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  56. </head>
    57. 

  57. <body>
    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-func_desc-summary_run-1_suffix-bold_task-conditionalstopsignal">Reports for: task <span class="bids-entity">conditionalstopsignal</span>, run <span class="bids-entity">1</span>.</a>
    69. 

  69.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-2_suffix-bold_task-conditionalstopsignal">Reports for: task <span class="bids-entity">conditionalstopsignal</span>, run <span class="bids-entity">2</span>.</a>
    70. 

  70.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-3_suffix-bold_task-conditionalstopsignal">Reports for: task <span class="bids-entity">conditionalstopsignal</span>, run <span class="bids-entity">3</span>.</a>
    71. 

  71.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-None_suffix-bold_task-freerecall">Reports for: task <span class="bids-entity">freerecall</span>.</a>
    72. 

  72.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-None_suffix-bold_task-sherlockPart1">Reports for: task <span class="bids-entity">sherlockPart1</span>.</a>
    73. 

  73.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-None_suffix-bold_task-sherlockPart2">Reports for: task <span class="bids-entity">sherlockPart2</span>.</a>
    74. 

  74.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-1_suffix-bold_task-stopsignal">Reports for: task <span class="bids-entity">stopsignal</span>, run <span class="bids-entity">1</span>.</a>
    75. 

  75.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-2_suffix-bold_task-stopsignal">Reports for: task <span class="bids-entity">stopsignal</span>, run <span class="bids-entity">2</span>.</a>
    76. 

  76.                     <a class="dropdown-item" href="#datatype-func_desc-summary_run-3_suffix-bold_task-stopsignal">Reports for: task <span class="bids-entity">stopsignal</span>, run <span class="bids-entity">3</span>.</a>
    77. 

  77.             </div>
    78. 

  78.         </li>
    79. 

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

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

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

  82.     </ul>
    83. 

  83. </div>
    84. 

  84. </nav>
    85. 

  85. <noscript>
    86. 

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

  87. </noscript>
    88. 

  88. 

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

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

  91.         <div id="datatype-anat_desc-summary_suffix-T1w">
    92. 

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

  93. 		<li>Subject ID: 15</li>
    94. 

  94. 		<li>Structural images: 1 T1-weighted </li>
    95. 

  95. 		<li>Functional series: 3</li>
    96. 

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

  97. 			<li>Task: freerecall (1 run)</li>
    98. 

  98. 			<li>Task: sherlockPart1 (1 run)</li>
    99. 

  99. 			<li>Task: sherlockPart2 (1 run)</li>
    100. 

  100. 		</ul>
    101. 

  101. 		<li>Standard output spaces: MNI152NLin2009cAsym</li>
    102. 

  102. 		<li>Non-standard output spaces: </li>
    103. 

  103. 		<li>FreeSurfer reconstruction: Not run</li>
    104. 

  104. 	</ul>
    105. 

  105.         </div>
    106. 

  106.     </div>
    107. 

  107.     <div id="Anatomical">
    108. 

  108.     <h1 class="sub-report-title">Anatomical</h1>
    109. 

  109.         <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
    110. 

  110.                     <h3 class="elem-title">Anatomical Conformation</h3>
    111. 

  111. 		<ul class="elem-desc">
    112. 

  112. 			<li>Input T1w images: 1</li>
    113. 

  113. 			<li>Output orientation: RAS</li>
    114. 

  114. 			<li>Output dimensions: 192x243x209</li>
    115. 

  115. 			<li>Output voxel size: 0.9mm x 0.86mm x 0.86mm</li>
    116. 

  116. 			<li>Discarded images: 0</li>
    117. 

  117. 

  118. 		</ul>
    119. 

  119.         </div>
    120. 

  120.         <div id="datatype-anat_suffix-dseg">
    121. 

  121. <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-15/figures/sub-15_dseg.svg" style="width: 100%" />
    122. 

  122. </div>
    123. 

  123. <div class="elem-filename">
    124. 

  124.     Get figure file: <a href="./sub-15/figures/sub-15_dseg.svg" target="_blank">sub-15/figures/sub-15_dseg.svg</a>
    125. 

  125. </div>
    126. 

  126. 

  127.         </div>
    128. 

  128.         <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
    129. 

  129. <h3 class="run-title">Spatial normalization of the anatomical T1w reference</h3><p class="elem-desc">Results of nonlinear alignment of the T1w reference one or more template space(s). Hover on the panels with the mouse pointer to transition between both spaces.</p><p class="elem-caption">Spatial normalization of the T1w image to the <code>MNI152NLin2009cAsym</code> template.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_space-MNI152NLin2009cAsym_T1w.svg">
    130. 

  130. Problem loading figure sub-15/figures/sub-15_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
    131. 

  131. </div>
    132. 

  132. <div class="elem-filename">
    133. 

  133.     Get figure file: <a href="./sub-15/figures/sub-15_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-15/figures/sub-15_space-MNI152NLin2009cAsym_T1w.svg</a>
    134. 

  134. </div>
    135. 

  135. 

  136.         </div>
    137. 

  137.     </div>
    138. 

  138.     <div id="Functional">
    139. 

  139.     <h1 class="sub-report-title">Functional</h1>
    140. 

  140.         <div id="datatype-func_desc-summary_run-1_suffix-bold_task-conditionalstopsignal">
    141. 

  141. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">conditionalstopsignal</span>, run <span class="bids-entity">1</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    142. 

  142. 		<ul class="elem-desc">
    143. 

  143. 			<li>Repetition time (TR): 2s</li>
    144. 

  144. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    145. 

  145. 			<li>Slice timing correction: Not applied</li>
    146. 

  146. 			<li>Susceptibility distortion correction: None</li>
    147. 

  147. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    148. 

  148. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, cosine00, cosine01, cosine02, cosine03, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02</li>
    149. 

  149. 			<li>Non-steady-state volumes: 0</li>
    150. 

  150. 		</ul>
    151. 

  151.         </div>
    152. 

  152.         <div id="datatype-func_desc-flirtbbr_run-1_suffix-bold_task-conditionalstopsignal">
    153. 

  153. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-flirtbbr_bold.svg">
    154. 

  154. Problem loading figure sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    155. 

  155. </div>
    156. 

  156. <div class="elem-filename">
    157. 

  157.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-flirtbbr_bold.svg</a>
    158. 

  158. </div>
    159. 

  159. 

  160.         </div>
    161. 

  161.         <div id="datatype-func_desc-rois_run-1_suffix-bold_task-conditionalstopsignal">
    162. 

  162. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-rois_bold.svg" style="width: 100%" />
    163. 

  163. </div>
    164. 

  164. <div class="elem-filename">
    165. 

  165.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-rois_bold.svg</a>
    166. 

  166. </div>
    167. 

  167. 

  168.         </div>
    169. 

  169.         <div id="datatype-func_desc-compcorvar_run-1_suffix-bold_task-conditionalstopsignal">
    170. 

  170. <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-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-compcorvar_bold.svg" style="width: 100%" />
    171. 

  171. </div>
    172. 

  172. <div class="elem-filename">
    173. 

  173.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-compcorvar_bold.svg</a>
    174. 

  174. </div>
    175. 

  175. 

  176.         </div>
    177. 

  177.         <div id="datatype-func_desc-carpetplot_run-1_suffix-bold_task-conditionalstopsignal">
    178. 

  178. <h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-carpetplot_bold.svg" style="width: 100%" />
    179. 

  179. </div>
    180. 

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

  181.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-carpetplot_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-carpetplot_bold.svg</a>
    182. 

  182. </div>
    183. 

  183. 

  184.         </div>
    185. 

  185.         <div id="datatype-func_desc-confoundcorr_run-1_suffix-bold_task-conditionalstopsignal">
    186. 

  186. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    187. 

  187. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    188. 

  188. Right: magnitude of the correlation between each confound time series and the
    189. 

  189. mean global signal. Strong correlations might be indicative of partial volume
    190. 

  190. effects and can inform decisions about feature orthogonalization prior to
    191. 

  191. confound regression.
    192. 

  192. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-confoundcorr_bold.svg" style="width: 100%" />
    193. 

  193. </div>
    194. 

  194. <div class="elem-filename">
    195. 

  195.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-1_desc-confoundcorr_bold.svg</a>
    196. 

  196. </div>
    197. 

  197. 

  198.         </div>
    199. 

  199.         <div id="datatype-func_desc-summary_run-2_suffix-bold_task-conditionalstopsignal">
    200. 

  200. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">conditionalstopsignal</span>, run <span class="bids-entity">2</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    201. 

  201. 		<ul class="elem-desc">
    202. 

  202. 			<li>Repetition time (TR): 2s</li>
    203. 

  203. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    204. 

  204. 			<li>Slice timing correction: Not applied</li>
    205. 

  205. 			<li>Susceptibility distortion correction: None</li>
    206. 

  206. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    207. 

  207. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, t_comp_cor_07, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, a_comp_cor_135, 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_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02</li>
    208. 

  208. 			<li>Non-steady-state volumes: 1</li>
    209. 

  209. 		</ul>
    210. 

  210.         </div>
    211. 

  211.         <div id="datatype-func_desc-flirtbbr_run-2_suffix-bold_task-conditionalstopsignal">
    212. 

  212. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-flirtbbr_bold.svg">
    213. 

  213. Problem loading figure sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    214. 

  214. </div>
    215. 

  215. <div class="elem-filename">
    216. 

  216.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-flirtbbr_bold.svg</a>
    217. 

  217. </div>
    218. 

  218. 

  219.         </div>
    220. 

  220.         <div id="datatype-func_desc-rois_run-2_suffix-bold_task-conditionalstopsignal">
    221. 

  221. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-rois_bold.svg" style="width: 100%" />
    222. 

  222. </div>
    223. 

  223. <div class="elem-filename">
    224. 

  224.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-rois_bold.svg</a>
    225. 

  225. </div>
    226. 

  226. 

  227.         </div>
    228. 

  228.         <div id="datatype-func_desc-compcorvar_run-2_suffix-bold_task-conditionalstopsignal">
    229. 

  229. <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-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-compcorvar_bold.svg" style="width: 100%" />
    230. 

  230. </div>
    231. 

  231. <div class="elem-filename">
    232. 

  232.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-compcorvar_bold.svg</a>
    233. 

  233. </div>
    234. 

  234. 

  235.         </div>
    236. 

  236.         <div id="datatype-func_desc-carpetplot_run-2_suffix-bold_task-conditionalstopsignal">
    237. 

  237. <h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-carpetplot_bold.svg" style="width: 100%" />
    238. 

  238. </div>
    239. 

  239. <div class="elem-filename">
    240. 

  240.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-carpetplot_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-carpetplot_bold.svg</a>
    241. 

  241. </div>
    242. 

  242. 

  243.         </div>
    244. 

  244.         <div id="datatype-func_desc-confoundcorr_run-2_suffix-bold_task-conditionalstopsignal">
    245. 

  245. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    246. 

  246. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    247. 

  247. Right: magnitude of the correlation between each confound time series and the
    248. 

  248. mean global signal. Strong correlations might be indicative of partial volume
    249. 

  249. effects and can inform decisions about feature orthogonalization prior to
    250. 

  250. confound regression.
    251. 

  251. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-confoundcorr_bold.svg" style="width: 100%" />
    252. 

  252. </div>
    253. 

  253. <div class="elem-filename">
    254. 

  254.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-2_desc-confoundcorr_bold.svg</a>
    255. 

  255. </div>
    256. 

  256. 

  257.         </div>
    258. 

  258.         <div id="datatype-func_desc-summary_run-3_suffix-bold_task-conditionalstopsignal">
    259. 

  259. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">conditionalstopsignal</span>, run <span class="bids-entity">3</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    260. 

  260. 		<ul class="elem-desc">
    261. 

  261. 			<li>Repetition time (TR): 2s</li>
    262. 

  262. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    263. 

  263. 			<li>Slice timing correction: Not applied</li>
    264. 

  264. 			<li>Susceptibility distortion correction: None</li>
    265. 

  265. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    266. 

