading figure sub-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-overtverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-overtverbgeneration_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-overtverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-overtverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-overtverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_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-02/figures/sub-02_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-02/figures/sub-02_ses-retest_task-overtverbgeneration_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_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_power2, global_signal_derivative1_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, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, 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_11, aroma_motion_15, aroma_motion_18, aroma_motion_20, aroma_motion_21, aroma_motion_23, aroma_motion_27, aroma_motion_29.</p>
    482. 

  482. 		</details>
    483. 

  483.         </div>
    484. 

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

  485. <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-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg">
    486. 

  486. Problem loading figure sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    487. 

  487. </div>
    488. 

  488. <div class="elem-filename">
    489. 

  489.     Get figure file: <a href="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-bbregister_bold.svg</a>
    490. 

  490. </div>
    491. 

  491. 

  492.         </div>
    493. 

  493.         <div id="datatype-figures_desc-rois_session-retest_suffix-bold_task-overtwordrepetition">
    494. 

  494. <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-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-rois_bold.svg" style="width: 100%" />
    495. 

  495. </div>
    496. 

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

  497.     Get figure file: <a href="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-rois_bold.svg</a>
    498. 

  498. </div>
    499. 

  499. 

  500.         </div>
    501. 

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

  502. <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-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg" style="width: 100%" />
    503. 

  503. </div>
    504. 

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

  505.     Get figure file: <a href="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-compcorvar_bold.svg</a>
    506. 

  506. </div>
    507. 

  507. 

  508.         </div>
    509. 

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

  510. <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-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg" style="width: 100%" />
    511. 

  511. </div>
    512. 

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

  513.     Get figure file: <a href="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-carpetplot_bold.svg</a>
    514. 

  514. </div>
    515. 

  515. 

  516.         </div>
    517. 

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

  518. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    519. 

  519. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    520. 

  520. Right: magnitude of the correlation between each confound time series and the
    521. 

  521. mean global signal. Strong correlations might be indicative of partial volume
    522. 

  522. effects and can inform decisions about feature orthogonalization prior to
    523. 

  523. confound regression.
    524. 

  524. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg" style="width: 100%" />
    525. 

  525. </div>
    526. 

  526. <div class="elem-filename">
    527. 

  527.     Get figure file: <a href="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-confoundcorr_bold.svg</a>
    528. 

  528. </div>
    529. 

  529. 

  530.         </div>
    531. 

  531.         <div id="datatype-figures_desc-aroma_session-retest_suffix-bold_task-overtwordrepetition">
    532. 

  532. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    533. 

  533. black brain outline. Right hand side of each map: time series (top in seconds),
    534. 

  534. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    535. 

  535. in green; noise components in red.
    536. 

  536. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg" style="width: 100%" />
    537. 

  537. </div>
    538. 

  538. <div class="elem-filename">
    539. 

  539.     Get figure file: <a href="./sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_ses-retest_task-overtwordrepetition_desc-aroma_bold.svg</a>
    540. 

  540. </div>
    541. 

  541. 

  542.         </div>
    543. 

  543.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-covertverbgeneration">
    544. 

  544. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">covertverbgeneration</span>.</h2>                    <details open>
    545. 

  545. 		<summary>Summary</summary>
    546. 

  546. 		<ul class="elem-desc">
    547. 

  547. 			<li>Repetition time (TR): 2.5s</li>
    548. 

  548. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    549. 

  549. 			<li>Single-echo EPI sequence.</li>
    550. 

  550. 			<li>Slice timing correction: Applied</li>
    551. 

  551. 			<li>Susceptibility distortion correction: None</li>
    552. 

  552. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    553. 

  553. 			<li>Non-steady-state volumes: 1</li>
    554. 

  554. 		</ul>
    555. 

  555. 		</details>
    556. 

  556. 		<details>
    557. 

  557. 			<summary>Confounds collected</summary><br />
    558. 

  558. 			<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_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, aroma_motion_01, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_11, aroma_motion_13, aroma_motion_14, aroma_motion_16, aroma_motion_17, aroma_motion_19, aroma_motion_21, aroma_motion_22, aroma_motion_24, aroma_motion_25, aroma_motion_26, aroma_motion_27, aroma_motion_30.</p>
    559. 

  559. 		</details>
    560. 

  560.         </div>
    561. 

  561.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-covertverbgeneration">
    562. 

  562. <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-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg">
    563. 

  563. Problem loading figure sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    564. 

  564. </div>
    565. 

  565. <div class="elem-filename">
    566. 

  566.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-bbregister_bold.svg</a>
    567. 

  567. </div>
    568. 

  568. 

  569.         </div>
    570. 

  570.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-covertverbgeneration">
    571. 

  571. <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-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-rois_bold.svg" style="width: 100%" />
    572. 

  572. </div>
    573. 

  573. <div class="elem-filename">
    574. 

  574.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-rois_bold.svg</a>
    575. 

  575. </div>
    576. 

  576. 

  577.         </div>
    578. 

  578.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-covertverbgeneration">
    579. 

  579. <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-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
    580. 

  580. </div>
    581. 

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

  582.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-compcorvar_bold.svg</a>
    583. 

  583. </div>
    584. 

  584. 

  585.         </div>
    586. 

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

  587. <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-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
    588. 

  588. </div>
    589. 

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

  590.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-carpetplot_bold.svg</a>
    591. 

  591. </div>
    592. 

  592. 

  593.         </div>
    594. 

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

  595. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    596. 

  596. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    597. 

  597. Right: magnitude of the correlation between each confound time series and the
    598. 

  598. mean global signal. Strong correlations might be indicative of partial volume
    599. 

  599. effects and can inform decisions about feature orthogonalization prior to
    600. 

  600. confound regression.
    601. 

  601. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
    602. 

  602. </div>
    603. 

  603. <div class="elem-filename">
    604. 

  604.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-confoundcorr_bold.svg</a>
    605. 

  605. </div>
    606. 

  606. 

  607.         </div>
    608. 

  608.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-covertverbgeneration">
    609. 

  609. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    610. 

  610. black brain outline. Right hand side of each map: time series (top in seconds),
    611. 

  611. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    612. 

  612. in green; noise components in red.
    613. 

  613. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
    614. 

  614. </div>
    615. 

  615. <div class="elem-filename">
    616. 

  616.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-covertverbgeneration_desc-aroma_bold.svg</a>
    617. 

  617. </div>
    618. 

  618. 

  619.         </div>
    620. 

  620.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-fingerfootlips">
    621. 

  621. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">fingerfootlips</span>.</h2>                    <details open>
    622. 

  622. 		<summary>Summary</summary>
    623. 

  623. 		<ul class="elem-desc">
    624. 

  624. 			<li>Repetition time (TR): 2.5s</li>
    625. 

  625. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    626. 

  626. 			<li>Single-echo EPI sequence.</li>
    627. 

  627. 			<li>Slice timing correction: Applied</li>
    628. 

  628. 			<li>Susceptibility distortion correction: None</li>
    629. 

  629. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    630. 

  630. 			<li>Non-steady-state volumes: 1</li>
    631. 

  631. 		</ul>
    632. 

  632. 		</details>
    633. 

  633. 		<details>
    634. 

  634. 			<summary>Confounds collected</summary><br />
    635. 

  635. 			<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_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, aroma_motion_02, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_14, aroma_motion_16, aroma_motion_17, aroma_motion_18, aroma_motion_19, aroma_motion_23, aroma_motion_24, aroma_motion_28, aroma_motion_29, aroma_motion_30, aroma_motion_32, aroma_motion_34, aroma_motion_35, aroma_motion_37, aroma_motion_38, aroma_motion_39.</p>
    636. 

  636. 		</details>
    637. 

  637.         </div>
    638. 

  638.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-fingerfootlips">
    639. 

  639. <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-02/figures/sub-02_ses-test_task-fingerfootlips_desc-bbregister_bold.svg">
    640. 

  640. Problem loading figure sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    641. 

  641. </div>
    642. 

  642. <div class="elem-filename">
    643. 

  643.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-bbregister_bold.svg</a>
    644. 

  644. </div>
    645. 

  645. 

  646.         </div>
    647. 

  647.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-fingerfootlips">
    648. 

  648. <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-02/figures/sub-02_ses-test_task-fingerfootlips_desc-rois_bold.svg" style="width: 100%" />
    649. 

  649. </div>
    650. 

  650. <div class="elem-filename">
    651. 

  651.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-rois_bold.svg</a>
    652. 

  652. </div>
    653. 

  653. 

  654.         </div>
    655. 

  655.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-fingerfootlips">
    656. 

  656. <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-02/figures/sub-02_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg" style="width: 100%" />
    657. 

  657. </div>
    658. 

  658. <div class="elem-filename">
    659. 

  659.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-compcorvar_bold.svg</a>
    660. 

  660. </div>
    661. 

