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    12. 

  12. .sub-report-title {}
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    49. 

  49. </head>
    50. 

  50. <body>
    51. 

  51. 

  52. 

  53. <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
    54. 

  54. <div class="collapse navbar-collapse">
    55. 

  55.     <ul class="navbar-nav">
    56. 

  56.         <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
    57. 

  57.         <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
    58. 

  58.         <li class="nav-item dropdown">
    59. 

  59.             <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
    60. 

  60.             <div class="dropdown-menu" aria-labelledby="navbarFunctional">
    61. 

  61.                 <a class="dropdown-item" href="#task-story1"> Task: story1</a>
    62. 

  62.                 <a class="dropdown-item" href="#task-story2"> Task: story2</a>
    63. 

  63.                 <a class="dropdown-item" href="#task-story3"> Task: story3</a>
    64. 

  64.             </div>
    65. 

  65.         </li>
    66. 

  66.         <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
    67. 

  67.         <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
    68. 

  68.         <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
    69. 

  69.     </ul>
    70. 

  70. </div>
    71. 

  71. </nav>
    72. 

  72. <noscript>
    73. 

  73.     <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
    74. 

  74. </noscript>
    75. 

  75. 

  76.     <div id="Summary">
    77. 

  77.     <h1 class="sub-report-title">Summary</h1>
    78. 

  78. <ul class="elem-desc">
    79. 

  79. 		<li>Subject ID: sub-tb3964</li>
    80. 

  80. 		<li>Structural images: 1 T1-weighted </li>
    81. 

  81. 		<li>Functional series: 3</li>
    82. 

  82. 		<ul class="elem-desc">
    83. 

  83. 			<li>Task: story1 (1 run)</li>
    84. 

  84. 			<li>Task: story2 (1 run)</li>
    85. 

  85. 			<li>Task: story3 (1 run)</li>
    86. 

  86. 		</ul>
    87. 

  87. 		<li>Resampling targets: MNI152NLin2009cAsym, fsaverage5
    88. 

  88. 		<li>FreeSurfer reconstruction: Not run</li>
    89. 

  89. 	</ul>    </div>
    90. 

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

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

  92. <h3 class="elem-title">Anatomical Conformation</h3>
    93. 

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

  94. 			<li>Input T1w images: 1</li>
    95. 

  95. 			<li>Output orientation: RAS</li>
    96. 

  96. 			<li>Output dimensions: 208x256x256</li>
    97. 

  97. 			<li>Output voxel size: 1mm x 1mm x 1mm</li>
    98. 

  98. 			<li>Discarded images: 0</li>
    99. 

  99. 

  100. 		</ul><h3 class="elem-title">Brain mask and brain tissue segmentation of the T1w</h3><p class="elem-desc">This panel shows the template T1-weighted image (if several T1w images were found), with contours delineating the detected brain mask and brain tissue segmentations.<p><br />                        <div class="elem-image">
    101. 

  101.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_seg_brainmask.svg">filename:sub-tb3964/figures/sub-tb3964_seg_brainmask.svg</object>
    102. 

  102.                         </div>
    103. 

  103.                         <div class="elem-filename">
    104. 

  104.                             Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_seg_brainmask.svg" target="_blank">sub-tb3964/figures/sub-tb3964_seg_brainmask.svg</a>
    105. 

  105.                         </div>
    106. 

  106. <h3 class="elem-title">T1 to MNI registration</h3><p class="elem-desc">Nonlinear mapping of the T1w image into MNI space. Hover on the panel with the mouse to transition between both spaces.<p><br />                        <div class="elem-image">
    107. 

  107.                         <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_t1_2_mni.svg">filename:sub-tb3964/figures/sub-tb3964_t1_2_mni.svg</object>
    108. 

  108.                         </div>
    109. 

  109.                         <div class="elem-filename">
    110. 

  110.                             Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_t1_2_mni.svg" target="_blank">sub-tb3964/figures/sub-tb3964_t1_2_mni.svg</a>
    111. 

  111.                         </div>
    112. 

  112.     </div>
    113. 

  113.     <div id="Functional">
    114. 

  114.     <h1 class="sub-report-title">Functional</h1>
    115. 

  115.             <div id="task-story1">
    116. 

  116.                 <h2 class="run-title">Reports for  Task: story1</h2>
    117. 

  117. <h3 class="elem-title">Summary</h3>
    118. 

  118. 		<ul class="elem-desc">
    119. 

  119. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    120. 

  120. 			<li>Slice timing correction: Not applied</li>
    121. 

  121. 			<li>Susceptibility distortion correction: None</li>
    122. 

  122. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    123. 

  123. 			<li>Functional series resampled to spaces: template, fsaverage5</li>
    124. 

  124. 			<li>Confounds collected: csf, white_matter, global_signal, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z</li>
    125. 