  266. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, t_comp_cor_07, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, a_comp_cor_135, a_comp_cor_136, a_comp_cor_137, a_comp_cor_138, a_comp_cor_139, 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_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01</li>
    267. 

  267. 			<li>Non-steady-state volumes: 1</li>
    268. 

  268. 		</ul>
    269. 

  269.         </div>
    270. 

  270.         <div id="datatype-func_desc-flirtbbr_run-3_suffix-bold_task-conditionalstopsignal">
    271. 

  271. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-flirtbbr_bold.svg">
    272. 

  272. Problem loading figure sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    273. 

  273. </div>
    274. 

  274. <div class="elem-filename">
    275. 

  275.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-flirtbbr_bold.svg</a>
    276. 

  276. </div>
    277. 

  277. 

  278.         </div>
    279. 

  279.         <div id="datatype-func_desc-rois_run-3_suffix-bold_task-conditionalstopsignal">
    280. 

  280. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-rois_bold.svg" style="width: 100%" />
    281. 

  281. </div>
    282. 

  282. <div class="elem-filename">
    283. 

  283.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-rois_bold.svg</a>
    284. 

  284. </div>
    285. 

  285. 

  286.         </div>
    287. 

  287.         <div id="datatype-func_desc-compcorvar_run-3_suffix-bold_task-conditionalstopsignal">
    288. 

  288. <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-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-compcorvar_bold.svg" style="width: 100%" />
    289. 

  289. </div>
    290. 

  290. <div class="elem-filename">
    291. 

  291.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-compcorvar_bold.svg</a>
    292. 

  292. </div>
    293. 

  293. 

  294.         </div>
    295. 

  295.         <div id="datatype-func_desc-carpetplot_run-3_suffix-bold_task-conditionalstopsignal">
    296. 

  296. <h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-carpetplot_bold.svg" style="width: 100%" />
    297. 

  297. </div>
    298. 

  298. <div class="elem-filename">
    299. 

  299.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-carpetplot_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-carpetplot_bold.svg</a>
    300. 

  300. </div>
    301. 

  301. 

  302.         </div>
    303. 

  303.         <div id="datatype-func_desc-confoundcorr_run-3_suffix-bold_task-conditionalstopsignal">
    304. 

  304. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    305. 

  305. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    306. 

  306. Right: magnitude of the correlation between each confound time series and the
    307. 

  307. mean global signal. Strong correlations might be indicative of partial volume
    308. 

  308. effects and can inform decisions about feature orthogonalization prior to
    309. 

  309. confound regression.
    310. 

  310. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-confoundcorr_bold.svg" style="width: 100%" />
    311. 

  311. </div>
    312. 

  312. <div class="elem-filename">
    313. 

  313.     Get figure file: <a href="./sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-conditionalstopsignal_run-3_desc-confoundcorr_bold.svg</a>
    314. 

  314. </div>
    315. 

  315. 

  316.         </div>
    317. 

  317.         <div id="datatype-func_desc-summary_run-None_suffix-bold_task-freerecall">
    318. 

  318. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">freerecall</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    319. 

  319. 		<ul class="elem-desc">
    320. 

  320. 			<li>Repetition time (TR): 1.5s</li>
    321. 

  321. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    322. 

  322. 			<li>Slice timing correction: Not applied</li>
    323. 

  323. 			<li>Susceptibility distortion correction: None</li>
    324. 

  324. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    325. 

  325. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, a_comp_cor_135, a_comp_cor_136, a_comp_cor_137, a_comp_cor_138, a_comp_cor_139, a_comp_cor_140, a_comp_cor_141, a_comp_cor_142, a_comp_cor_143, a_comp_cor_144, a_comp_cor_145, a_comp_cor_146, a_comp_cor_147, a_comp_cor_148, a_comp_cor_149, a_comp_cor_150, a_comp_cor_151, a_comp_cor_152, a_comp_cor_153, a_comp_cor_154, a_comp_cor_155, a_comp_cor_156, a_comp_cor_157, a_comp_cor_158, a_comp_cor_159, a_comp_cor_160, a_comp_cor_161, a_comp_cor_162, a_comp_cor_163, a_comp_cor_164, a_comp_cor_165, a_comp_cor_166, a_comp_cor_167, a_comp_cor_168, a_comp_cor_169, a_comp_cor_170, a_comp_cor_171, a_comp_cor_172, a_comp_cor_173, a_comp_cor_174, a_comp_cor_175, a_comp_cor_176, a_comp_cor_177, a_comp_cor_178, a_comp_cor_179, a_comp_cor_180, a_comp_cor_181, a_comp_cor_182, a_comp_cor_183, a_comp_cor_184, a_comp_cor_185, a_comp_cor_186, a_comp_cor_187, a_comp_cor_188, a_comp_cor_189, a_comp_cor_190, a_comp_cor_191, a_comp_cor_192, a_comp_cor_193, a_comp_cor_194, a_comp_cor_195, a_comp_cor_196, a_comp_cor_197, a_comp_cor_198, a_comp_cor_199, a_comp_cor_200, a_comp_cor_201, a_comp_cor_202, a_comp_cor_203, a_comp_cor_204, a_comp_cor_205, a_comp_cor_206, a_comp_cor_207, a_comp_cor_208, a_comp_cor_209, a_comp_cor_210, a_comp_cor_211, a_comp_cor_212, a_comp_cor_213, a_comp_cor_214, a_comp_cor_215, a_comp_cor_216, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, cosine08, cosine09, cosine10, cosine11, cosine12, cosine13, cosine14, cosine15, cosine16, cosine17, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17, motion_outlier18, motion_outlier19, motion_outlier20, motion_outlier21, motion_outlier22, motion_outlier23, motion_outlier24, motion_outlier25, motion_outlier26, motion_outlier27, motion_outlier28, motion_outlier29, motion_outlier30, motion_outlier31, motion_outlier32, motion_outlier33, motion_outlier34, motion_outlier35, motion_outlier36, motion_outlier37, motion_outlier38, motion_outlier39, motion_outlier40, motion_outlier41, motion_outlier42, motion_outlier43, motion_outlier44, motion_outlier45, motion_outlier46, motion_outlier47, motion_outlier48, motion_outlier49, motion_outlier50, motion_outlier51, motion_outlier52, motion_outlier53, motion_outlier54, motion_outlier55, motion_outlier56, motion_outlier57, motion_outlier58, motion_outlier59, motion_outlier60, motion_outlier61, motion_outlier62, motion_outlier63, motion_outlier64, motion_outlier65, motion_outlier66, motion_outlier67, motion_outlier68, motion_outlier69, motion_outlier70, motion_outlier71, motion_outlier72, motion_outlier73, motion_outlier74, motion_outlier75, motion_outlier76, motion_outlier77, motion_outlier78, motion_outlier79, motion_outlier80, motion_outlier81, motion_outlier82, motion_outlier83, motion_outlier84, motion_outlier85, motion_outlier86, motion_outlier87, motion_outlier88, motion_outlier89, motion_outlier90, motion_outlier91, motion_outlier92, motion_outlier93, motion_outlier94, motion_outlier95, motion_outlier96, motion_outlier97, motion_outlier98, motion_outlier99, motion_outlier100, motion_outlier101, motion_outlier102, motion_outlier103, motion_outlier104, motion_outlier105, motion_outlier106, motion_outlier107, motion_outlier108, motion_outlier109, motion_outlier110, motion_outlier111, motion_outlier112, motion_outlier113, motion_outlier114, motion_outlier115, motion_outlier116, motion_outlier117, motion_outlier118, motion_outlier119, motion_outlier120, motion_outlier121, motion_outlier122, motion_outlier123, motion_outlier124, motion_outlier125, motion_outlier126, motion_outlier127, motion_outlier128, motion_outlier129, motion_outlier130, motion_outlier131, motion_outlier132, motion_outlier133, motion_outlier134, motion_outlier135, motion_outlier136, motion_outlier137, motion_outlier138, motion_outlier139, motion_outlier140, motion_outlier141, motion_outlier142, motion_outlier143, motion_outlier144, motion_outlier145, motion_outlier146, motion_outlier147, motion_outlier148, motion_outlier149, motion_outlier150, motion_outlier151, motion_outlier152, motion_outlier153, motion_outlier154, motion_outlier155, motion_outlier156, motion_outlier157, motion_outlier158, motion_outlier159, motion_outlier160, motion_outlier161, motion_outlier162, motion_outlier163, motion_outlier164, motion_outlier165, motion_outlier166, motion_outlier167, motion_outlier168, motion_outlier169, motion_outlier170, motion_outlier171, motion_outlier172, motion_outlier173, motion_outlier174, motion_outlier175, motion_outlier176, motion_outlier177, motion_outlier178, motion_outlier179, motion_outlier180, motion_outlier181, motion_outlier182, motion_outlier183, motion_outlier184, motion_outlier185, motion_outlier186, motion_outlier187, motion_outlier188, motion_outlier189, motion_outlier190, motion_outlier191, motion_outlier192, motion_outlier193, motion_outlier194, motion_outlier195, motion_outlier196, motion_outlier197, motion_outlier198, motion_outlier199, motion_outlier200, motion_outlier201, motion_outlier202, motion_outlier203, motion_outlier204, motion_outlier205, motion_outlier206, motion_outlier207, motion_outlier208, motion_outlier209, motion_outlier210, motion_outlier211, motion_outlier212, motion_outlier213, motion_outlier214, motion_outlier215, motion_outlier216, motion_outlier217, motion_outlier218, motion_outlier219, motion_outlier220, motion_outlier221, motion_outlier222, motion_outlier223, motion_outlier224, motion_outlier225, motion_outlier226, motion_outlier227, motion_outlier228, motion_outlier229, motion_outlier230, motion_outlier231, motion_outlier232, motion_outlier233, motion_outlier234, motion_outlier235, motion_outlier236, motion_outlier237, motion_outlier238, motion_outlier239, motion_outlier240, motion_outlier241, motion_outlier242, motion_outlier243, motion_outlier244, motion_outlier245, motion_outlier246, motion_outlier247, motion_outlier248, motion_outlier249, motion_outlier250, motion_outlier251, motion_outlier252, motion_outlier253, motion_outlier254, motion_outlier255, motion_outlier256, motion_outlier257, motion_outlier258, motion_outlier259, motion_outlier260, motion_outlier261, motion_outlier262, motion_outlier263, motion_outlier264, motion_outlier265, motion_outlier266, motion_outlier267, motion_outlier268, motion_outlier269, motion_outlier270, motion_outlier271, motion_outlier272, motion_outlier273, motion_outlier274, motion_outlier275, motion_outlier276, motion_outlier277, motion_outlier278, motion_outlier279, motion_outlier280, motion_outlier281, motion_outlier282, motion_outlier283, motion_outlier284, motion_outlier285, motion_outlier286, motion_outlier287, motion_outlier288, motion_outlier289, motion_outlier290, motion_outlier291, motion_outlier292, motion_outlier293, motion_outlier294, motion_outlier295, motion_outlier296, motion_outlier297, motion_outlier298, motion_outlier299, motion_outlier300</li>
    326. 

  326. 			<li>Non-steady-state volumes: 0</li>
    327. 

  327. 		</ul>
    328. 

  328.         </div>
    329. 

  329.         <div id="datatype-func_desc-flirtbbr_run-None_suffix-bold_task-freerecall">
    330. 

  330. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-freerecall_desc-flirtbbr_bold.svg">
    331. 

  331. Problem loading figure sub-15/figures/sub-15_task-freerecall_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    332. 

  332. </div>
    333. 

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

  334.     Get figure file: <a href="./sub-15/figures/sub-15_task-freerecall_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-freerecall_desc-flirtbbr_bold.svg</a>
    335. 

  335. </div>
    336. 

  336. 

  337.         </div>
    338. 

  338.         <div id="datatype-func_desc-rois_run-None_suffix-bold_task-freerecall">
    339. 

  339. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-freerecall_desc-rois_bold.svg" style="width: 100%" />
    340. 

  340. </div>
    341. 