  661. 

  662.         </div>
    663. 

  663.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-fingerfootlips">
    664. 

  664. <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-02/figures/sub-02_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg" style="width: 100%" />
    665. 

  665. </div>
    666. 

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

  667.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-carpetplot_bold.svg</a>
    668. 

  668. </div>
    669. 

  669. 

  670.         </div>
    671. 

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

  672. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    673. 

  673. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    674. 

  674. Right: magnitude of the correlation between each confound time series and the
    675. 

  675. mean global signal. Strong correlations might be indicative of partial volume
    676. 

  676. effects and can inform decisions about feature orthogonalization prior to
    677. 

  677. confound regression.
    678. 

  678. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg" style="width: 100%" />
    679. 

  679. </div>
    680. 

  680. <div class="elem-filename">
    681. 

  681.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-confoundcorr_bold.svg</a>
    682. 

  682. </div>
    683. 

  683. 

  684.         </div>
    685. 

  685.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-fingerfootlips">
    686. 

  686. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    687. 

  687. black brain outline. Right hand side of each map: time series (top in seconds),
    688. 

  688. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    689. 

  689. in green; noise components in red.
    690. 

  690. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-aroma_bold.svg" style="width: 100%" />
    691. 

  691. </div>
    692. 

  692. <div class="elem-filename">
    693. 

  693.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-fingerfootlips_desc-aroma_bold.svg</a>
    694. 

  694. </div>
    695. 

  695. 

  696.         </div>
    697. 

  697.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-linebisection">
    698. 

  698. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">linebisection</span>.</h2>                    <details open>
    699. 

  699. 		<summary>Summary</summary>
    700. 

  700. 		<ul class="elem-desc">
    701. 

  701. 			<li>Repetition time (TR): 2.5s</li>
    702. 

  702. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    703. 

  703. 			<li>Single-echo EPI sequence.</li>
    704. 

  704. 			<li>Slice timing correction: Applied</li>
    705. 

  705. 			<li>Susceptibility distortion correction: None</li>
    706. 

  706. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    707. 

  707. 			<li>Non-steady-state volumes: 1</li>
    708. 

  708. 		</ul>
    709. 

  709. 		</details>
    710. 

  710. 		<details>
    711. 

  711. 			<summary>Confounds collected</summary><br />
    712. 

  712. 			<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_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, rmsd, t_comp_cor_00, 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, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, aroma_motion_02, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_06, aroma_motion_10, aroma_motion_11, aroma_motion_12, aroma_motion_13, aroma_motion_14, aroma_motion_17, aroma_motion_18, aroma_motion_19, aroma_motion_20, aroma_motion_23, aroma_motion_25, aroma_motion_27, aroma_motion_29, aroma_motion_30, aroma_motion_32, aroma_motion_33, aroma_motion_34.</p>
    713. 

  713. 		</details>
    714. 

  714.         </div>
    715. 

  715.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-linebisection">
    716. 

  716. <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-02/figures/sub-02_ses-test_task-linebisection_desc-bbregister_bold.svg">
    717. 

  717. Problem loading figure sub-02/figures/sub-02_ses-test_task-linebisection_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    718. 

  718. </div>
    719. 

  719. <div class="elem-filename">
    720. 

  720.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-linebisection_desc-bbregister_bold.svg</a>
    721. 

  721. </div>
    722. 

  722. 

  723.         </div>
    724. 

  724.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-linebisection">
    725. 

  725. <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-02/figures/sub-02_ses-test_task-linebisection_desc-rois_bold.svg" style="width: 100%" />
    726. 

  726. </div>
    727. 

  727. <div class="elem-filename">
    728. 

  728.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-linebisection_desc-rois_bold.svg</a>
    729. 

  729. </div>
    730. 

  730. 

  731.         </div>
    732. 

  732.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-linebisection">
    733. 

  733. <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-02/figures/sub-02_ses-test_task-linebisection_desc-compcorvar_bold.svg" style="width: 100%" />
    734. 

  734. </div>
    735. 

  735. <div class="elem-filename">
    736. 

  736.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-linebisection_desc-compcorvar_bold.svg</a>
    737. 

  737. </div>
    738. 

  738. 

  739.         </div>
    740. 

  740.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-linebisection">
    741. 

  741. <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-02/figures/sub-02_ses-test_task-linebisection_desc-carpetplot_bold.svg" style="width: 100%" />
    742. 

  742. </div>
    743. 

  743. <div class="elem-filename">
    744. 

  744.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-linebisection_desc-carpetplot_bold.svg</a>
    745. 

  745. </div>
    746. 

  746. 

  747.         </div>
    748. 

  748.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-linebisection">
    749. 

  749. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    750. 

  750. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    751. 

  751. Right: magnitude of the correlation between each confound time series and the
    752. 

  752. mean global signal. Strong correlations might be indicative of partial volume
    753. 

  753. effects and can inform decisions about feature orthogonalization prior to
    754. 

  754. confound regression.
    755. 

  755. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-confoundcorr_bold.svg" style="width: 100%" />
    756. 

  756. </div>
    757. 

  757. <div class="elem-filename">
    758. 

  758.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-linebisection_desc-confoundcorr_bold.svg</a>
    759. 

  759. </div>
    760. 

  760. 

  761.         </div>
    762. 

  762.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-linebisection">
    763. 

  763. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    764. 

  764. black brain outline. Right hand side of each map: time series (top in seconds),
    765. 

  765. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    766. 

  766. in green; noise components in red.
    767. 

  767. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-aroma_bold.svg" style="width: 100%" />
    768. 

  768. </div>
    769. 

  769. <div class="elem-filename">
    770. 

  770.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-linebisection_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-linebisection_desc-aroma_bold.svg</a>
    771. 

  771. </div>
    772. 

  772. 

  773.         </div>
    774. 

  774.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-overtverbgeneration">
    775. 

  775. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtverbgeneration</span>.</h2>                    <details open>
    776. 

  776. 		<summary>Summary</summary>
    777. 

  777. 		<ul class="elem-desc">
    778. 

  778. 			<li>Repetition time (TR): 5s</li>
    779. 

  779. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    780. 

  780. 			<li>Single-echo EPI sequence.</li>
    781. 

  781. 			<li>Slice timing correction: Applied</li>
    782. 

  782. 			<li>Susceptibility distortion correction: None</li>
    783. 

  783. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    784. 

  784. 			<li>Non-steady-state volumes: 1</li>
    785. 

  785. 		</ul>
    786. 

  786. 		</details>
    787. 

  787. 		<details>
    788. 

  788. 			<summary>Confounds collected</summary><br />
    789. 

  789. 			<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_power2, global_signal_derivative1_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, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, aroma_motion_01, aroma_motion_03, aroma_motion_04, aroma_motion_05, aroma_motion_07, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_13, aroma_motion_14, aroma_motion_17, aroma_motion_20, aroma_motion_29, aroma_motion_30.</p>
    790. 

  790. 		</details>
    791. 

  791.         </div>
    792. 

  792.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-overtverbgeneration">
    793. 

  793. <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-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg">
    794. 

  794. Problem loading figure sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    795. 

  795. </div>
    796. 

  796. <div class="elem-filename">
    797. 

  797.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-bbregister_bold.svg</a>
    798. 

  798. </div>
    799. 

  799. 

  800.         </div>
    801. 

  801.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-overtverbgeneration">
    802. 

  802. <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-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-rois_bold.svg" style="width: 100%" />
    803. 

  803. </div>
    804. 

  804. <div class="elem-filename">
    805. 

  805.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-rois_bold.svg</a>
    806. 

  806. </div>
    807. 

  807. 

  808.         </div>
    809. 

  809.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-overtverbgeneration">
    810. 

  810. <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-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg" style="width: 100%" />
    811. 

  811. </div>
    812. 

  812. <div class="elem-filename">
    813. 

  813.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-compcorvar_bold.svg</a>
    814. 

  814. </div>
    815. 

  815. 

  816.         </div>
    817. 

  817.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-overtverbgeneration">
    818. 

  818. <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-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg" style="width: 100%" />
    819. 

  819. </div>
    820. 

  820. <div class="elem-filename">
    821. 

  821.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-carpetplot_bold.svg</a>
    822. 

  822. </div>
    823. 

  823. 

  824.         </div>
    825. 

  825.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-overtverbgeneration">
    826. 

  826. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    827. 

  827. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    828. 

  828. Right: magnitude of the correlation between each confound time series and the
    829. 

  829. mean global signal. Strong correlations might be indicative of partial volume
    830. 

  830. effects and can inform decisions about feature orthogonalization prior to
    831. 

  831. confound regression.
    832. 

  832. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg" style="width: 100%" />
    833. 

  833. </div>
    834. 

  834. <div class="elem-filename">
    835. 

  835.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-confoundcorr_bold.svg</a>
    836. 