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

  342.     Get figure file: <a href="./sub-15/figures/sub-15_task-freerecall_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-freerecall_desc-rois_bold.svg</a>
    343. 

  343. </div>
    344. 

  344. 

  345.         </div>
    346. 

  346.         <div id="datatype-func_desc-compcorvar_run-None_suffix-bold_task-freerecall">
    347. 

  347. <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-15/figures/sub-15_task-freerecall_desc-compcorvar_bold.svg" style="width: 100%" />
    348. 

  348. </div>
    349. 

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

  350.     Get figure file: <a href="./sub-15/figures/sub-15_task-freerecall_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-freerecall_desc-compcorvar_bold.svg</a>
    351. 

  351. </div>
    352. 

  352. 

  353.         </div>
    354. 

  354.         <div id="datatype-func_desc-confoundcorr_run-None_suffix-bold_task-freerecall">
    355. 

  355. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    356. 

  356. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    357. 

  357. Right: magnitude of the correlation between each confound time series and the
    358. 

  358. mean global signal. Strong correlations might be indicative of partial volume
    359. 

  359. effects and can inform decisions about feature orthogonalization prior to
    360. 

  360. confound regression.
    361. 

  361. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-freerecall_desc-confoundcorr_bold.svg" style="width: 100%" />
    362. 

  362. </div>
    363. 

  363. <div class="elem-filename">
    364. 

  364.     Get figure file: <a href="./sub-15/figures/sub-15_task-freerecall_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-freerecall_desc-confoundcorr_bold.svg</a>
    365. 

  365. </div>
    366. 

  366. 

  367.         </div>
    368. 

  368.         <div id="datatype-func_desc-summary_run-None_suffix-bold_task-sherlockPart1">
    369. 

  369. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">sherlockPart1</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    370. 

  370. 		<ul class="elem-desc">
    371. 

  371. 			<li>Repetition time (TR): 1.5s</li>
    372. 

  372. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    373. 

  373. 			<li>Slice timing correction: Not applied</li>
    374. 

  374. 			<li>Susceptibility distortion correction: None</li>
    375. 

  375. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    376. 

  376. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, a_comp_cor_135, a_comp_cor_136, a_comp_cor_137, a_comp_cor_138, a_comp_cor_139, a_comp_cor_140, a_comp_cor_141, a_comp_cor_142, a_comp_cor_143, a_comp_cor_144, a_comp_cor_145, a_comp_cor_146, a_comp_cor_147, a_comp_cor_148, a_comp_cor_149, a_comp_cor_150, a_comp_cor_151, a_comp_cor_152, a_comp_cor_153, a_comp_cor_154, a_comp_cor_155, a_comp_cor_156, a_comp_cor_157, a_comp_cor_158, a_comp_cor_159, a_comp_cor_160, a_comp_cor_161, a_comp_cor_162, a_comp_cor_163, a_comp_cor_164, a_comp_cor_165, a_comp_cor_166, a_comp_cor_167, a_comp_cor_168, a_comp_cor_169, a_comp_cor_170, a_comp_cor_171, a_comp_cor_172, a_comp_cor_173, a_comp_cor_174, a_comp_cor_175, a_comp_cor_176, a_comp_cor_177, a_comp_cor_178, a_comp_cor_179, a_comp_cor_180, a_comp_cor_181, a_comp_cor_182, a_comp_cor_183, a_comp_cor_184, a_comp_cor_185, a_comp_cor_186, a_comp_cor_187, a_comp_cor_188, a_comp_cor_189, a_comp_cor_190, a_comp_cor_191, a_comp_cor_192, a_comp_cor_193, a_comp_cor_194, a_comp_cor_195, a_comp_cor_196, a_comp_cor_197, a_comp_cor_198, a_comp_cor_199, a_comp_cor_200, a_comp_cor_201, a_comp_cor_202, a_comp_cor_203, a_comp_cor_204, a_comp_cor_205, a_comp_cor_206, a_comp_cor_207, a_comp_cor_208, a_comp_cor_209, a_comp_cor_210, a_comp_cor_211, a_comp_cor_212, a_comp_cor_213, a_comp_cor_214, a_comp_cor_215, a_comp_cor_216, a_comp_cor_217, a_comp_cor_218, a_comp_cor_219, a_comp_cor_220, a_comp_cor_221, a_comp_cor_222, a_comp_cor_223, a_comp_cor_224, a_comp_cor_225, a_comp_cor_226, a_comp_cor_227, a_comp_cor_228, a_comp_cor_229, a_comp_cor_230, a_comp_cor_231, a_comp_cor_232, a_comp_cor_233, a_comp_cor_234, a_comp_cor_235, a_comp_cor_236, a_comp_cor_237, a_comp_cor_238, a_comp_cor_239, a_comp_cor_240, a_comp_cor_241, a_comp_cor_242, a_comp_cor_243, a_comp_cor_244, a_comp_cor_245, a_comp_cor_246, a_comp_cor_247, a_comp_cor_248, a_comp_cor_249, a_comp_cor_250, a_comp_cor_251, a_comp_cor_252, a_comp_cor_253, a_comp_cor_254, a_comp_cor_255, a_comp_cor_256, a_comp_cor_257, a_comp_cor_258, a_comp_cor_259, a_comp_cor_260, a_comp_cor_261, a_comp_cor_262, a_comp_cor_263, a_comp_cor_264, a_comp_cor_265, a_comp_cor_266, a_comp_cor_267, a_comp_cor_268, a_comp_cor_269, a_comp_cor_270, a_comp_cor_271, a_comp_cor_272, a_comp_cor_273, a_comp_cor_274, a_comp_cor_275, a_comp_cor_276, a_comp_cor_277, a_comp_cor_278, a_comp_cor_279, a_comp_cor_280, a_comp_cor_281, a_comp_cor_282, a_comp_cor_283, a_comp_cor_284, a_comp_cor_285, a_comp_cor_286, a_comp_cor_287, a_comp_cor_288, a_comp_cor_289, a_comp_cor_290, a_comp_cor_291, a_comp_cor_292, a_comp_cor_293, a_comp_cor_294, a_comp_cor_295, a_comp_cor_296, a_comp_cor_297, a_comp_cor_298, a_comp_cor_299, a_comp_cor_300, a_comp_cor_301, a_comp_cor_302, a_comp_cor_303, a_comp_cor_304, a_comp_cor_305, a_comp_cor_306, a_comp_cor_307, a_comp_cor_308, a_comp_cor_309, a_comp_cor_310, a_comp_cor_311, a_comp_cor_312, a_comp_cor_313, a_comp_cor_314, a_comp_cor_315, a_comp_cor_316, a_comp_cor_317, a_comp_cor_318, a_comp_cor_319, a_comp_cor_320, a_comp_cor_321, a_comp_cor_322, a_comp_cor_323, a_comp_cor_324, a_comp_cor_325, a_comp_cor_326, a_comp_cor_327, a_comp_cor_328, a_comp_cor_329, a_comp_cor_330, a_comp_cor_331, a_comp_cor_332, a_comp_cor_333, a_comp_cor_334, a_comp_cor_335, a_comp_cor_336, a_comp_cor_337, a_comp_cor_338, a_comp_cor_339, a_comp_cor_340, a_comp_cor_341, a_comp_cor_342, a_comp_cor_343, a_comp_cor_344, a_comp_cor_345, a_comp_cor_346, a_comp_cor_347, a_comp_cor_348, a_comp_cor_349, a_comp_cor_350, a_comp_cor_351, a_comp_cor_352, a_comp_cor_353, a_comp_cor_354, a_comp_cor_355, a_comp_cor_356, a_comp_cor_357, a_comp_cor_358, a_comp_cor_359, a_comp_cor_360, a_comp_cor_361, a_comp_cor_362, a_comp_cor_363, a_comp_cor_364, a_comp_cor_365, a_comp_cor_366, a_comp_cor_367, a_comp_cor_368, a_comp_cor_369, a_comp_cor_370, a_comp_cor_371, a_comp_cor_372, a_comp_cor_373, a_comp_cor_374, a_comp_cor_375, a_comp_cor_376, a_comp_cor_377, a_comp_cor_378, a_comp_cor_379, a_comp_cor_380, a_comp_cor_381, a_comp_cor_382, a_comp_cor_383, a_comp_cor_384, a_comp_cor_385, a_comp_cor_386, a_comp_cor_387, a_comp_cor_388, a_comp_cor_389, a_comp_cor_390, a_comp_cor_391, a_comp_cor_392, a_comp_cor_393, a_comp_cor_394, a_comp_cor_395, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, cosine08, cosine09, cosine10, cosine11, cosine12, cosine13, cosine14, cosine15, cosine16, cosine17, cosine18, cosine19, cosine20, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06</li>
    377. 

  377. 			<li>Non-steady-state volumes: 1</li>
    378. 

  378. 		</ul>
    379. 

  379.         </div>
    380. 

  380.         <div id="datatype-func_desc-flirtbbr_run-None_suffix-bold_task-sherlockPart1">
    381. 

  381. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-sherlockPart1_desc-flirtbbr_bold.svg">
    382. 

  382. Problem loading figure sub-15/figures/sub-15_task-sherlockPart1_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    383. 

  383. </div>
    384. 

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

  385.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart1_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart1_desc-flirtbbr_bold.svg</a>
    386. 

  386. </div>
    387. 

  387. 

  388.         </div>
    389. 

  389.         <div id="datatype-func_desc-rois_run-None_suffix-bold_task-sherlockPart1">
    390. 

  390. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-sherlockPart1_desc-rois_bold.svg" style="width: 100%" />
    391. 

  391. </div>
    392. 

  392. <div class="elem-filename">
    393. 

  393.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart1_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart1_desc-rois_bold.svg</a>
    394. 

  394. </div>
    395. 

  395. 

  396.         </div>
    397. 

  397.         <div id="datatype-func_desc-compcorvar_run-None_suffix-bold_task-sherlockPart1">
    398. 

  398. <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-15/figures/sub-15_task-sherlockPart1_desc-compcorvar_bold.svg" style="width: 100%" />
    399. 

  399. </div>
    400. 

  400. <div class="elem-filename">
    401. 

  401.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart1_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart1_desc-compcorvar_bold.svg</a>
    402. 

  402. </div>
    403. 

  403. 

  404.         </div>
    405. 

  405.         <div id="datatype-func_desc-confoundcorr_run-None_suffix-bold_task-sherlockPart1">
    406. 

  406. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    407. 

  407. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    408. 

  408. Right: magnitude of the correlation between each confound time series and the
    409. 

  409. mean global signal. Strong correlations might be indicative of partial volume
    410. 

  410. effects and can inform decisions about feature orthogonalization prior to
    411. 

  411. confound regression.
    412. 

  412. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-sherlockPart1_desc-confoundcorr_bold.svg" style="width: 100%" />
    413. 

  413. </div>
    414. 

  414. <div class="elem-filename">
    415. 

  415.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart1_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart1_desc-confoundcorr_bold.svg</a>
    416. 

  416. </div>
    417. 

  417. 

  418.         </div>
    419. 

  419.         <div id="datatype-func_desc-summary_run-None_suffix-bold_task-sherlockPart2">
    420. 

  420. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">sherlockPart2</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    421. 

  421. 		<ul class="elem-desc">
    422. 

  422. 			<li>Repetition time (TR): 1.5s</li>
    423. 

  423. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    424. 

  424. 			<li>Slice timing correction: Not applied</li>
    425. 

  425. 			<li>Susceptibility distortion correction: None</li>
    426. 

  426. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    427. 