  836. </div>
    837. 

  837. 

  838.         </div>
    839. 

  839.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-overtverbgeneration">
    840. 

  840. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    841. 

  841. black brain outline. Right hand side of each map: time series (top in seconds),
    842. 

  842. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    843. 

  843. in green; noise components in red.
    844. 

  844. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-aroma_bold.svg" style="width: 100%" />
    845. 

  845. </div>
    846. 

  846. <div class="elem-filename">
    847. 

  847.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtverbgeneration_desc-aroma_bold.svg</a>
    848. 

  848. </div>
    849. 

  849. 

  850.         </div>
    851. 

  851.         <div id="datatype-figures_desc-summary_session-test_suffix-bold_task-overtwordrepetition">
    852. 

  852. <h2 class="sub-report-group">Reports for: session <span class="bids-entity">test</span>, task <span class="bids-entity">overtwordrepetition</span>.</h2>                    <details open>
    853. 

  853. 		<summary>Summary</summary>
    854. 

  854. 		<ul class="elem-desc">
    855. 

  855. 			<li>Repetition time (TR): 5s</li>
    856. 

  856. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    857. 

  857. 			<li>Single-echo EPI sequence.</li>
    858. 

  858. 			<li>Slice timing correction: Applied</li>
    859. 

  859. 			<li>Susceptibility distortion correction: None</li>
    860. 

  860. 			<li>Registration: FreeSurfer <code>bbregister</code> (boundary-based registration, BBR) - 6 dof</li>
    861. 

  861. 			<li>Non-steady-state volumes: 1</li>
    862. 

  862. 		</ul>
    863. 

  863. 		</details>
    864. 

  864. 		<details>
    865. 

  865. 			<summary>Confounds collected</summary><br />
    866. 

  866. 			<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_power2, global_signal_derivative1_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, 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_derivative1_power2, trans_y_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_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, aroma_motion_08, aroma_motion_09, aroma_motion_10, aroma_motion_15, aroma_motion_16, aroma_motion_17, aroma_motion_23, aroma_motion_25.</p>
    867. 

  867. 		</details>
    868. 

  868.         </div>
    869. 

  869.         <div id="datatype-figures_desc-bbregister_session-test_suffix-bold_task-overtwordrepetition">
    870. 

  870. <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-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg">
    871. 

  871. Problem loading figure sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    872. 

  872. </div>
    873. 

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

  874.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-bbregister_bold.svg</a>
    875. 

  875. </div>
    876. 

  876. 

  877.         </div>
    878. 

  878.         <div id="datatype-figures_desc-rois_session-test_suffix-bold_task-overtwordrepetition">
    879. 

  879. <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-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-rois_bold.svg" style="width: 100%" />
    880. 

  880. </div>
    881. 

  881. <div class="elem-filename">
    882. 

  882.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-rois_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-rois_bold.svg</a>
    883. 

  883. </div>
    884. 

  884. 

  885.         </div>
    886. 

  886.         <div id="datatype-figures_desc-compcorvar_session-test_suffix-bold_task-overtwordrepetition">
    887. 

  887. <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-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg" style="width: 100%" />
    888. 

  888. </div>
    889. 

  889. <div class="elem-filename">
    890. 

  890.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-compcorvar_bold.svg</a>
    891. 

  891. </div>
    892. 

  892. 

  893.         </div>
    894. 

  894.         <div id="datatype-figures_desc-carpetplot_session-test_suffix-bold_task-overtwordrepetition">
    895. 

  895. <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-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg" style="width: 100%" />
    896. 

  896. </div>
    897. 

  897. <div class="elem-filename">
    898. 

  898.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-carpetplot_bold.svg</a>
    899. 

  899. </div>
    900. 

  900. 

  901.         </div>
    902. 

  902.         <div id="datatype-figures_desc-confoundcorr_session-test_suffix-bold_task-overtwordrepetition">
    903. 

  903. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    904. 

  904. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    905. 

  905. Right: magnitude of the correlation between each confound time series and the
    906. 

  906. mean global signal. Strong correlations might be indicative of partial volume
    907. 

  907. effects and can inform decisions about feature orthogonalization prior to
    908. 

  908. confound regression.
    909. 

  909. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg" style="width: 100%" />
    910. 

  910. </div>
    911. 

  911. <div class="elem-filename">
    912. 

  912.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-confoundcorr_bold.svg</a>
    913. 

  913. </div>
    914. 

  914. 

  915.         </div>
    916. 

  916.         <div id="datatype-figures_desc-aroma_session-test_suffix-bold_task-overtwordrepetition">
    917. 

  917. <h3 class="run-title">ICA Components classified by AROMA</h3><p class="elem-caption">Maps created with maximum intensity projection (glass brain) with a
    918. 

  918. black brain outline. Right hand side of each map: time series (top in seconds),
    919. 

  919. frequency spectrum (bottom in Hertz). Components classified as signal are plotted
    920. 

  920. in green; noise components in red.
    921. 

  921. </p>                    <img class="svg-reportlet" src="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-aroma_bold.svg" style="width: 100%" />
    922. 

  922. </div>
    923. 

  923. <div class="elem-filename">
    924. 

  924.     Get figure file: <a href="./sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-aroma_bold.svg" target="_blank">sub-02/figures/sub-02_ses-test_task-overtwordrepetition_desc-aroma_bold.svg</a>
    925. 

  925. </div>
    926. 

  926. 

  927.         </div>
    928. 

  928.     </div>
    929. 

  929.     <div id="About">
    930. 

  930.     <h1 class="sub-report-title">About</h1>
    931. 

  931.         <div id="datatype-figures_desc-about_suffix-T1w">
    932. 

  932.                     <ul>
    933. 

  933. 		<li>fMRIPrep version: 20.1.1+79.g1a72777b</li>
    934. 

  934. 		<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 02 -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>
    935. 

  935. 		<li>Date preprocessed: 2020-07-24 23:39:52 -0700</li>
    936. 

  936. 	</ul>
    937. 

  937. </div>
    938. 

  938.         </div>
    939. 

  939.     </div>
    940. 

  940. 

  941. <div id="boilerplate">
    942. 

  942.     <h1 class="sub-report-title">Methods</h1>
    943. 

  943.     <p>We kindly ask to report results preprocessed with this tool using the following
    944. 

  944.        boilerplate.</p>
    945. 

  945.     <ul class="nav nav-tabs" id="myTab" role="tablist">
    946. 

  946.         <li class="nav-item">
    947. 

  947.             <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
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    949. 

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    950. 

  950.             <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
    951. 

  951.         </li>
    952. 

  952.         <li class="nav-item">
    953. 

  953.             <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
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  956.     <div class="tab-content" id="myTabContent">
    957. 

  957.       <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>
    958. 

  958. <dl>
    959. 

  959. <dt>Anatomical data preprocessing</dt>
    960. 

  960. <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>
    961. 

  961. </dd>
    962. 

  962. <dt>Functional data preprocessing</dt>
    963. 

  963. <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>
    964. 

  964. </dd>
    965. 

  965. </dl>
    966. 

  966. <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>
    967. 

  967. <h3 id="copyright-waiver">Copyright Waiver</h3>
    968. 

  968. <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>
    969. 

  969. <h3 id="references" class="unnumbered">References</h3>
    970. 

  970. <div id="refs" class="references">
    971. 

  971. <div id="ref-nilearn">
    972. 

  972. <p>Abraham, Alexandre, Fabian Pedregosa, Michael Eickenberg, Philippe Gervais, Andreas Mueller, Jean Kossaifi, Alexandre Gramfort, Bertrand Thirion, and Gael Varoquaux. 2014. “Machine Learning for Neuroimaging with Scikit-Learn.” <em>Frontiers in Neuroinformatics</em> 8. <a href="https://doi.org/10.3389/fninf.2014.00014" class="uri">https://doi.org/10.3389/fninf.2014.00014</a>.</p>
    973. 

  973. </div>
    974. 

  974. <div id="ref-ants">
    975. 

  975. <p>Avants, B.B., C.L. Epstein, M. Grossman, and J.C. Gee. 2008. “Symmetric Diffeomorphic Image Registration with Cross-Correlation: Evaluating Automated Labeling of Elderly and Neurodegenerative Brain.” <em>Medical Image Analysis</em> 12 (1): 26–41. <a href="https://doi.org/10.1016/j.media.2007.06.004" class="uri">https://doi.org/10.1016/j.media.2007.06.004</a>.</p>
    976. 

  976. </div>
    977. 

  977. <div id="ref-compcor">
    978. 

  978. <p>Behzadi, Yashar, Khaled Restom, Joy Liau, and Thomas T. Liu. 2007. “A Component Based Noise Correction Method (CompCor) for BOLD and Perfusion Based fMRI.” <em>NeuroImage</em> 37 (1): 90–101. <a href="https://doi.org/10.1016/j.neuroimage.2007.04.042" class="uri">https://doi.org/10.1016/j.neuroimage.2007.04.042</a>.</p>
    979. 