  427. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_derivative1_power2, white_matter_power2, global_signal, global_signal_derivative1, global_signal_derivative1_power2, global_signal_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, a_comp_cor_135, a_comp_cor_136, a_comp_cor_137, a_comp_cor_138, a_comp_cor_139, a_comp_cor_140, a_comp_cor_141, a_comp_cor_142, a_comp_cor_143, a_comp_cor_144, a_comp_cor_145, a_comp_cor_146, a_comp_cor_147, a_comp_cor_148, a_comp_cor_149, a_comp_cor_150, a_comp_cor_151, a_comp_cor_152, a_comp_cor_153, a_comp_cor_154, a_comp_cor_155, a_comp_cor_156, a_comp_cor_157, a_comp_cor_158, a_comp_cor_159, a_comp_cor_160, a_comp_cor_161, a_comp_cor_162, a_comp_cor_163, a_comp_cor_164, a_comp_cor_165, a_comp_cor_166, a_comp_cor_167, a_comp_cor_168, a_comp_cor_169, a_comp_cor_170, a_comp_cor_171, a_comp_cor_172, a_comp_cor_173, a_comp_cor_174, a_comp_cor_175, a_comp_cor_176, a_comp_cor_177, a_comp_cor_178, a_comp_cor_179, a_comp_cor_180, a_comp_cor_181, a_comp_cor_182, a_comp_cor_183, a_comp_cor_184, a_comp_cor_185, a_comp_cor_186, a_comp_cor_187, a_comp_cor_188, a_comp_cor_189, a_comp_cor_190, a_comp_cor_191, a_comp_cor_192, a_comp_cor_193, a_comp_cor_194, a_comp_cor_195, a_comp_cor_196, a_comp_cor_197, a_comp_cor_198, a_comp_cor_199, a_comp_cor_200, a_comp_cor_201, a_comp_cor_202, a_comp_cor_203, a_comp_cor_204, a_comp_cor_205, a_comp_cor_206, a_comp_cor_207, a_comp_cor_208, a_comp_cor_209, a_comp_cor_210, a_comp_cor_211, a_comp_cor_212, a_comp_cor_213, a_comp_cor_214, a_comp_cor_215, a_comp_cor_216, a_comp_cor_217, a_comp_cor_218, a_comp_cor_219, a_comp_cor_220, a_comp_cor_221, a_comp_cor_222, a_comp_cor_223, a_comp_cor_224, a_comp_cor_225, a_comp_cor_226, a_comp_cor_227, a_comp_cor_228, a_comp_cor_229, a_comp_cor_230, a_comp_cor_231, a_comp_cor_232, a_comp_cor_233, a_comp_cor_234, a_comp_cor_235, a_comp_cor_236, a_comp_cor_237, a_comp_cor_238, a_comp_cor_239, a_comp_cor_240, a_comp_cor_241, a_comp_cor_242, a_comp_cor_243, a_comp_cor_244, a_comp_cor_245, a_comp_cor_246, a_comp_cor_247, a_comp_cor_248, a_comp_cor_249, a_comp_cor_250, a_comp_cor_251, a_comp_cor_252, a_comp_cor_253, a_comp_cor_254, a_comp_cor_255, a_comp_cor_256, a_comp_cor_257, a_comp_cor_258, a_comp_cor_259, a_comp_cor_260, a_comp_cor_261, a_comp_cor_262, a_comp_cor_263, a_comp_cor_264, a_comp_cor_265, a_comp_cor_266, a_comp_cor_267, a_comp_cor_268, a_comp_cor_269, a_comp_cor_270, a_comp_cor_271, a_comp_cor_272, a_comp_cor_273, a_comp_cor_274, a_comp_cor_275, a_comp_cor_276, a_comp_cor_277, a_comp_cor_278, a_comp_cor_279, a_comp_cor_280, a_comp_cor_281, a_comp_cor_282, a_comp_cor_283, a_comp_cor_284, a_comp_cor_285, a_comp_cor_286, a_comp_cor_287, a_comp_cor_288, a_comp_cor_289, a_comp_cor_290, a_comp_cor_291, a_comp_cor_292, a_comp_cor_293, a_comp_cor_294, a_comp_cor_295, a_comp_cor_296, a_comp_cor_297, a_comp_cor_298, a_comp_cor_299, a_comp_cor_300, a_comp_cor_301, a_comp_cor_302, a_comp_cor_303, a_comp_cor_304, a_comp_cor_305, a_comp_cor_306, a_comp_cor_307, a_comp_cor_308, a_comp_cor_309, a_comp_cor_310, a_comp_cor_311, a_comp_cor_312, a_comp_cor_313, a_comp_cor_314, a_comp_cor_315, a_comp_cor_316, a_comp_cor_317, a_comp_cor_318, a_comp_cor_319, a_comp_cor_320, a_comp_cor_321, a_comp_cor_322, a_comp_cor_323, a_comp_cor_324, a_comp_cor_325, a_comp_cor_326, a_comp_cor_327, a_comp_cor_328, a_comp_cor_329, a_comp_cor_330, a_comp_cor_331, a_comp_cor_332, a_comp_cor_333, a_comp_cor_334, a_comp_cor_335, a_comp_cor_336, a_comp_cor_337, a_comp_cor_338, a_comp_cor_339, a_comp_cor_340, a_comp_cor_341, a_comp_cor_342, a_comp_cor_343, a_comp_cor_344, a_comp_cor_345, a_comp_cor_346, a_comp_cor_347, a_comp_cor_348, a_comp_cor_349, a_comp_cor_350, a_comp_cor_351, a_comp_cor_352, a_comp_cor_353, a_comp_cor_354, a_comp_cor_355, a_comp_cor_356, a_comp_cor_357, a_comp_cor_358, a_comp_cor_359, a_comp_cor_360, a_comp_cor_361, a_comp_cor_362, a_comp_cor_363, a_comp_cor_364, a_comp_cor_365, a_comp_cor_366, a_comp_cor_367, a_comp_cor_368, a_comp_cor_369, a_comp_cor_370, a_comp_cor_371, a_comp_cor_372, a_comp_cor_373, a_comp_cor_374, a_comp_cor_375, a_comp_cor_376, a_comp_cor_377, a_comp_cor_378, a_comp_cor_379, a_comp_cor_380, a_comp_cor_381, a_comp_cor_382, a_comp_cor_383, a_comp_cor_384, a_comp_cor_385, a_comp_cor_386, a_comp_cor_387, a_comp_cor_388, a_comp_cor_389, a_comp_cor_390, a_comp_cor_391, a_comp_cor_392, a_comp_cor_393, a_comp_cor_394, a_comp_cor_395, a_comp_cor_396, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, cosine08, cosine09, cosine10, cosine11, cosine12, cosine13, cosine14, cosine15, cosine16, cosine17, cosine18, cosine19, cosine20, cosine21, cosine22, non_steady_state_outlier00, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_power2, trans_y_derivative1_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17</li>
    428. 

  428. 			<li>Non-steady-state volumes: 1</li>
    429. 

  429. 		</ul>
    430. 

  430.         </div>
    431. 

  431.         <div id="datatype-func_desc-flirtbbr_run-None_suffix-bold_task-sherlockPart2">
    432. 

  432. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-sherlockPart2_desc-flirtbbr_bold.svg">
    433. 

  433. Problem loading figure sub-15/figures/sub-15_task-sherlockPart2_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    434. 

  434. </div>
    435. 

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

  436.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart2_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart2_desc-flirtbbr_bold.svg</a>
    437. 

  437. </div>
    438. 

  438. 

  439.         </div>
    440. 

  440.         <div id="datatype-func_desc-rois_run-None_suffix-bold_task-sherlockPart2">
    441. 

  441. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-sherlockPart2_desc-rois_bold.svg" style="width: 100%" />
    442. 

  442. </div>
    443. 

  443. <div class="elem-filename">
    444. 

  444.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart2_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart2_desc-rois_bold.svg</a>
    445. 

  445. </div>
    446. 

  446. 

  447.         </div>
    448. 

  448.         <div id="datatype-func_desc-compcorvar_run-None_suffix-bold_task-sherlockPart2">
    449. 

  449. <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-15/figures/sub-15_task-sherlockPart2_desc-compcorvar_bold.svg" style="width: 100%" />
    450. 

  450. </div>
    451. 

  451. <div class="elem-filename">
    452. 

  452.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart2_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart2_desc-compcorvar_bold.svg</a>
    453. 

  453. </div>
    454. 

  454. 

  455.         </div>
    456. 

  456.         <div id="datatype-func_desc-confoundcorr_run-None_suffix-bold_task-sherlockPart2">
    457. 

  457. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    458. 

  458. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    459. 

  459. Right: magnitude of the correlation between each confound time series and the
    460. 

  460. mean global signal. Strong correlations might be indicative of partial volume
    461. 

  461. effects and can inform decisions about feature orthogonalization prior to
    462. 

  462. confound regression.
    463. 

  463. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-sherlockPart2_desc-confoundcorr_bold.svg" style="width: 100%" />
    464. 

  464. </div>
    465. 

  465. <div class="elem-filename">
    466. 

  466.     Get figure file: <a href="./sub-15/figures/sub-15_task-sherlockPart2_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-sherlockPart2_desc-confoundcorr_bold.svg</a>
    467. 

  467. </div>
    468. 

  468. 

  469.         </div>
    470. 

  470.         <div id="datatype-func_desc-summary_run-1_suffix-bold_task-stopsignal">
    471. 

  471. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">stopsignal</span>, run <span class="bids-entity">1</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    472. 

  472. 		<ul class="elem-desc">
    473. 

  473. 			<li>Repetition time (TR): 2s</li>
    474. 

  474. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    475. 

  475. 			<li>Slice timing correction: Not applied</li>
    476. 

  476. 			<li>Susceptibility distortion correction: None</li>
    477. 

  477. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    478. 

  478. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, a_comp_cor_135, a_comp_cor_136, a_comp_cor_137, a_comp_cor_138, a_comp_cor_139, a_comp_cor_140, a_comp_cor_141, 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_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02</li>
    479. 

  479. 			<li>Non-steady-state volumes: 1</li>
    480. 

  480. 		</ul>
    481. 

  481.         </div>
    482. 

  482.         <div id="datatype-func_desc-flirtbbr_run-1_suffix-bold_task-stopsignal">
    483. 

  483. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-flirtbbr_bold.svg">
    484. 

  484. Problem loading figure sub-15/figures/sub-15_task-stopsignal_run-1_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    485. 

  485. </div>
    486. 

  486. <div class="elem-filename">
    487. 

  487.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-1_desc-flirtbbr_bold.svg</a>
    488. 

  488. </div>
    489. 

  489. 

  490.         </div>
    491. 

  491.         <div id="datatype-func_desc-rois_run-1_suffix-bold_task-stopsignal">
    492. 

  492. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-rois_bold.svg" style="width: 100%" />
    493. 

  493. </div>
    494. 

  494. <div class="elem-filename">
    495. 

  495.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-1_desc-rois_bold.svg</a>
    496. 

  496. </div>
    497. 

  497. 

  498.         </div>
    499. 

  499.         <div id="datatype-func_desc-compcorvar_run-1_suffix-bold_task-stopsignal">
    500. 

  500. <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-15/figures/sub-15_task-stopsignal_run-1_desc-compcorvar_bold.svg" style="width: 100%" />
    501. 

  501. </div>
    502. 

  502. <div class="elem-filename">
    503. 

  503.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-1_desc-compcorvar_bold.svg</a>
    504. 

  504. </div>
    505. 

  505. 

  506.         </div>
    507. 

  507.         <div id="datatype-func_desc-carpetplot_run-1_suffix-bold_task-stopsignal">
    508. 

  508. <h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-carpetplot_bold.svg" style="width: 100%" />
    509. 

  509. </div>
    510. 

  510. <div class="elem-filename">
    511. 

  511.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-carpetplot_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-1_desc-carpetplot_bold.svg</a>
    512. 

  512. </div>
    513. 

  513. 

  514.         </div>
    515. 

  515.         <div id="datatype-func_desc-confoundcorr_run-1_suffix-bold_task-stopsignal">
    516. 

  516. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    517. 

  517. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    518. 

  518. Right: magnitude of the correlation between each confound time series and the
    519. 

  519. mean global signal. Strong correlations might be indicative of partial volume
    520. 

  520. effects and can inform decisions about feature orthogonalization prior to
    521. 

  521. confound regression.
    522. 

  522. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-confoundcorr_bold.svg" style="width: 100%" />
    523. 

  523. </div>
    524. 

  524. <div class="elem-filename">
    525. 