  979. </div>
    980. 

  980. <div id="ref-afni">
    981. 

  981. <p>Cox, Robert W., and James S. Hyde. 1997. “Software Tools for Analysis and Visualization of fMRI Data.” <em>NMR in Biomedicine</em> 10 (4-5): 171–78. <a href="https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5&lt;171::AID-NBM453&gt;3.0.CO;2-L" class="uri">https://doi.org/10.1002/(SICI)1099-1492(199706/08)10:4/5&lt;171::AID-NBM453&gt;3.0.CO;2-L</a>.</p>
    982. 

  982. </div>
    983. 

  983. <div id="ref-fs_reconall">
    984. 

  984. <p>Dale, Anders M., Bruce Fischl, and Martin I. Sereno. 1999. “Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction.” <em>NeuroImage</em> 9 (2): 179–94. <a href="https://doi.org/10.1006/nimg.1998.0395" class="uri">https://doi.org/10.1006/nimg.1998.0395</a>.</p>
    985. 

  985. </div>
    986. 

  986. <div id="ref-fmriprep2">
    987. 

  987. <p>Esteban, Oscar, Ross Blair, Christopher J. Markiewicz, Shoshana L. Berleant, Craig Moodie, Feilong Ma, Ayse Ilkay Isik, et al. 2018. “FMRIPrep.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.852659" class="uri">https://doi.org/10.5281/zenodo.852659</a>.</p>
    988. 

  988. </div>
    989. 

  989. <div id="ref-fmriprep1">
    990. 

  990. <p>Esteban, Oscar, Christopher Markiewicz, Ross W Blair, Craig Moodie, Ayse Ilkay Isik, Asier Erramuzpe Aliaga, James Kent, et al. 2018. “fMRIPrep: A Robust Preprocessing Pipeline for Functional MRI.” <em>Nature Methods</em>. <a href="https://doi.org/10.1038/s41592-018-0235-4" class="uri">https://doi.org/10.1038/s41592-018-0235-4</a>.</p>
    991. 

  991. </div>
    992. 

  992. <div id="ref-mni152nlin6asym">
    993. 

  993. <p>Evans, AC, AL Janke, DL Collins, and S Baillet. 2012. “Brain Templates and Atlases.” <em>NeuroImage</em> 62 (2): 911–22. <a href="https://doi.org/10.1016/j.neuroimage.2012.01.024" class="uri">https://doi.org/10.1016/j.neuroimage.2012.01.024</a>.</p>
    994. 

  994. </div>
    995. 

  995. <div id="ref-mni152nlin2009casym">
    996. 

  996. <p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
    997. 

  997. </div>
    998. 

  998. <div id="ref-hcppipelines">
    999. 

  999. <p>Glasser, Matthew F., Stamatios N. Sotiropoulos, J. Anthony Wilson, Timothy S. Coalson, Bruce Fischl, Jesper L. Andersson, Junqian Xu, et al. 2013. “The Minimal Preprocessing Pipelines for the Human Connectome Project.” <em>NeuroImage</em>, Mapping the connectome, 80: 105–24. <a href="https://doi.org/10.1016/j.neuroimage.2013.04.127" class="uri">https://doi.org/10.1016/j.neuroimage.2013.04.127</a>.</p>
    1000. 

  1000. </div>
    1001. 

  1001. <div id="ref-nipype1">
    1002. 

  1002. <p>Gorgolewski, K., C. D. Burns, C. Madison, D. Clark, Y. O. Halchenko, M. L. Waskom, and S. Ghosh. 2011. “Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python.” <em>Frontiers in Neuroinformatics</em> 5: 13. <a href="https://doi.org/10.3389/fninf.2011.00013" class="uri">https://doi.org/10.3389/fninf.2011.00013</a>.</p>
    1003. 

  1003. </div>
    1004. 

  1004. <div id="ref-nipype2">
    1005. 

  1005. <p>Gorgolewski, Krzysztof J., Oscar Esteban, Christopher J. Markiewicz, Erik Ziegler, David Gage Ellis, Michael Philipp Notter, Dorota Jarecka, et al. 2018. “Nipype.” <em>Software</em>. Zenodo. <a href="https://doi.org/10.5281/zenodo.596855" class="uri">https://doi.org/10.5281/zenodo.596855</a>.</p>
    1006. 

  1006. </div>
    1007. 

  1007. <div id="ref-bbr">
    1008. 

  1008. <p>Greve, Douglas N, and Bruce Fischl. 2009. “Accurate and Robust Brain Image Alignment Using Boundary-Based Registration.” <em>NeuroImage</em> 48 (1): 63–72. <a href="https://doi.org/10.1016/j.neuroimage.2009.06.060" class="uri">https://doi.org/10.1016/j.neuroimage.2009.06.060</a>.</p>
    1009. 

  1009. </div>
    1010. 

  1010. <div id="ref-mcflirt">
    1011. 

  1011. <p>Jenkinson, Mark, Peter Bannister, Michael Brady, and Stephen Smith. 2002. “Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images.” <em>NeuroImage</em> 17 (2): 825–41. <a href="https://doi.org/10.1006/nimg.2002.1132" class="uri">https://doi.org/10.1006/nimg.2002.1132</a>.</p>
    1012. 

  1012. </div>
    1013. 

  1013. <div id="ref-mindboggle">
    1014. 

  1014. <p>Klein, Arno, Satrajit S. Ghosh, Forrest S. Bao, Joachim Giard, Yrjö Häme, Eliezer Stavsky, Noah Lee, et al. 2017. “Mindboggling Morphometry of Human Brains.” <em>PLOS Computational Biology</em> 13 (2): e1005350. <a href="https://doi.org/10.1371/journal.pcbi.1005350" class="uri">https://doi.org/10.1371/journal.pcbi.1005350</a>.</p>
    1015. 

  1015. </div>
    1016. 

  1016. <div id="ref-lanczos">
    1017. 

  1017. <p>Lanczos, C. 1964. “Evaluation of Noisy Data.” <em>Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis</em> 1 (1): 76–85. <a href="https://doi.org/10.1137/0701007" class="uri">https://doi.org/10.1137/0701007</a>.</p>
    1018. 

  1018. </div>
    1019. 

  1019. <div id="ref-power_fd_dvars">
    1020. 

  1020. <p>Power, Jonathan D., Anish Mitra, Timothy O. Laumann, Abraham Z. Snyder, Bradley L. Schlaggar, and Steven E. Petersen. 2014. “Methods to Detect, Characterize, and Remove Motion Artifact in Resting State fMRI.” <em>NeuroImage</em> 84 (Supplement C): 320–41. <a href="https://doi.org/10.1016/j.neuroimage.2013.08.048" class="uri">https://doi.org/10.1016/j.neuroimage.2013.08.048</a>.</p>
    1021. 

  1021. </div>
    1022. 

  1022. <div id="ref-aroma">
    1023. 

  1023. <p>Pruim, Raimon H. R., Maarten Mennes, Daan van Rooij, Alberto Llera, Jan K. Buitelaar, and Christian F. Beckmann. 2015. “ICA-AROMA: A Robust ICA-Based Strategy for Removing Motion Artifacts from fMRI Data.” <em>NeuroImage</em> 112 (Supplement C): 267–77. <a href="https://doi.org/10.1016/j.neuroimage.2015.02.064" class="uri">https://doi.org/10.1016/j.neuroimage.2015.02.064</a>.</p>
    1024. 

  1024. </div>
    1025. 

  1025. <div id="ref-fs_template">
    1026. 

  1026. <p>Reuter, Martin, Herminia Diana Rosas, and Bruce Fischl. 2010. “Highly Accurate Inverse Consistent Registration: A Robust Approach.” <em>NeuroImage</em> 53 (4): 1181–96. <a href="https://doi.org/10.1016/j.neuroimage.2010.07.020" class="uri">https://doi.org/10.1016/j.neuroimage.2010.07.020</a>.</p>
    1027. 

  1027. </div>
    1028. 

  1028. <div id="ref-confounds_satterthwaite_2013">
    1029. 

  1029. <p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
    1030. 

  1030. </div>
    1031. 

  1031. <div id="ref-n4">
    1032. 

  1032. <p>Tustison, N. J., B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee. 2010. “N4ITK: Improved N3 Bias Correction.” <em>IEEE Transactions on Medical Imaging</em> 29 (6): 1310–20. <a href="https://doi.org/10.1109/TMI.2010.2046908" class="uri">https://doi.org/10.1109/TMI.2010.2046908</a>.</p>
    1033. 

  1033. </div>
    1034. 

  1034. <div id="ref-fsl_fast">
    1035. 

  1035. <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>
    1036. 

  1036. </div>
    1037. 