  525.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-1_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-1_desc-confoundcorr_bold.svg</a>
    526. 

  526. </div>
    527. 

  527. 

  528.         </div>
    529. 

  529.         <div id="datatype-func_desc-summary_run-2_suffix-bold_task-stopsignal">
    530. 

  530. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">stopsignal</span>, run <span class="bids-entity">2</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    531. 

  531. 		<ul class="elem-desc">
    532. 

  532. 			<li>Repetition time (TR): 2s</li>
    533. 

  533. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    534. 

  534. 			<li>Slice timing correction: Not applied</li>
    535. 

  535. 			<li>Susceptibility distortion correction: None</li>
    536. 

  536. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    537. 

  537. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, cosine00, cosine01, cosine02, cosine03, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04</li>
    538. 

  538. 			<li>Non-steady-state volumes: 0</li>
    539. 

  539. 		</ul>
    540. 

  540.         </div>
    541. 

  541.         <div id="datatype-func_desc-flirtbbr_run-2_suffix-bold_task-stopsignal">
    542. 

  542. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-flirtbbr_bold.svg">
    543. 

  543. Problem loading figure sub-15/figures/sub-15_task-stopsignal_run-2_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    544. 

  544. </div>
    545. 

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

  546.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-2_desc-flirtbbr_bold.svg</a>
    547. 

  547. </div>
    548. 

  548. 

  549.         </div>
    550. 

  550.         <div id="datatype-func_desc-rois_run-2_suffix-bold_task-stopsignal">
    551. 

  551. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-rois_bold.svg" style="width: 100%" />
    552. 

  552. </div>
    553. 

  553. <div class="elem-filename">
    554. 

  554.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-2_desc-rois_bold.svg</a>
    555. 

  555. </div>
    556. 

  556. 

  557.         </div>
    558. 

  558.         <div id="datatype-func_desc-compcorvar_run-2_suffix-bold_task-stopsignal">
    559. 

  559. <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-15/figures/sub-15_task-stopsignal_run-2_desc-compcorvar_bold.svg" style="width: 100%" />
    560. 

  560. </div>
    561. 

  561. <div class="elem-filename">
    562. 

  562.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-2_desc-compcorvar_bold.svg</a>
    563. 

  563. </div>
    564. 

  564. 

  565.         </div>
    566. 

  566.         <div id="datatype-func_desc-carpetplot_run-2_suffix-bold_task-stopsignal">
    567. 

  567. <h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-carpetplot_bold.svg" style="width: 100%" />
    568. 

  568. </div>
    569. 

  569. <div class="elem-filename">
    570. 

  570.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-carpetplot_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-2_desc-carpetplot_bold.svg</a>
    571. 

  571. </div>
    572. 

  572. 

  573.         </div>
    574. 

  574.         <div id="datatype-func_desc-confoundcorr_run-2_suffix-bold_task-stopsignal">
    575. 

  575. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    576. 

  576. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    577. 

  577. Right: magnitude of the correlation between each confound time series and the
    578. 

  578. mean global signal. Strong correlations might be indicative of partial volume
    579. 

  579. effects and can inform decisions about feature orthogonalization prior to
    580. 

  580. confound regression.
    581. 

  581. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-confoundcorr_bold.svg" style="width: 100%" />
    582. 

  582. </div>
    583. 

  583. <div class="elem-filename">
    584. 

  584.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-2_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-2_desc-confoundcorr_bold.svg</a>
    585. 

  585. </div>
    586. 

  586. 

  587.         </div>
    588. 

  588.         <div id="datatype-func_desc-summary_run-3_suffix-bold_task-stopsignal">
    589. 

  589. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">stopsignal</span>, run <span class="bids-entity">3</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    590. 

  590. 		<ul class="elem-desc">
    591. 

  591. 			<li>Repetition time (TR): 2s</li>
    592. 

  592. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    593. 

  593. 			<li>Slice timing correction: Not applied</li>
    594. 

  594. 			<li>Susceptibility distortion correction: None</li>
    595. 

  595. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    596. 

  596. 			<li>Confounds collected: csf, csf_derivative1, csf_power2, csf_derivative1_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, t_comp_cor_06, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, a_comp_cor_06, a_comp_cor_07, a_comp_cor_08, a_comp_cor_09, a_comp_cor_10, a_comp_cor_11, a_comp_cor_12, a_comp_cor_13, a_comp_cor_14, a_comp_cor_15, a_comp_cor_16, a_comp_cor_17, a_comp_cor_18, a_comp_cor_19, a_comp_cor_20, a_comp_cor_21, a_comp_cor_22, a_comp_cor_23, a_comp_cor_24, a_comp_cor_25, a_comp_cor_26, a_comp_cor_27, a_comp_cor_28, a_comp_cor_29, a_comp_cor_30, a_comp_cor_31, a_comp_cor_32, a_comp_cor_33, a_comp_cor_34, a_comp_cor_35, a_comp_cor_36, a_comp_cor_37, a_comp_cor_38, a_comp_cor_39, a_comp_cor_40, a_comp_cor_41, a_comp_cor_42, a_comp_cor_43, a_comp_cor_44, a_comp_cor_45, a_comp_cor_46, a_comp_cor_47, a_comp_cor_48, a_comp_cor_49, a_comp_cor_50, a_comp_cor_51, a_comp_cor_52, a_comp_cor_53, a_comp_cor_54, a_comp_cor_55, a_comp_cor_56, a_comp_cor_57, a_comp_cor_58, a_comp_cor_59, a_comp_cor_60, a_comp_cor_61, a_comp_cor_62, a_comp_cor_63, a_comp_cor_64, a_comp_cor_65, a_comp_cor_66, a_comp_cor_67, a_comp_cor_68, a_comp_cor_69, a_comp_cor_70, a_comp_cor_71, a_comp_cor_72, a_comp_cor_73, a_comp_cor_74, a_comp_cor_75, a_comp_cor_76, a_comp_cor_77, a_comp_cor_78, a_comp_cor_79, a_comp_cor_80, a_comp_cor_81, a_comp_cor_82, a_comp_cor_83, a_comp_cor_84, a_comp_cor_85, a_comp_cor_86, a_comp_cor_87, a_comp_cor_88, a_comp_cor_89, a_comp_cor_90, a_comp_cor_91, a_comp_cor_92, a_comp_cor_93, a_comp_cor_94, a_comp_cor_95, a_comp_cor_96, a_comp_cor_97, a_comp_cor_98, a_comp_cor_99, a_comp_cor_100, a_comp_cor_101, a_comp_cor_102, a_comp_cor_103, a_comp_cor_104, a_comp_cor_105, a_comp_cor_106, a_comp_cor_107, a_comp_cor_108, a_comp_cor_109, a_comp_cor_110, a_comp_cor_111, a_comp_cor_112, a_comp_cor_113, a_comp_cor_114, a_comp_cor_115, a_comp_cor_116, a_comp_cor_117, a_comp_cor_118, a_comp_cor_119, a_comp_cor_120, a_comp_cor_121, a_comp_cor_122, a_comp_cor_123, a_comp_cor_124, a_comp_cor_125, a_comp_cor_126, a_comp_cor_127, a_comp_cor_128, a_comp_cor_129, a_comp_cor_130, a_comp_cor_131, a_comp_cor_132, a_comp_cor_133, a_comp_cor_134, cosine00, cosine01, cosine02, cosine03, non_steady_state_outlier00, non_steady_state_outlier01, non_steady_state_outlier02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_derivative1_power2, rot_x_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_derivative1_power2, rot_z_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06</li>
    597. 

  597. 			<li>Non-steady-state volumes: 3</li>
    598. 

  598. 		</ul>
    599. 

  599.         </div>
    600. 

  600.         <div id="datatype-func_desc-flirtbbr_run-3_suffix-bold_task-stopsignal">
    601. 

  601. <h3 class="run-title">Alignment of functional and anatomical MRI data (surface driven)</h3><p class="elem-caption">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR. Note that Nearest Neighbor interpolation is used in the reportlets in order to highlight potential spin-history and other artifacts, whereas final images are resampled using Lanczos interpolation.</p>                    <object class="svg-reportlet" type="image/svg+xml" data="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-flirtbbr_bold.svg">
    602. 

  602. Problem loading figure sub-15/figures/sub-15_task-stopsignal_run-3_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    603. 

  603. </div>
    604. 

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

  605.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-flirtbbr_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-3_desc-flirtbbr_bold.svg</a>
    606. 

  606. </div>
    607. 

  607. 

  608.         </div>
    609. 

  609.         <div id="datatype-func_desc-rois_run-3_suffix-bold_task-stopsignal">
    610. 

  610. <h3 class="run-title">Brain mask and (temporal/anatomical) CompCor ROIs</h3><p class="elem-caption">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-rois_bold.svg" style="width: 100%" />
    611. 

  611. </div>
    612. 

  612. <div class="elem-filename">
    613. 

  613.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-rois_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-3_desc-rois_bold.svg</a>
    614. 

  614. </div>
    615. 

  615. 

  616.         </div>
    617. 

  617.         <div id="datatype-func_desc-compcorvar_run-3_suffix-bold_task-stopsignal">
    618. 

  618. <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-15/figures/sub-15_task-stopsignal_run-3_desc-compcorvar_bold.svg" style="width: 100%" />
    619. 

  619. </div>
    620. 

  620. <div class="elem-filename">
    621. 

  621.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-compcorvar_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-3_desc-compcorvar_bold.svg</a>
    622. 

  622. </div>
    623. 

  623. 

  624.         </div>
    625. 

  625.         <div id="datatype-func_desc-carpetplot_run-3_suffix-bold_task-stopsignal">
    626. 

  626. <h3 class="run-title">BOLD Summary</h3><p class="elem-caption">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.</p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-carpetplot_bold.svg" style="width: 100%" />
    627. 

  627. </div>
    628. 

  628. <div class="elem-filename">
    629. 

  629.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-carpetplot_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-3_desc-carpetplot_bold.svg</a>
    630. 

  630. </div>
    631. 

  631. 

  632.         </div>
    633. 

  633.         <div id="datatype-func_desc-confoundcorr_run-3_suffix-bold_task-stopsignal">
    634. 

  634. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    635. 

  635. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    636. 

  636. Right: magnitude of the correlation between each confound time series and the
    637. 

  637. mean global signal. Strong correlations might be indicative of partial volume
    638. 

  638. effects and can inform decisions about feature orthogonalization prior to
    639. 

  639. confound regression.
    640. 

  640. </p>                    <img class="svg-reportlet" src="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-confoundcorr_bold.svg" style="width: 100%" />
    641. 

  641. </div>
    642. 

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

  643.     Get figure file: <a href="./sub-15/figures/sub-15_task-stopsignal_run-3_desc-confoundcorr_bold.svg" target="_blank">sub-15/figures/sub-15_task-stopsignal_run-3_desc-confoundcorr_bold.svg</a>
    644. 

  644. </div>
    645. 

  645. 

  646.         </div>
    647. 

  647.     </div>
    648. 

  648.     <div id="About">
    649. 

  649.     <h1 class="sub-report-title">About</h1>
    650. 

  650.         <div id="datatype-anat_desc-about_suffix-T1w">
    651. 

  651.                     <ul>
    652. 

  652. 		<li>fMRIPrep version: 20.0.6</li>
    653. 

  653. 		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/sherlock /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/sherlock/derivatives --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Resources/fmriprep/license.txt participant --participant-label sub-15 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
    654. 

  654. 		<li>Date preprocessed: 2020-05-18 14:31:04 -0400</li>
    655. 

  655. 	</ul>
    656. 

  656. </div>
    657. 

  657.         </div>
    658. 

  658.     </div>
    659. 

  659. 

  660. <div id="boilerplate">
    661. 

  661.     <h1 class="sub-report-title">Methods</h1>
    662. 

  662.     <p>We kindly ask to report results preprocessed with this tool using the following
    663. 

  663.        boilerplate.</p>
    664. 

  664.     <ul class="nav nav-tabs" id="myTab" role="tablist">
    665. 

  665.         <li class="nav-item">
    666. 

  666.             <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
    667. 

  667.         </li>
    668. 

  668.         <li class="nav-item">
    669. 

  669.             <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
    670. 

  670.         </li>
    671. 

  671.         <li class="nav-item">
    672. 

  672.             <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
    673. 

  673.         </li>
    674. 

  674.     </ul>
    675. 

  675.     <div class="tab-content" id="myTabContent">
    676. 

  676.       <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPrep</em> 20.0.6 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.4.2 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
    677. 

  677. <dl>
    678. 

  678. <dt>Anatomical data preprocessing</dt>
    679. 

  679. <dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) with <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010)</span>, distributed with ANTs 2.2.0 <span class="citation" data-cites="ants">(Avants et al. 2008, RRID:SCR_004757)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped with a <em>Nipype</em> implementation of the <code>antsBrainExtraction.sh</code> workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through nonlinear registration with <code>antsRegistration</code> (ANTs 2.2.0), using brain-extracted versions of both T1w reference and the T1w template. The following template was selected for spatial normalization: <em>ICBM 152 Nonlinear Asymmetrical template version 2009c</em> [<span class="citation" data-cites="mni152nlin2009casym">Fonov et al. (2009)</span>, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],</p>
    680. 

  680. </dd>
    681. 

  681. <dt>Functional data preprocessing</dt>
    682. 

  682. <dd><p>For each of the 3 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Susceptibility distortion correction (SDC) was omitted. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying the transforms to correct for head-motion. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled into standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). Components are also calculated separately within the WM and CSF masks. For each CompCor decomposition, the <em>k</em> components with the largest singular values are retained, such that the retained components’ time series are sufficient to explain 50 percent of variance across the nuisance mask (CSF, WM, combined, or temporal). The remaining components are dropped from consideration. The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. The confound time series derived from head motion estimates and global signals were expanded with the inclusion of temporal derivatives and quadratic terms for each <span class="citation" data-cites="confounds_satterthwaite_2013">(Satterthwaite et al. 2013)</span>. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS were annotated as motion outliers. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and output spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
    683. 

  683. </dd>
    684. 

  684. </dl>
    685. 

  685. <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>
    686. 

  686. <h3 id="copyright-waiver">Copyright Waiver</h3>
    687. 

  687. <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>
    688. 

  688. <h3 id="references" class="unnumbered">References</h3>
    689. 

  689. <div id="refs" class="references">
    690. 

  690. <div id="ref-nilearn">
    691. 

  691. <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>
    692. 

  692. </div>
    693. 

  693. <div id="ref-ants">
    694. 

  694. <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>
    695. 

  695. </div>
    696. 

  696. <div id="ref-compcor">
    697. 

  697. <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>
    698. 

  698. </div>
    699. 

  699. <div id="ref-fmriprep2">
    700. 

  700. <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>
    701. 

  701. </div>
    702. 

  702. <div id="ref-fmriprep1">
    703. 

  703. <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>
    704. 

  704. </div>
    705. 

  705. <div id="ref-mni152nlin2009casym">
    706. 

  706. <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>
    707. 

  707. </div>
    708. 

  708. <div id="ref-nipype1">
    709. 

  709. <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>
    710. 

  710. </div>
    711. 

  711. <div id="ref-nipype2">
    712. 

  712. <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>
    713. 

  713. </div>
    714. 

  714. <div id="ref-bbr">
    715. 

  715. <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>
    716. 

  716. </div>
    717. 

  717. <div id="ref-mcflirt">
    718. 

  718. <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>
    719. 

  719. </div>
    720. 

  720. <div id="ref-flirt">
    721. 

  721. <p>Jenkinson, Mark, and Stephen Smith. 2001. “A Global Optimisation Method for Robust Affine Registration of Brain Images.” <em>Medical Image Analysis</em> 5 (2): 143–56. <a href="https://doi.org/10.1016/S1361-8415(01)00036-6" class="uri">https://doi.org/10.1016/S1361-8415(01)00036-6</a>.</p>
    722. 

  722. </div>
    723. 

  723. <div id="ref-lanczos">
    724. 

  724. <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>
    725. 

  725. </div>
    726. 

  726. <div id="ref-power_fd_dvars">
    727. 

  727. <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>
    728. 

  728. </div>
    729. 

  729. <div id="ref-confounds_satterthwaite_2013">
    730. 

  730. <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>
    731. 

  731. </div>
    732. 

  732. <div id="ref-n4">
    733. 

  733. <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>
    734. 

  734. </div>
    735. 

  735. <div id="ref-fsl_fast">
    736. 

  736. <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>
    737. 

  737. </div>
    738. 

  738. </div></div></div>
    739. 

  739.       <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
    740. 

  740. Results included in this manuscript come from preprocessing
    741. 

  741. performed using *fMRIPrep* 20.0.6
    742. 

  742. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    743. 

  743. which is based on *Nipype* 1.4.2
    744. 

  744. (@nipype1; @nipype2; RRID:SCR_002502).
    745. 

  745. 

  746. Anatomical data preprocessing
    747. 

  747. 

  748. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
    749. 

  749. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
    750. 

  750. The T1w-reference was then skull-stripped with a *Nipype* implementation of
    751. 

  751. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
    752. 

  752. as target template.
    753. 

  753. Brain tissue segmentation of cerebrospinal fluid (CSF),
    754. 

  754. white-matter (WM) and gray-matter (GM) was performed on
    755. 

  755. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
    756. 

  756. @fsl_fast].
    757. 

  757. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
    758. 

  758. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
    759. 

  759. using brain-extracted versions of both T1w reference and the T1w template.
    760. 

  760. The following template was selected for spatial normalization:
    761. 

  761. *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
    762. 

  762. 

  763. Functional data preprocessing
    764. 

  764. 

  765. : For each of the 3 BOLD runs found per subject (across all
    766. 

  766. tasks and sessions), the following preprocessing was performed.
    767. 

  767. First, a reference volume and its skull-stripped version were generated
    768. 

  768. using a custom methodology of *fMRIPrep*.
    769. 

  769. Susceptibility distortion correction (SDC) was omitted.
    770. 

  770. The BOLD reference was then co-registered to the T1w reference using
    771. 

  771. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
    772. 

  772. cost-function.
    773. 

  773. Co-registration was configured with nine degrees of freedom to account
    774. 

  774. for distortions remaining in the BOLD reference.
    775. 

  775. Head-motion parameters with respect to the BOLD reference
    776. 

  776. (transformation matrices, and six corresponding rotation and translation
    777. 

  777. parameters) are estimated before any spatiotemporal filtering using
    778. 

  778. `mcflirt` [FSL 5.0.9, @mcflirt].
    779. 

  779. The BOLD time-series (including slice-timing correction when applied)
    780. 

  780. were resampled onto their original, native space by applying
    781. 

  781. the transforms to correct for head-motion.
    782. 

  782. These resampled BOLD time-series will be referred to as *preprocessed
    783. 

  783. BOLD in original space*, or just *preprocessed BOLD*.
    784. 

  784. The BOLD time-series were resampled into standard space,
    785. 

  785. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
    786. 

  786. First, a reference volume and its skull-stripped version were generated
    787. 

  787. using a custom methodology of *fMRIPrep*.
    788. 

  788. Several confounding time-series were calculated based on the
    789. 

  789. *preprocessed BOLD*: framewise displacement (FD), DVARS and
    790. 

  790. three region-wise global signals.
    791. 

  791. FD and DVARS are calculated for each functional run, both using their
    792. 

  792. implementations in *Nipype* [following the definitions by @power_fd_dvars].
    793. 

  793. The three global signals are extracted within the CSF, the WM, and
    794. 

  794. the whole-brain masks.
    795. 

  795. Additionally, a set of physiological regressors were extracted to
    796. 

  796. allow for component-based noise correction [*CompCor*, @compcor].
    797. 

  797. Principal components are estimated after high-pass filtering the
    798. 

  798. *preprocessed BOLD* time-series (using a discrete cosine filter with
    799. 

  799. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    800. 

  800. and anatomical (aCompCor).
    801. 

  801. tCompCor components are then calculated from the top 5% variable
    802. 

  802. voxels within a mask covering the subcortical regions.
    803. 

  803. This subcortical mask is obtained by heavily eroding the brain mask,
    804. 

  804. which ensures it does not include cortical GM regions.
    805. 

  805. For aCompCor, components are calculated within the intersection of
    806. 

  806. the aforementioned mask and the union of CSF and WM masks calculated
    807. 

  807. in T1w space, after their projection to the native space of each
    808. 

  808. functional run (using the inverse BOLD-to-T1w transformation). Components
    809. 

  809. are also calculated separately within the WM and CSF masks.
    810. 

  810. For each CompCor decomposition, the *k* components with the largest singular
    811. 

  811. values are retained, such that the retained components' time series are
    812. 

  812. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
    813. 

  813. WM, combined, or temporal). The remaining components are dropped from
    814. 

  814. consideration.
    815. 

  815. The head-motion estimates calculated in the correction step were also
    816. 

  816. placed within the corresponding confounds file.
    817. 

  817. The confound time series derived from head motion estimates and global
    818. 

  818. signals were expanded with the inclusion of temporal derivatives and
    819. 

  819. quadratic terms for each [@confounds_satterthwaite_2013].
    820. 

  820. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    821. 

  821. were annotated as motion outliers.
    822. 

  822. All resamplings can be performed with *a single interpolation
    823. 

  823. step* by composing all the pertinent transformations (i.e. head-motion
    824. 

  824. transform matrices, susceptibility distortion correction when available,
    825. 

  825. and co-registrations to anatomical and output spaces).
    826. 

  826. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
    827. 

  827. configured with Lanczos interpolation to minimize the smoothing
    828. 

  828. effects of other kernels [@lanczos].
    829. 

  829. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
    830. 

  830. (FreeSurfer).
    831. 

  831. 

  832. 

  833. Many internal operations of *fMRIPrep* use
    834. 

  834. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
    835. 

  835. mostly within the functional processing workflow.
    836. 

  836. For more details of the pipeline, see [the section corresponding
    837. 

  837. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
    838. 

  838. 

  839. 

  840. ### Copyright Waiver
    841. 

  841. 

  842. The above boilerplate text was automatically generated by fMRIPrep
    843. 

  843. with the express intention that users should copy and paste this
    844. 

  844. text into their manuscripts *unchanged*.
    845. 

  845. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
    846. 

  846. 

  847. ### References
    848. 

  848. 

  849. </pre>
    850. 

  850. </div>
    851. 

  851.       <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
    852. 

  852. using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
    853. 

  853. RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
    854. 

  854. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
    855. 

  855. 

  856. \begin{description}
    857. 

  857. \item[Anatomical data preprocessing]
    858. 

  858. The T1-weighted (T1w) image was corrected for intensity non-uniformity
    859. 

  859. (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
    860. 

  860. ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
    861. 

  861. throughout the workflow. The T1w-reference was then skull-stripped with
    862. 

  862. a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
    863. 

  863. workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
    864. 

  864. segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
    865. 

  865. gray-matter (GM) was performed on the brain-extracted T1w using
    866. 

  866. \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
    867. 

  867. Volume-based spatial normalization to one standard space
    868. 

  868. (MNI152NLin2009cAsym) was performed through nonlinear registration with
    869. 

  869. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
    870. 

  870. of both T1w reference and the T1w template. The following template was
    871. 

  871. selected for spatial normalization: \emph{ICBM 152 Nonlinear
    872. 

  872. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
    873. 

  873. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
    874. 

  874. \item[Functional data preprocessing]
    875. 

  875. For each of the 3 BOLD runs found per subject (across all tasks and
    876. 

  876. sessions), the following preprocessing was performed. First, a reference
    877. 

  877. volume and its skull-stripped version were generated using a custom
    878. 

  878. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
    879. 

  879. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
    880. 

  880. reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
    881. 

  881. boundary-based registration \citep{bbr} cost-function. Co-registration
    882. 

  882. was configured with nine degrees of freedom to account for distortions
    883. 

  883. remaining in the BOLD reference. Head-motion parameters with respect to
    884. 