  1037. </div></div></div>
    1038. 

  1038.       <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
    1039. 

  1039. Results included in this manuscript come from preprocessing
    1040. 

  1040. performed using *fMRIPrep* 20.1.1+79.g1a72777b
    1041. 

  1041. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    1042. 

  1042. which is based on *Nipype* 1.5.0
    1043. 

  1043. (@nipype1; @nipype2; RRID:SCR_002502).
    1044. 

  1044. 

  1045. Anatomical data preprocessing
    1046. 

  1046. 

  1047. : A total of 2 T1-weighted (T1w) images were found within the input
    1048. 

  1048. BIDS dataset.
    1049. 

  1049. All of them were corrected for intensity non-uniformity (INU)
    1050. 

  1050. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757].
    1051. 

  1051. The T1w-reference was then skull-stripped with a *Nipype* implementation of
    1052. 

  1052. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
    1053. 

  1053. as target template.
    1054. 

  1054. Brain tissue segmentation of cerebrospinal fluid (CSF),
    1055. 

  1055. white-matter (WM) and gray-matter (GM) was performed on
    1056. 

  1056. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
    1057. 

  1057. @fsl_fast].
    1058. 

  1058. A T1w-reference map was computed after registration of
    1059. 

  1059. 2 T1w images (after INU-correction) using
    1060. 

  1060. `mri_robust_template` [FreeSurfer 6.0.1, @fs_template].
    1061. 

  1061. Brain surfaces were reconstructed using `recon-all` [FreeSurfer 6.0.1,
    1062. 

  1062. RRID:SCR_001847, @fs_reconall], and the brain mask estimated
    1063. 

  1063. previously was refined with a custom variation of the method to reconcile
    1064. 

  1064. ANTs-derived and FreeSurfer-derived segmentations of the cortical
    1065. 

  1065. gray-matter of Mindboggle [RRID:SCR_002438, @mindboggle].
    1066. 

  1066. Volume-based spatial normalization to two standard spaces (MNI152NLin2009cAsym, MNI152NLin6Asym) was performed through
    1067. 

  1067. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
    1068. 

  1068. using brain-extracted versions of both T1w reference and the T1w template.
    1069. 

  1069. The following templates were selected for spatial normalization:
    1070. 

  1070. *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], 
    1071. 

  1071. 

  1072. Functional data preprocessing
    1073. 

  1073. 

  1074. : For each of the 10 BOLD runs found per subject (across all
    1075. 

  1075. tasks and sessions), the following preprocessing was performed.
    1076. 

  1076. First, a reference volume and its skull-stripped version were generated
    1077. 

  1077.  using a custom
    1078. 

  1078. methodology of *fMRIPrep*.
    1079. 

  1079. Susceptibility distortion correction (SDC) was omitted.
    1080. 

  1080. The BOLD reference was then co-registered to the T1w reference using
    1081. 

  1081. `bbregister` (FreeSurfer) which implements boundary-based registration [@bbr].
    1082. 

  1082. Co-registration was configured with six degrees of freedom.
    1083. 

  1083. Head-motion parameters with respect to the BOLD reference
    1084. 

  1084. (transformation matrices, and six corresponding rotation and translation
    1085. 

  1085. parameters) are estimated before any spatiotemporal filtering using
    1086. 

  1086. `mcflirt` [FSL 5.0.9, @mcflirt].
    1087. 

  1087. BOLD runs were slice-time corrected using `3dTshift` from
    1088. 

  1088. AFNI 20160207 [@afni, RRID:SCR_005927].
    1089. 

  1089. The BOLD time-series were resampled onto the following surfaces
    1090. 

  1090. (FreeSurfer reconstruction nomenclature):
    1091. 

  1091. *fsaverage*.
    1092. 

  1092. The BOLD time-series (including slice-timing correction when applied)
    1093. 

  1093. were resampled onto their original, native space by applying
    1094. 

  1094. the transforms to correct for head-motion.
    1095. 

  1095. These resampled BOLD time-series will be referred to as *preprocessed
    1096. 

  1096. BOLD in original space*, or just *preprocessed BOLD*.
    1097. 

  1097. *Grayordinates* files [@hcppipelines] containing 91k samples were also
    1098. 

  1098. generated using the highest-resolution ``fsaverage`` as intermediate standardized
    1099. 

  1099. surface space.
    1100. 

  1100. Automatic removal of motion artifacts using independent component analysis
    1101. 

  1101. [ICA-AROMA, @aroma] was performed on the *preprocessed BOLD on MNI space*
    1102. 

  1102. time-series after removal of non-steady state volumes and spatial smoothing
    1103. 

  1103. with an isotropic, Gaussian kernel of 6mm FWHM (full-width half-maximum).
    1104. 

  1104. Corresponding "non-aggresively" denoised runs were produced after such
    1105. 

  1105. smoothing.
    1106. 

  1106. Additionally, the "aggressive" noise-regressors were collected and placed
    1107. 

  1107. in the corresponding confounds file.
    1108. 

  1108. Several confounding time-series were calculated based on the
    1109. 

  1109. *preprocessed BOLD*: framewise displacement (FD), DVARS and
    1110. 

  1110. three region-wise global signals.
    1111. 

  1111. FD was computed using two formulations following Power (absolute sum of
    1112. 

  1112. relative motions, @power_fd_dvars) and Jenkinson (relative root mean square
    1113. 

  1113. displacement between affines, @mcflirt).
    1114. 

  1114. FD and DVARS are calculated for each functional run, both using their
    1115. 

  1115. implementations in *Nipype* [following the definitions by @power_fd_dvars].
    1116. 

  1116. The three global signals are extracted within the CSF, the WM, and
    1117. 

  1117. the whole-brain masks.
    1118. 

  1118. Additionally, a set of physiological regressors were extracted to
    1119. 

  1119. allow for component-based noise correction [*CompCor*, @compcor].
    1120. 

  1120. Principal components are estimated after high-pass filtering the
    1121. 

  1121. *preprocessed BOLD* time-series (using a discrete cosine filter with
    1122. 

  1122. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    1123. 

  1123. and anatomical (aCompCor).
    1124. 

  1124. tCompCor components are then calculated from the top 2% variable
    1125. 

  1125. voxels within the brain mask.
    1126. 

  1126. For aCompCor, three probabilistic masks (CSF, WM and combined CSF+WM)
    1127. 

  1127. are generated in anatomical space.
    1128. 

  1128. The implementation differs from that of Behzadi et al. in that instead
    1129. 

  1129. of eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
    1130. 

  1130. subtracted a mask of pixels that likely contain a volume fraction of GM.
    1131. 

  1131. This mask is obtained by dilating a GM mask extracted from the FreeSurfer's *aseg* segmentation, and it ensures components are not extracted
    1132. 

  1132. from voxels containing a minimal fraction of GM.
    1133. 

  1133. Finally, these masks are resampled into BOLD space and binarized by
    1134. 

  1134. thresholding at 0.99 (as in the original implementation).
    1135. 

  1135. Components are also calculated separately within the WM and CSF masks.
    1136. 

  1136. For each CompCor decomposition, the *k* components with the largest singular
    1137. 

  1137. values are retained, such that the retained components' time series are
    1138. 

  1138. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
    1139. 

  1139. WM, combined, or temporal). The remaining components are dropped from
    1140. 

  1140. consideration.
    1141. 

  1141. The head-motion estimates calculated in the correction step were also
    1142. 

  1142. placed within the corresponding confounds file.
    1143. 

  1143. The confound time series derived from head motion estimates and global
    1144. 

  1144. signals were expanded with the inclusion of temporal derivatives and
    1145. 

  1145. quadratic terms for each [@confounds_satterthwaite_2013].
    1146. 

  1146. Frames that exceeded a threshold of 0.5 mm FD or
    1147. 

  1147. 1.5 standardised DVARS were annotated as motion outliers.
    1148. 

  1148. All resamplings can be performed with *a single interpolation
    1149. 

  1149. step* by composing all the pertinent transformations (i.e. head-motion
    1150. 

  1150. transform matrices, susceptibility distortion correction when available,
    1151. 

  1151. and co-registrations to anatomical and output spaces).
    1152. 

  1152. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
    1153. 

  1153. configured with Lanczos interpolation to minimize the smoothing
    1154. 

  1154. effects of other kernels [@lanczos].
    1155. 

  1155. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
    1156. 

  1156. (FreeSurfer).
    1157. 

  1157. 

  1158. 

  1159. Many internal operations of *fMRIPrep* use
    1160. 

  1160. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
    1161. 

  1161. mostly within the functional processing workflow.
    1162. 

  1162. For more details of the pipeline, see [the section corresponding
    1163. 

  1163. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
    1164. 

  1164. 

  1165. 

  1166. ### Copyright Waiver
    1167. 

  1167. 