  884. the BOLD reference (transformation matrices, and six corresponding
    885. 

  885. rotation and translation parameters) are estimated before any
    886. 

  886. spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
    887. 

  887. 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
    888. 

  888. correction when applied) were resampled onto their original, native
    889. 

  889. space by applying the transforms to correct for head-motion. These
    890. 

  890. resampled BOLD time-series will be referred to as \emph{preprocessed
    891. 

  891. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
    892. 

  892. time-series were resampled into standard space, generating a
    893. 

  893. \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
    894. 

  894. reference volume and its skull-stripped version were generated using a
    895. 

  895. custom methodology of \emph{fMRIPrep}. Several confounding time-series
    896. 

  896. were calculated based on the \emph{preprocessed BOLD}: framewise
    897. 

  897. displacement (FD), DVARS and three region-wise global signals. FD and
    898. 

  898. DVARS are calculated for each functional run, both using their
    899. 

  899. implementations in \emph{Nipype} \citep[following the definitions
    900. 

  900. by][]{power_fd_dvars}. The three global signals are extracted within the
    901. 

  901. CSF, the WM, and the whole-brain masks. Additionally, a set of
    902. 

  902. physiological regressors were extracted to allow for component-based
    903. 

  903. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
    904. 

  904. components are estimated after high-pass filtering the
    905. 

  905. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
    906. 

  906. with 128s cut-off) for the two \emph{CompCor} variants: temporal
    907. 

  907. (tCompCor) and anatomical (aCompCor). tCompCor components are then
    908. 

  908. calculated from the top 5\% variable voxels within a mask covering the
    909. 

  909. subcortical regions. This subcortical mask is obtained by heavily
    910. 

  910. eroding the brain mask, which ensures it does not include cortical GM
    911. 

  911. regions. For aCompCor, components are calculated within the intersection
    912. 

  912. of the aforementioned mask and the union of CSF and WM masks calculated
    913. 

  913. in T1w space, after their projection to the native space of each
    914. 

  914. functional run (using the inverse BOLD-to-T1w transformation).
    915. 

  915. Components are also calculated separately within the WM and CSF masks.
    916. 

  916. For each CompCor decomposition, the \emph{k} components with the largest
    917. 

  917. singular values are retained, such that the retained components' time
    918. 

  918. series are sufficient to explain 50 percent of variance across the
    919. 

  919. nuisance mask (CSF, WM, combined, or temporal). The remaining components
    920. 

  920. are dropped from consideration. The head-motion estimates calculated in
    921. 

  921. the correction step were also placed within the corresponding confounds
    922. 

  922. file. The confound time series derived from head motion estimates and
    923. 

  923. global signals were expanded with the inclusion of temporal derivatives
    924. 

  924. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
    925. 

  925. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    926. 

  926. were annotated as motion outliers. All resamplings can be performed with
    927. 

  927. \emph{a single interpolation step} by composing all the pertinent
    928. 

  928. transformations (i.e.~head-motion transform matrices, susceptibility
    929. 

  929. distortion correction when available, and co-registrations to anatomical
    930. 

  930. and output spaces). Gridded (volumetric) resamplings were performed
    931. 

  931. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
    932. 

  932. interpolation to minimize the smoothing effects of other kernels
    933. 

  933. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
    934. 

  934. \texttt{mri\_vol2surf} (FreeSurfer).
    935. 

  935. \end{description}
    936. 

  936. 

  937. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
    938. 

  938. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
    939. 

  939. processing workflow. For more details of the pipeline, see
    940. 

  940. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
    941. 

  941. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
    942. 

  942. 

  943. \hypertarget{copyright-waiver}{%
    944. 

  944. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
    945. 

  945. 

  946. The above boilerplate text was automatically generated by fMRIPrep with
    947. 

  947. the express intention that users should copy and paste this text into
    948. 

  948. their manuscripts \emph{unchanged}. It is released under the
    949. 

  949. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
    950. 

  950. 

  951. \hypertarget{references}{%
    952. 

  952. \subsubsection{References}\label{references}}
    953. 

  953. 

  954. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
    955. 

  955. <h3>Bibliography</h3>
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  1184. }
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    1192. 

  1192.     pages = {320-341},
    1193. 

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  1208.     volume = 64,
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  1210. }
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  1211. 

  1212. 

  1213. @article{nilearn,
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  1214.     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},
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  1215.     doi = {10.3389/fninf.2014.00014},
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    1246. 

  1246.     url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
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  1248.     year = 2007
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  1249. }
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    1257. 

  1257.     series = {Mapping the Connectome},
    1258. 

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    1259. 

  1259.     url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
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    1277. 

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    1278. 

  1278.     journal = {NMR in Biomedicine},
    1279. 

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    1280. 

  1280.     pages = {171-178},
    1281. 

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    1282. 

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  1284. }
    1285. 

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  1286. @article{posse_t2s,
    1287. 

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    1288. 

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    1289. 

  1289.     journal = {Magnetic Resonance in Medicine},
    1290. 

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    1291. 

  1291.     pages = {87-97},
    1292. 

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    1293. 

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    1294. 

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    1295. 

  1295. }
    1296. 

  1296. </pre>
    1297. 

  1297. </div>
    1298. 

  1298.     </div>
    1299. 

  1299. </div>
    1300. 

  1300. 

  1301. <div id="errors">
    1302. 

  1302.     <h1 class="sub-report-title">Errors</h1>
    1303. 

  1303.     <details>
    1304. 

  1304.         <summary>Node Name: fmriprep_wf.single_subject_15_wf.anat_preproc_wf.anat_norm_wf.std_dseg</summary><br>
    1305. 

  1305.         <div>
    1306. 

  1306.             File: <code>/dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/sherlock/derivatives/fmriprep/sub-15/log/20200518-164829_facec23e-2405-4162-ae93-cf8ec916ff39/crash-20200518-165405-f00275v-std_dseg.a0-f52f3264-525b-4cf1-88b3-6e62c99cd54c.txt</code><br>
    1307. 

  1307.             Working Directory: <code>/dartfs/rc/lab/D/DBIC/cosanlab/work/fmriprep_wf/single_subject_15_wf/anat_preproc_wf/anat_norm_wf/_template_MNI152NLin2009cAsym/std_dseg</code><br>
    1308. 

  1308.             Inputs: <br>
    1309. 

  1309.             <ul>
    1310. 

  1310.                 <li>args: <code><undefined></code></li>
    1311. 

  1311.                 <li>default_value: <code>0.0</code></li>
    1312. 

  1312.                 <li>dimension: <code>3</code></li>
    1313. 

  1313.                 <li>environ: <code>{'NSLOTS': '1'}</code></li>
    1314. 

  1314.                 <li>float: <code>True</code></li>
    1315. 

  1315.                 <li>input_image: <code><undefined></code></li>
    1316. 

  1316.                 <li>input_image_type: <code><undefined></code></li>
    1317. 

  1317.                 <li>interpolation: <code>MultiLabel</code></li>
    1318. 

  1318.                 <li>interpolation_parameters: <code><undefined></code></li>
    1319. 

  1319.                 <li>invert_transform_flags: <code><undefined></code></li>
    1320. 

  1320.                 <li>num_threads: <code>1</code></li>
    1321. 

  1321.                 <li>out_postfix: <code>_trans</code></li>
    1322. 

  1322.                 <li>output_image: <code><undefined></code></li>
    1323. 

  1323.                 <li>print_out_composite_warp_file: <code><undefined></code></li>
    1324. 

  1324.                 <li>reference_image: <code><undefined></code></li>
    1325. 

  1325.                 <li>transforms: <code>['/dartfs/rc/lab/D/DBIC/cosanlab/work/fmriprep_wf/single_subject_15_wf/anat_preproc_wf/anat_norm_wf/_template_MNI152NLin2009cAsym/registration/ants_t1_to_mniComposite.h5']</code></li>
    1326. 

  1326.             </ul>
    1327. 

  1327.             <pre>Traceback (most recent call last):
    1328. 

  1328.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/plugins/multiproc.py", line 67, in run_node
    1329. 

  1329.     result["result"] = node.run(updatehash=updatehash)
    1330. 

  1330.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 443, in run
    1331. 

  1331.     cached, updated = self.is_cached()
    1332. 

  1332.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 332, in is_cached
    1333. 

  1333.     hashed_inputs, hashvalue = self._get_hashval()
    1334. 

  1334.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 538, in _get_hashval
    1335. 

  1335.     self._get_inputs()
    1336. 

  1336.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 580, in _get_inputs
    1337. 

  1337.     outputs = _load_resultfile(results_fname).outputs
    1338. 

  1338.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/utils.py", line 293, in load_resultfile
    1339. 

  1339.     result = loadpkl(results_file)
    1340. 

  1340.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 672, in loadpkl
    1341. 

  1341.     raise e
    1342. 

  1342.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 649, in loadpkl
    1343. 

  1343.     unpkl = pickle.loads(pkl_contents)
    1344. 

  1344.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1440, in __setstate__
    1345. 

  1345.     self.trait_set( trait_change_notify = trait_change_notify, **state )
    1346. 

  1346.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1543, in trait_set
    1347. 

  1347.     setattr( self, name, value )
    1348. 

  1348.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 329, in validate
    1349. 

  1349.     value = super(File, self).validate(objekt, name, value, return_pathlike=True)
    1350. 

  1350.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 134, in validate
    1351. 

  1351.     self.error(objekt, name, str(value))
    1352. 

  1352.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/trait_handlers.py", line 172, in error
    1353. 

  1353.     value )
    1354. 

  1354. traits.trait_errors.TraitError: The 't1w_file' trait of a _TemplateFlowSelectOutputSpec instance must be a pathlike object or string representing an existing file, but a value of '/dartfs-hpc/rc/home/1/f0034g1/.cache/templateflow/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz' <class 'str'> was specified.</pre>
    1355. 

  1355.         </div>
    1356. 

  1356.     </details>
    1357. 

  1357.     <details>
    1358. 

  1358.         <summary>Node Name: fmriprep_wf.single_subject_15_wf.anat_preproc_wf.anat_norm_wf.std_mask</summary><br>
    1359. 

  1359.         <div>
    1360. 

  1360.             File: <code>/dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/sherlock/derivatives/fmriprep/sub-15/log/20200518-164829_facec23e-2405-4162-ae93-cf8ec916ff39/crash-20200518-165405-f00275v-std_mask.a0-879e21bb-f3c3-4f56-9359-b8b793b3dd73.txt</code><br>
    1361. 

  1361.             Working Directory: <code>/dartfs/rc/lab/D/DBIC/cosanlab/work/fmriprep_wf/single_subject_15_wf/anat_preproc_wf/anat_norm_wf/_template_MNI152NLin2009cAsym/std_mask</code><br>
    1362. 

  1362.             Inputs: <br>
    1363. 

  1363.             <ul>
    1364. 

  1364.                 <li>args: <code><undefined></code></li>
    1365. 

  1365.                 <li>default_value: <code>0.0</code></li>
    1366. 

  1366.                 <li>dimension: <code>3</code></li>
    1367. 

  1367.                 <li>environ: <code>{'NSLOTS': '1'}</code></li>
    1368. 

  1368.                 <li>float: <code>True</code></li>
    1369. 

  1369.                 <li>input_image: <code><undefined></code></li>
    1370. 

  1370.                 <li>input_image_type: <code><undefined></code></li>
    1371. 

  1371.                 <li>interpolation: <code>MultiLabel</code></li>
    1372. 

  1372.                 <li>interpolation_parameters: <code><undefined></code></li>
    1373. 

  1373.                 <li>invert_transform_flags: <code><undefined></code></li>
    1374. 

  1374.                 <li>num_threads: <code>1</code></li>
    1375. 

  1375.                 <li>out_postfix: <code>_trans</code></li>
    1376. 

  1376.                 <li>output_image: <code><undefined></code></li>
    1377. 

  1377.                 <li>print_out_composite_warp_file: <code><undefined></code></li>
    1378. 

  1378.                 <li>reference_image: <code><undefined></code></li>
    1379. 