  1168. The above boilerplate text was automatically generated by fMRIPrep
    1169. 

  1169. with the express intention that users should copy and paste this
    1170. 

  1170. text into their manuscripts *unchanged*.
    1171. 

  1171. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
    1172. 

  1172. 

  1173. ### References
    1174. 

  1174. 

  1175. </pre>
    1176. 

  1176. </div>
    1177. 

  1177.       <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
    1178. 

  1178. using \emph{fMRIPrep} 20.1.1+79.g1a72777b (\citet{fmriprep1};
    1179. 

  1179. \citet{fmriprep2}; RRID:SCR\_016216), which is based on \emph{Nipype}
    1180. 

  1180. 1.5.0 (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
    1181. 

  1181. 

  1182. \begin{description}
    1183. 

  1183. \item[Anatomical data preprocessing]
    1184. 

  1184. A total of 2 T1-weighted (T1w) images were found within the input BIDS
    1185. 

  1185. dataset. All of them were corrected for intensity non-uniformity (INU)
    1186. 

  1186. with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with ANTs
    1187. 

  1187. 2.2.0 \citep[RRID:SCR\_004757]{ants}. The T1w-reference was then
    1188. 

  1188. skull-stripped with a \emph{Nipype} implementation of the
    1189. 

  1189. \texttt{antsBrainExtraction.sh} workflow (from ANTs), using OASIS30ANTs
    1190. 

  1190. as target template. Brain tissue segmentation of cerebrospinal fluid
    1191. 

  1191. (CSF), white-matter (WM) and gray-matter (GM) was performed on the
    1192. 

  1192. brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
    1193. 

  1193. RRID:SCR\_002823,][]{fsl_fast}. A T1w-reference map was computed after
    1194. 

  1194. registration of 2 T1w images (after INU-correction) using
    1195. 

  1195. \texttt{mri\_robust\_template} \citep[FreeSurfer 6.0.1,][]{fs_template}.
    1196. 

  1196. Brain surfaces were reconstructed using \texttt{recon-all}
    1197. 

  1197. \citep[FreeSurfer 6.0.1, RRID:SCR\_001847,][]{fs_reconall}, and the
    1198. 

  1198. brain mask estimated previously was refined with a custom variation of
    1199. 

  1199. the method to reconcile ANTs-derived and FreeSurfer-derived
    1200. 

  1200. segmentations of the cortical gray-matter of Mindboggle
    1201. 

  1201. \citep[RRID:SCR\_002438,][]{mindboggle}. Volume-based spatial
    1202. 

  1202. normalization to two standard spaces (MNI152NLin2009cAsym,
    1203. 

  1203. MNI152NLin6Asym) was performed through nonlinear registration with
    1204. 

  1204. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
    1205. 

  1205. of both T1w reference and the T1w template. The following templates were
    1206. 

  1206. selected for spatial normalization: \emph{ICBM 152 Nonlinear
    1207. 

  1207. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
    1208. 

  1208. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]}, \emph{FSL's
    1209. 

  1209. MNI ICBM 152 non-linear 6th Generation Asymmetric Average Brain
    1210. 

  1210. Stereotaxic Registration Model} {[}\citet{mni152nlin6asym},
    1211. 

  1211. RRID:SCR\_002823; TemplateFlow ID: MNI152NLin6Asym{]},
    1212. 

  1212. \item[Functional data preprocessing]
    1213. 

  1213. For each of the 10 BOLD runs found per subject (across all tasks and
    1214. 

  1214. sessions), the following preprocessing was performed. First, a reference
    1215. 

  1215. volume and its skull-stripped version were generated using a custom
    1216. 

  1216. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
    1217. 

  1217. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
    1218. 

  1218. reference using \texttt{bbregister} (FreeSurfer) which implements
    1219. 

  1219. boundary-based registration \citep{bbr}. Co-registration was configured
    1220. 

  1220. with six degrees of freedom. Head-motion parameters with respect to the
    1221. 

  1221. BOLD reference (transformation matrices, and six corresponding rotation
    1222. 

  1222. and translation parameters) are estimated before any spatiotemporal
    1223. 

  1223. filtering using \texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. BOLD
    1224. 

  1224. runs were slice-time corrected using \texttt{3dTshift} from AFNI
    1225. 

  1225. 20160207 \citep[RRID:SCR\_005927]{afni}. The BOLD time-series were
    1226. 

  1226. resampled onto the following surfaces (FreeSurfer reconstruction
    1227. 

  1227. nomenclature): \emph{fsaverage}. The BOLD time-series (including
    1228. 

  1228. slice-timing correction when applied) were resampled onto their
    1229. 

  1229. original, native space by applying the transforms to correct for
    1230. 

  1230. head-motion. These resampled BOLD time-series will be referred to as
    1231. 

  1231. \emph{preprocessed BOLD in original space}, or just \emph{preprocessed
    1232. 

  1232. BOLD}. \emph{Grayordinates} files \citep{hcppipelines} containing 91k
    1233. 

  1233. samples were also generated using the highest-resolution
    1234. 

  1234. \texttt{fsaverage} as intermediate standardized surface space. Automatic
    1235. 

  1235. removal of motion artifacts using independent component analysis
    1236. 

  1236. \citep[ICA-AROMA,][]{aroma} was performed on the \emph{preprocessed BOLD
    1237. 

  1237. on MNI space} time-series after removal of non-steady state volumes and
    1238. 

  1238. spatial smoothing with an isotropic, Gaussian kernel of 6mm FWHM
    1239. 

  1239. (full-width half-maximum). Corresponding ``non-aggresively'' denoised
    1240. 

  1240. runs were produced after such smoothing. Additionally, the
    1241. 

  1241. ``aggressive'' noise-regressors were collected and placed in the
    1242. 

  1242. corresponding confounds file. Several confounding time-series were
    1243. 

  1243. calculated based on the \emph{preprocessed BOLD}: framewise displacement
    1244. 

  1244. (FD), DVARS and three region-wise global signals. FD was computed using
    1245. 

  1245. two formulations following Power (absolute sum of relative motions,
    1246. 

  1246. \citet{power_fd_dvars}) and Jenkinson (relative root mean square
    1247. 

  1247. displacement between affines, \citet{mcflirt}). FD and DVARS are
    1248. 

  1248. calculated for each functional run, both using their implementations in
    1249. 

  1249. \emph{Nipype} \citep[following the definitions by][]{power_fd_dvars}.
    1250. 

  1250. The three global signals are extracted within the CSF, the WM, and the
    1251. 

  1251. whole-brain masks. Additionally, a set of physiological regressors were
    1252. 

  1252. extracted to allow for component-based noise correction
    1253. 

  1253. \citep[\emph{CompCor},][]{compcor}. Principal components are estimated
    1254. 

  1254. after high-pass filtering the \emph{preprocessed BOLD} time-series
    1255. 

  1255. (using a discrete cosine filter with 128s cut-off) for the two
    1256. 

  1256. \emph{CompCor} variants: temporal (tCompCor) and anatomical (aCompCor).
    1257. 

  1257. tCompCor components are then calculated from the top 2\% variable voxels
    1258. 

  1258. within the brain mask. For aCompCor, three probabilistic masks (CSF, WM
    1259. 

  1259. and combined CSF+WM) are generated in anatomical space. The
    1260. 

  1260. implementation differs from that of Behzadi et al.~in that instead of
    1261. 

  1261. eroding the masks by 2 pixels on BOLD space, the aCompCor masks are
    1262. 

  1262. subtracted a mask of pixels that likely contain a volume fraction of GM.
    1263. 

  1263. This mask is obtained by dilating a GM mask extracted from the
    1264. 

  1264. FreeSurfer's \emph{aseg} segmentation, and it ensures components are not
    1265. 

  1265. extracted from voxels containing a minimal fraction of GM. Finally,
    1266. 

  1266. these masks are resampled into BOLD space and binarized by thresholding
    1267. 

  1267. at 0.99 (as in the original implementation). Components are also
    1268. 

  1268. calculated separately within the WM and CSF masks. For each CompCor
    1269. 

  1269. decomposition, the \emph{k} components with the largest singular values
    1270. 

  1270. are retained, such that the retained components' time series are
    1271. 

  1271. sufficient to explain 50 percent of variance across the nuisance mask
    1272. 

  1272. (CSF, WM, combined, or temporal). The remaining components are dropped
    1273. 

  1273. from consideration. The head-motion estimates calculated in the
    1274. 

  1274. correction step were also placed within the corresponding confounds
    1275. 

  1275. file. The confound time series derived from head motion estimates and
    1276. 

  1276. global signals were expanded with the inclusion of temporal derivatives
    1277. 

  1277. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
    1278. 

  1278. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    1279. 

  1279. were annotated as motion outliers. All resamplings can be performed with
    1280. 

  1280. \emph{a single interpolation step} by composing all the pertinent
    1281. 

  1281. transformations (i.e.~head-motion transform matrices, susceptibility
    1282. 

  1282. distortion correction when available, and co-registrations to anatomical
    1283. 

  1283. and output spaces). Gridded (volumetric) resamplings were performed
    1284. 

  1284. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
    1285. 

  1285. interpolation to minimize the smoothing effects of other kernels
    1286. 

  1286. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
    1287. 

  1287. \texttt{mri\_vol2surf} (FreeSurfer).
    1288. 

  1288. \end{description}
    1289. 

  1289. 

  1290. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
    1291. 

  1291. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
    1292. 

  1292. processing workflow. For more details of the pipeline, see
    1293. 

  1293. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
    1294. 

  1294. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
    1295. 

  1295. 

  1296. \hypertarget{copyright-waiver}{%
    1297. 

  1297. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
    1298. 

  1298. 

  1299. The above boilerplate text was automatically generated by fMRIPrep with
    1300. 

  1300. the express intention that users should copy and paste this text into
    1301. 

  1301. their manuscripts \emph{unchanged}. It is released under the
    1302. 

  1302. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
    1303. 

  1303. 

  1304. \hypertarget{references}{%
    1305. 

  1305. \subsubsection{References}\label{references}}
    1306. 

  1306. 

  1307. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
    1308. 

  1308. <h3>Bibliography</h3>
    1309. 

  1309. <pre>@article{fmriprep1,
    1310. 

  1310.     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},
    1311. 

  1311.     title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
    1312. 

  1312.     year = {2018},
    1313. 

  1313.     doi = {10.1038/s41592-018-0235-4},
    1314. 

  1314.     journal = {Nature Methods}
    1315. 

  1315. }
    1316. 

  1316. 

  1317. @article{fmriprep2,
    1318. 

  1318.     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.},
    1319. 

  1319.     title = {fMRIPrep},
    1320. 

  1320.     year = 2018,
    1321. 

  1321.     doi = {10.5281/zenodo.852659},
    1322. 

  1322.     publisher = {Zenodo},
    1323. 

  1323.     journal = {Software}
    1324. 

  1324. }
    1325. 

  1325. 

  1326. @article{nipype1,
    1327. 

  1327.     author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
    1328. 

  1328.     doi = {10.3389/fninf.2011.00013},
    1329. 

  1329.     journal = {Frontiers in Neuroinformatics},
    1330. 

  1330.     pages = 13,
    1331. 

  1331.     shorttitle = {Nipype},
    1332. 

  1332.     title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
    1333. 

  1333.     volume = 5,
    1334. 

  1334.     year = 2011
    1335. 

  1335. }
    1336. 

  1336. 

  1337. @article{nipype2,
    1338. 

  1338.     author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
    1339. 

  1339.     title = {Nipype},
    1340. 

  1340.     year = 2018,
    1341. 

  1341.     doi = {10.5281/zenodo.596855},
    1342. 

  1342.     publisher = {Zenodo},
    1343. 

  1343.     journal = {Software}
    1344. 

  1344. }
    1345. 

  1345. 

  1346. @article{n4,
    1347. 

  1347.     author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
    1348. 

  1348.     doi = {10.1109/TMI.2010.2046908},
    1349. 

  1349.     issn = {0278-0062},
    1350. 

  1350.     journal = {IEEE Transactions on Medical Imaging},
    1351. 

  1351.     number = 6,
    1352. 

  1352.     pages = {1310-1320},
    1353. 

  1353.     shorttitle = {N4ITK},
    1354. 

  1354.     title = {N4ITK: Improved N3 Bias Correction},
    1355. 

  1355.     volume = 29,
    1356. 

  1356.     year = 2010
    1357. 

  1357. }
    1358. 

  1358. 

  1359. @article{fs_reconall,
    1360. 

  1360.     author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
    1361. 

  1361.     doi = {10.1006/nimg.1998.0395},
    1362. 

  1362.     issn = {1053-8119},
    1363. 

  1363.     journal = {NeuroImage},
    1364. 

  1364.     number = 2,
    1365. 

  1365.     pages = {179-194},
    1366. 

  1366.     shorttitle = {Cortical Surface-Based Analysis},
    1367. 

  1367.     title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
    1368. 

  1368.     url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
    1369. 

  1369.     volume = 9,
    1370. 

  1370.     year = 1999
    1371. 

  1371. }
    1372. 

  1372. 

  1373. 

  1374. 

  1375. @article{mindboggle,
    1376. 

  1376.     author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
    1377. 

  1377.     doi = {10.1371/journal.pcbi.1005350},
    1378. 

  1378.     issn = {1553-7358},
    1379. 

  1379.     journal = {PLOS Computational Biology},
    1380. 

  1380.     number = 2,
    1381. 

  1381.     pages = {e1005350},
    1382. 

  1382.     title = {Mindboggling morphometry of human brains},
    1383. 

  1383.     url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
    1384. 

  1384.     volume = 13,
    1385. 

  1385.     year = 2017
    1386. 

  1386. }
    1387. 

  1387. 

  1388. @article{mni152lin,
    1389. 

  1389.     title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
    1390. 

  1390.     author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
    1391. 

  1391.     volume = {2},
    1392. 

  1392.     issn = {1053-8119},
    1393. 

  1393.     shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
    1394. 

  1394.     doi = {10.1006/nimg.1995.1012},
    1395. 

  1395.     number = {2, Part A},
    1396. 

  1396.     journal = {NeuroImage},
    1397. 

  1397.     year = {1995},
    1398. 

  1398.     pages = {89--101}
    1399. 

  1399. }
    1400. 

  1400. 

  1401. @article{mni152nlin2009casym,
    1402. 

  1402.     title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
    1403. 

  1403.     author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
    1404. 

  1404.     doi = {10.1016/S1053-8119(09)70884-5},
    1405. 

  1405.     journal = {NeuroImage},
    1406. 

  1406.     pages = {S102},
    1407. 

  1407.     volume = {47, Supplement 1},
    1408. 

  1408.     year = 2009
    1409. 

  1409. }
    1410. 

  1410. 

  1411. @article{mni152nlin6asym,
    1412. 

  1412.     author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
    1413. 

  1413.     title = {Brain templates and atlases},
    1414. 

  1414.     doi = {10.1016/j.neuroimage.2012.01.024},
    1415. 

  1415.     journal = {NeuroImage},
    1416. 

  1416.     volume = {62},
    1417. 

  1417.     number = {2},
    1418. 

  1418.     pages = {911--922},
    1419. 

  1419.     year = 2012
    1420. 

  1420. }
    1421. 

  1421. 

  1422. @article{ants,
    1423. 

  1423.     author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
    1424. 

  1424.     doi = {10.1016/j.media.2007.06.004},
    1425. 

  1425.     issn = {1361-8415},
    1426. 

  1426.     journal = {Medical Image Analysis},
    1427. 

  1427.     number = 1,
    1428. 

  1428.     pages = {26-41},
    1429. 

  1429.     shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
    1430. 

  1430.     title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
    1431. 

  1431.     url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
    1432. 

  1432.     volume = 12,
    1433. 

  1433.     year = 2008
    1434. 

  1434. }
    1435. 

  1435. 

  1436. @article{fsl_fast,
    1437. 

  1437.     author = {Zhang, Y. and Brady, M. and Smith, S.},
    1438. 

  1438.     doi = {10.1109/42.906424},
    1439. 

  1439.     issn = {0278-0062},
    1440. 

  1440.     journal = {IEEE Transactions on Medical Imaging},
    1441. 

  1441.     number = 1,
    1442. 

  1442.     pages = {45-57},
    1443. 

  1443.     title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
    1444. 

  1444.     volume = 20,
    1445. 

  1445.     year = 2001
    1446. 

  1446. }
    1447. 

  1447. 

  1448. 

  1449. @article{fieldmapless1,
    1450. 

  1450.     author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
    1451. 

  1451.     doi = {10.3389/fninf.2017.00017},
    1452. 

  1452.     issn = {1662-5196},
    1453. 

  1453.     journal = {Frontiers in Neuroinformatics},
    1454. 

  1454.     language = {English},
    1455. 

  1455.     title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
    1456. 

  1456.     url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
    1457. 

  1457.     volume = 11,
    1458. 

  1458.     year = 2017
    1459. 

  1459. }
    1460. 

  1460. 

  1461. @phdthesis{fieldmapless2,
    1462. 

  1462.     address = {Berlin},
    1463. 

  1463.     author = {Huntenburg, Julia M.},
    1464. 

  1464.     language = {eng},
    1465. 

  1465.     school = {Freie Universität},
    1466. 

  1466.     title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
    1467. 

  1467.     type = {Master's Thesis},
    1468. 

  1468.     url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
    1469. 

  1469.     year = 2014
    1470. 

  1470. }
    1471. 

  1471. 

  1472. @article{fieldmapless3,
    1473. 