  1379.                 <li>transforms: <code>['/dartfs/rc/lab/D/DBIC/cosanlab/work/fmriprep_wf/single_subject_15_wf/anat_preproc_wf/anat_norm_wf/_template_MNI152NLin2009cAsym/registration/ants_t1_to_mniComposite.h5']</code></li>
    1380. 

  1380.             </ul>
    1381. 

  1381.             <pre>Traceback (most recent call last):
    1382. 

  1382.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/plugins/multiproc.py", line 67, in run_node
    1383. 

  1383.     result["result"] = node.run(updatehash=updatehash)
    1384. 

  1384.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 443, in run
    1385. 

  1385.     cached, updated = self.is_cached()
    1386. 

  1386.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 332, in is_cached
    1387. 

  1387.     hashed_inputs, hashvalue = self._get_hashval()
    1388. 

  1388.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 538, in _get_hashval
    1389. 

  1389.     self._get_inputs()
    1390. 

  1390.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 580, in _get_inputs
    1391. 

  1391.     outputs = _load_resultfile(results_fname).outputs
    1392. 

  1392.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/utils.py", line 293, in load_resultfile
    1393. 

  1393.     result = loadpkl(results_file)
    1394. 

  1394.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 672, in loadpkl
    1395. 

  1395.     raise e
    1396. 

  1396.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 649, in loadpkl
    1397. 

  1397.     unpkl = pickle.loads(pkl_contents)
    1398. 

  1398.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1440, in __setstate__
    1399. 

  1399.     self.trait_set( trait_change_notify = trait_change_notify, **state )
    1400. 

  1400.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1543, in trait_set
    1401. 

  1401.     setattr( self, name, value )
    1402. 

  1402.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 329, in validate
    1403. 

  1403.     value = super(File, self).validate(objekt, name, value, return_pathlike=True)
    1404. 

  1404.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 134, in validate
    1405. 

  1405.     self.error(objekt, name, str(value))
    1406. 

  1406.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/trait_handlers.py", line 172, in error
    1407. 

  1407.     value )
    1408. 

  1408. traits.trait_errors.TraitError: The 't1w_file' trait of a _TemplateFlowSelectOutputSpec instance must be a pathlike object or string representing an existing file, but a value of '/dartfs-hpc/rc/home/1/f0034g1/.cache/templateflow/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz' <class 'str'> was specified.</pre>
    1409. 

  1409.         </div>
    1410. 

  1410.     </details>
    1411. 

  1411.     <details>
    1412. 

  1412.         <summary>Node Name: fmriprep_wf.single_subject_15_wf.anat_preproc_wf.anat_norm_wf.tpl_moving</summary><br>
    1413. 

  1413.         <div>
    1414. 

  1414.             File: <code>/dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/sherlock/derivatives/fmriprep/sub-15/log/20200518-164829_facec23e-2405-4162-ae93-cf8ec916ff39/crash-20200518-165405-f00275v-tpl_moving.a0-14b1ec0c-77f0-4dc0-bb66-659d38a228c8.txt</code><br>
    1415. 

  1415.             Working Directory: <code>/dartfs/rc/lab/D/DBIC/cosanlab/work/fmriprep_wf/single_subject_15_wf/anat_preproc_wf/anat_norm_wf/_template_MNI152NLin2009cAsym/tpl_moving</code><br>
    1416. 

  1416.             Inputs: <br>
    1417. 

  1417.             <ul>
    1418. 

  1418.                 <li>args: <code><undefined></code></li>
    1419. 

  1419.                 <li>default_value: <code>0.0</code></li>
    1420. 

  1420.                 <li>dimension: <code>3</code></li>
    1421. 

  1421.                 <li>environ: <code>{'NSLOTS': '1'}</code></li>
    1422. 

  1422.                 <li>float: <code>True</code></li>
    1423. 

  1423.                 <li>input_image: <code><undefined></code></li>
    1424. 

  1424.                 <li>input_image_type: <code><undefined></code></li>
    1425. 

  1425.                 <li>interpolation: <code>LanczosWindowedSinc</code></li>
    1426. 

  1426.                 <li>interpolation_parameters: <code><undefined></code></li>
    1427. 

  1427.                 <li>invert_transform_flags: <code><undefined></code></li>
    1428. 

  1428.                 <li>num_threads: <code>1</code></li>
    1429. 

  1429.                 <li>out_postfix: <code>_trans</code></li>
    1430. 

  1430.                 <li>output_image: <code><undefined></code></li>
    1431. 

  1431.                 <li>print_out_composite_warp_file: <code><undefined></code></li>
    1432. 

  1432.                 <li>reference_image: <code><undefined></code></li>
    1433. 

  1433.                 <li>transforms: <code>['/dartfs/rc/lab/D/DBIC/cosanlab/work/fmriprep_wf/single_subject_15_wf/anat_preproc_wf/anat_norm_wf/_template_MNI152NLin2009cAsym/registration/ants_t1_to_mniComposite.h5']</code></li>
    1434. 

  1434.             </ul>
    1435. 

  1435.             <pre>Traceback (most recent call last):
    1436. 

  1436.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/plugins/multiproc.py", line 67, in run_node
    1437. 

  1437.     result["result"] = node.run(updatehash=updatehash)
    1438. 

  1438.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 443, in run
    1439. 

  1439.     cached, updated = self.is_cached()
    1440. 

  1440.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 332, in is_cached
    1441. 

  1441.     hashed_inputs, hashvalue = self._get_hashval()
    1442. 

  1442.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 538, in _get_hashval
    1443. 

  1443.     self._get_inputs()
    1444. 

  1444.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 580, in _get_inputs
    1445. 

  1445.     outputs = _load_resultfile(results_fname).outputs
    1446. 

  1446.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/utils.py", line 293, in load_resultfile
    1447. 

  1447.     result = loadpkl(results_file)
    1448. 

  1448.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 672, in loadpkl
    1449. 

  1449.     raise e
    1450. 

  1450.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 649, in loadpkl
    1451. 

  1451.     unpkl = pickle.loads(pkl_contents)
    1452. 

  1452.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1440, in __setstate__
    1453. 

  1453.     self.trait_set( trait_change_notify = trait_change_notify, **state )
    1454. 

  1454.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1543, in trait_set
    1455. 

  1455.     setattr( self, name, value )
    1456. 

  1456.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 329, in validate
    1457. 

  1457.     value = super(File, self).validate(objekt, name, value, return_pathlike=True)
    1458. 

  1458.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 134, in validate
    1459. 

  1459.     self.error(objekt, name, str(value))
    1460. 

  1460.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/trait_handlers.py", line 172, in error
    1461. 

  1461.     value )
    1462. 

  1462. traits.trait_errors.TraitError: The 't1w_file' trait of a _TemplateFlowSelectOutputSpec instance must be a pathlike object or string representing an existing file, but a value of '/dartfs-hpc/rc/home/1/f0034g1/.cache/templateflow/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz' <class 'str'> was specified.</pre>
    1463. 

  1463.         </div>
    1464. 

  1464.     </details>
    1465. 

  1465.     <details>
    1466. 

  1466.         <summary>Node Name: fmriprep_wf.single_subject_15_wf.anat_preproc_wf.anat_norm_wf.std_tpms</summary><br>
    1467. 

  1467.         <div>
    1468. 

  1468.             File: <code>/dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/sherlock/derivatives/fmriprep/sub-15/log/20200518-164829_facec23e-2405-4162-ae93-cf8ec916ff39/crash-20200518-165400-f00275v-std_tpms.a0-1f699f47-600f-466f-85f9-025b59202473.txt</code><br>
    1469. 

  1469.             Working Directory: <code>/dartfs/rc/lab/D/DBIC/cosanlab/work/fmriprep_wf/single_subject_15_wf/anat_preproc_wf/anat_norm_wf/_template_MNI152NLin2009cAsym/std_tpms</code><br>
    1470. 

  1470.             Inputs: <br>
    1471. 

  1471.             <ul>
    1472. 

  1472.                 <li>args: <code><undefined></code></li>
    1473. 

  1473.                 <li>default_value: <code>0.0</code></li>
    1474. 

  1474.                 <li>dimension: <code>3</code></li>
    1475. 

  1475.                 <li>environ: <code>{'NSLOTS': '1'}</code></li>
    1476. 

  1476.                 <li>float: <code>True</code></li>
    1477. 

  1477.                 <li>input_image: <code><undefined></code></li>
    1478. 

  1478.                 <li>input_image_type: <code><undefined></code></li>
    1479. 

  1479.                 <li>interpolation: <code>Gaussian</code></li>
    1480. 

  1480.                 <li>interpolation_parameters: <code><undefined></code></li>
    1481. 

  1481.                 <li>invert_transform_flags: <code><undefined></code></li>
    1482. 

  1482.                 <li>num_threads: <code>1</code></li>
    1483. 

  1483.                 <li>out_postfix: <code>_trans</code></li>
    1484. 

  1484.                 <li>output_image: <code><undefined></code></li>
    1485. 

  1485.                 <li>print_out_composite_warp_file: <code><undefined></code></li>
    1486. 

  1486.                 <li>reference_image: <code><undefined></code></li>
    1487. 

  1487.                 <li>transforms: <code><undefined></code></li>
    1488. 

  1488.             </ul>
    1489. 

  1489.             <pre>Traceback (most recent call last):
    1490. 

  1490.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/plugins/multiproc.py", line 292, in _send_procs_to_workers
    1491. 

  1491.     num_subnodes = self.procs[jobid].num_subnodes()
    1492. 

  1492.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 1309, in num_subnodes
    1493. 

  1493.     self._get_inputs()
    1494. 

  1494.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 1323, in _get_inputs
    1495. 

  1495.     super(MapNode, self)._get_inputs()
    1496. 

  1496.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 580, in _get_inputs
    1497. 

  1497.     outputs = _load_resultfile(results_fname).outputs
    1498. 

  1498.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/utils.py", line 293, in load_resultfile
    1499. 

  1499.     result = loadpkl(results_file)
    1500. 

  1500.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 672, in loadpkl
    1501. 

  1501.     raise e
    1502. 

  1502.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/utils/filemanip.py", line 649, in loadpkl
    1503. 

  1503.     unpkl = pickle.loads(pkl_contents)
    1504. 

  1504.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1440, in __setstate__
    1505. 

  1505.     self.trait_set( trait_change_notify = trait_change_notify, **state )
    1506. 

  1506.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/has_traits.py", line 1543, in trait_set
    1507. 

  1507.     setattr( self, name, value )
    1508. 

  1508.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 329, in validate
    1509. 

  1509.     value = super(File, self).validate(objekt, name, value, return_pathlike=True)
    1510. 

  1510.   File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/traits_extension.py", line 134, in validate
    1511. 

  1511.     self.error(objekt, name, str(value))
    1512. 

  1512.   File "/usr/local/miniconda/lib/python3.7/site-packages/traits/trait_handlers.py", line 172, in error
    1513. 

  1513.     value )
    1514. 

  1514. traits.trait_errors.TraitError: The 't1w_file' trait of a _TemplateFlowSelectOutputSpec instance must be a pathlike object or string representing an existing file, but a value of '/dartfs-hpc/rc/home/1/f0034g1/.cache/templateflow/tpl-MNI152NLin2009cAsym/tpl-MNI152NLin2009cAsym_res-01_T1w.nii.gz' <class 'str'> was specified.</pre>
    1515. 

  1515.         </div>
    1516. 

  1516.     </details>
    1517. 

  1517. </div>
    1518. 

  1518. 

  1519. 

  1520. <script type="text/javascript">
    1521. 

  1521.     function toggle(id) {
    1522. 

  1522.         var element = document.getElementById(id);
    1523. 

  1523.         if(element.style.display == 'block')
    1524. 

  1524.             element.style.display = 'none';
    1525. 

  1525.         else
    1526. 

  1526.             element.style.display = 'block';
    1527. 

  1527.     }
    1528. 

  1528. </script>
    1529. 

  1529. </body>
    1530. 

  1530. </html>
    1531.