  1473.     author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
    1474. 

  1474.     doi = {10.1371/journal.pone.0152472},
    1475. 

  1475.     issn = {1932-6203},
    1476. 

  1476.     journal = {PLOS ONE},
    1477. 

  1477.     number = 3,
    1478. 

  1478.     pages = {e0152472},
    1479. 

  1479.     title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
    1480. 

  1480.     url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
    1481. 

  1481.     volume = 11,
    1482. 

  1482.     year = 2016
    1483. 

  1483. }
    1484. 

  1484. 

  1485. @article{flirt,
    1486. 

  1486.     title = {A global optimisation method for robust affine registration of brain images},
    1487. 

  1487.     volume = {5},
    1488. 

  1488.     issn = {1361-8415},
    1489. 

  1489.     url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
    1490. 

  1490.     doi = {10.1016/S1361-8415(01)00036-6},
    1491. 

  1491.     number = {2},
    1492. 

  1492.     urldate = {2018-07-27},
    1493. 

  1493.     journal = {Medical Image Analysis},
    1494. 

  1494.     author = {Jenkinson, Mark and Smith, Stephen},
    1495. 

  1495.     year = {2001},
    1496. 

  1496.     keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
    1497. 

  1497.     pages = {143--156}
    1498. 

  1498. }
    1499. 

  1499. 

  1500. @article{mcflirt,
    1501. 

  1501.     author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
    1502. 

  1502.     doi = {10.1006/nimg.2002.1132},
    1503. 

  1503.     issn = {1053-8119},
    1504. 

  1504.     journal = {NeuroImage},
    1505. 

  1505.     number = 2,
    1506. 

  1506.     pages = {825-841},
    1507. 

  1507.     title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
    1508. 

  1508.     url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
    1509. 

  1509.     volume = 17,
    1510. 

  1510.     year = 2002
    1511. 

  1511. }
    1512. 

  1512. 

  1513. @article{bbr,
    1514. 

  1514.     author = {Greve, Douglas N and Fischl, Bruce},
    1515. 

  1515.     doi = {10.1016/j.neuroimage.2009.06.060},
    1516. 

  1516.     issn = {1095-9572},
    1517. 

  1517.     journal = {NeuroImage},
    1518. 

  1518.     number = 1,
    1519. 

  1519.     pages = {63-72},
    1520. 

  1520.     title = {Accurate and robust brain image alignment using boundary-based registration},
    1521. 

  1521.     volume = 48,
    1522. 

  1522.     year = 2009
    1523. 

  1523. }
    1524. 

  1524. 

  1525. @article{aroma,
    1526. 

  1526.     author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
    1527. 

  1527.     doi = {10.1016/j.neuroimage.2015.02.064},
    1528. 

  1528.     issn = {1053-8119},
    1529. 

  1529.     journal = {NeuroImage},
    1530. 

  1530.     number = {Supplement C},
    1531. 

  1531.     pages = {267-277},
    1532. 

  1532.     shorttitle = {ICA-AROMA},
    1533. 

  1533.     title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
    1534. 

  1534.     url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
    1535. 

  1535.     volume = 112,
    1536. 

  1536.     year = 2015
    1537. 

  1537. }
    1538. 

  1538. 

  1539. @article{power_fd_dvars,
    1540. 

  1540.     author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
    1541. 

  1541.     doi = {10.1016/j.neuroimage.2013.08.048},
    1542. 

  1542.     issn = {1053-8119},
    1543. 

  1543.     journal = {NeuroImage},
    1544. 

  1544.     number = {Supplement C},
    1545. 

  1545.     pages = {320-341},
    1546. 

  1546.     title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
    1547. 

  1547.     url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
    1548. 

  1548.     volume = 84,
    1549. 

  1549.     year = 2014
    1550. 

  1550. }
    1551. 

  1551. 

  1552. @article{confounds_satterthwaite_2013,
    1553. 

  1553.     author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
    1554. 

  1554.     doi = {10.1016/j.neuroimage.2012.08.052},
    1555. 

  1555.     issn = {10538119},
    1556. 

  1556.     journal = {NeuroImage},
    1557. 

  1557.     number = 1,
    1558. 

  1558.     pages = {240--256},
    1559. 

  1559.     title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
    1560. 

  1560.     url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
    1561. 

  1561.     volume = 64,
    1562. 

  1562.     year = 2013
    1563. 

  1563. }
    1564. 

  1564. 

  1565. 

  1566. @article{nilearn,
    1567. 

  1567.     author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
    1568. 

  1568.     doi = {10.3389/fninf.2014.00014},
    1569. 

  1569.     issn = {1662-5196},
    1570. 

  1570.     journal = {Frontiers in Neuroinformatics},
    1571. 

  1571.     language = {English},
    1572. 

  1572.     title = {Machine learning for neuroimaging with scikit-learn},
    1573. 

  1573.     url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
    1574. 

  1574.     volume = 8,
    1575. 

  1575.     year = 2014
    1576. 

  1576. }
    1577. 

  1577. 

  1578. @article{lanczos,
    1579. 

  1579.     author = {Lanczos, C.},
    1580. 

  1580.     doi = {10.1137/0701007},
    1581. 

  1581.     issn = {0887-459X},
    1582. 

  1582.     journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
    1583. 

  1583.     number = 1,
    1584. 

  1584.     pages = {76-85},
    1585. 

  1585.     title = {Evaluation of Noisy Data},
    1586. 

  1586.     url = {http://epubs.siam.org/doi/10.1137/0701007},
    1587. 

  1587.     volume = 1,
    1588. 

  1588.     year = 1964
    1589. 

  1589. }
    1590. 

  1590. 

  1591. @article{compcor,
    1592. 

  1592.     author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
    1593. 

  1593.     doi = {10.1016/j.neuroimage.2007.04.042},
    1594. 

  1594.     issn = {1053-8119},
    1595. 

  1595.     journal = {NeuroImage},
    1596. 

  1596.     number = 1,
    1597. 

  1597.     pages = {90-101},
    1598. 

  1598.     title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
    1599. 

  1599.     url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
    1600. 

  1600.     volume = 37,
    1601. 

  1601.     year = 2007
    1602. 

  1602. }
    1603. 

  1603. 

  1604. @article{hcppipelines,
    1605. 

  1605.     author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
    1606. 

  1606.     doi = {10.1016/j.neuroimage.2013.04.127},
    1607. 

  1607.     issn = {1053-8119},
    1608. 

  1608.     journal = {NeuroImage},
    1609. 

  1609.     pages = {105-124},
    1610. 

  1610.     series = {Mapping the Connectome},
    1611. 

  1611.     title = {The minimal preprocessing pipelines for the Human Connectome Project},
    1612. 

  1612.     url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
    1613. 

  1613.     volume = 80,
    1614. 

  1614.     year = 2013
    1615. 

  1615. }
    1616. 

  1616. 

  1617. @article{fs_template,
    1618. 

  1618.     author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
    1619. 

  1619.     doi = {10.1016/j.neuroimage.2010.07.020},
    1620. 

  1620.     journal = {NeuroImage},
    1621. 

  1621.     number = 4,
    1622. 

  1622.     pages = {1181-1196},
    1623. 

  1623.     title = {Highly accurate inverse consistent registration: A robust approach},
    1624. 

  1624.     volume = 53,
    1625. 

  1625.     year = 2010
    1626. 

  1626. }
    1627. 

  1627. 

  1628. @article{afni,
    1629. 

  1629.     author = {Cox, Robert W. and Hyde, James S.},
    1630. 

  1630.     doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
    1631. 

  1631.     journal = {NMR in Biomedicine},
    1632. 

  1632.     number = {4-5},
    1633. 

  1633.     pages = {171-178},
    1634. 

  1634.     title = {Software tools for analysis and visualization of fMRI data},
    1635. 

  1635.     volume = 10,
    1636. 

  1636.     year = 1997
    1637. 

  1637. }
    1638. 

  1638. 

  1639. @article{posse_t2s,
    1640. 

  1640.     author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
    1641. 

  1641.     doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
    1642. 

  1642.     journal = {Magnetic Resonance in Medicine},
    1643. 

  1643.     number = 1,
    1644. 

  1644.     pages = {87-97},
    1645. 

  1645.     title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
    1646. 

  1646.     volume = 42,
    1647. 

  1647.     year = 1999
    1648. 

  1648. }
    1649. 

  1649. </pre>
    1650. 

  1650. </div>
    1651. 

  1651.     </div>
    1652. 

  1652. </div>
    1653. 

  1653. 

  1654. <div id="errors">
    1655. 

  1655.     <h1 class="sub-report-title">Errors</h1>
    1656. 

  1656.     <p>No errors to report!</p>
    1657. 

  1657. </div>
    1658. 

  1658. 

  1659. 

  1660. <script type="text/javascript">
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