  125. 		</ul><h3 class="elem-title">ROIs in BOLD space</h3><p class="elem-desc">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.<p>                            <div class="elem-image">
    126. 

  126.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story1_rois.svg">
    127. 

  127.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story1_rois.svg. If the link below works, please try reloading the report in your browser.</object>
    128. 

  128.                             </div>
    129. 

  129.                             <div class="elem-filename">
    130. 

  130.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story1_rois.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story1_rois.svg</a>
    131. 

  131.                             </div>
    132. 

  132. <h3 class="elem-title">EPI to T1 registration</h3><p class="elem-desc">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR<p>                            <div class="elem-image">
    133. 

  133.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story1_flirtbbr.svg">
    134. 

  134.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story1_flirtbbr.svg. If the link below works, please try reloading the report in your browser.</object>
    135. 

  135.                             </div>
    136. 

  136.                             <div class="elem-filename">
    137. 

  137.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story1_flirtbbr.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story1_flirtbbr.svg</a>
    138. 

  138.                             </div>
    139. 

  139. <h3 class="elem-title">BOLD Summary</h3><p class="elem-desc">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.<p>                            <div class="elem-image">
    140. 

  140.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story1_carpetplot.svg">
    141. 

  141.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story1_carpetplot.svg. If the link below works, please try reloading the report in your browser.</object>
    142. 

  142.                             </div>
    143. 

  143.                             <div class="elem-filename">
    144. 

  144.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story1_carpetplot.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story1_carpetplot.svg</a>
    145. 

  145.                             </div>
    146. 

  146.             </div>
    147. 

  147.             <div id="task-story2">
    148. 

  148.                 <h2 class="run-title">Reports for  Task: story2</h2>
    149. 

  149. <h3 class="elem-title">Summary</h3>
    150. 

  150. 		<ul class="elem-desc">
    151. 

  151. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    152. 

  152. 			<li>Slice timing correction: Not applied</li>
    153. 

  153. 			<li>Susceptibility distortion correction: None</li>
    154. 

  154. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    155. 

  155. 			<li>Functional series resampled to spaces: template, fsaverage5</li>
    156. 

  156. 			<li>Confounds collected: csf, white_matter, global_signal, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z</li>
    157. 

  157. 		</ul><h3 class="elem-title">ROIs in BOLD space</h3><p class="elem-desc">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.<p>                            <div class="elem-image">
    158. 

  158.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story2_rois.svg">
    159. 

  159.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story2_rois.svg. If the link below works, please try reloading the report in your browser.</object>
    160. 

  160.                             </div>
    161. 

  161.                             <div class="elem-filename">
    162. 

  162.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story2_rois.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story2_rois.svg</a>
    163. 

  163.                             </div>
    164. 

  164. <h3 class="elem-title">EPI to T1 registration</h3><p class="elem-desc">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR<p>                            <div class="elem-image">
    165. 

  165.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story2_flirtbbr.svg">
    166. 

  166.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story2_flirtbbr.svg. If the link below works, please try reloading the report in your browser.</object>
    167. 

  167.                             </div>
    168. 

  168.                             <div class="elem-filename">
    169. 

  169.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story2_flirtbbr.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story2_flirtbbr.svg</a>
    170. 

  170.                             </div>
    171. 

  171. <h3 class="elem-title">BOLD Summary</h3><p class="elem-desc">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.<p>                            <div class="elem-image">
    172. 

  172.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story2_carpetplot.svg">
    173. 

  173.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story2_carpetplot.svg. If the link below works, please try reloading the report in your browser.</object>
    174. 

  174.                             </div>
    175. 

  175.                             <div class="elem-filename">
    176. 

  176.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story2_carpetplot.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story2_carpetplot.svg</a>
    177. 

  177.                             </div>
    178. 

  178.             </div>
    179. 

  179.             <div id="task-story3">
    180. 

  180.                 <h2 class="run-title">Reports for  Task: story3</h2>
    181. 

  181. <h3 class="elem-title">Summary</h3>
    182. 

  182. 		<ul class="elem-desc">
    183. 

  183. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    184. 

  184. 			<li>Slice timing correction: Not applied</li>
    185. 

  185. 			<li>Susceptibility distortion correction: None</li>
    186. 

  186. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    187. 

  187. 			<li>Functional series resampled to spaces: template, fsaverage5</li>
    188. 

  188. 			<li>Confounds collected: csf, white_matter, global_signal, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, t_comp_cor_04, t_comp_cor_05, a_comp_cor_00, a_comp_cor_01, a_comp_cor_02, a_comp_cor_03, a_comp_cor_04, a_comp_cor_05, cosine00, cosine01, cosine02, cosine03, non_steady_state_outlier00, non_steady_state_outlier01, trans_x, trans_y, trans_z, rot_x, rot_y, rot_z</li>
    189. 

  189. 		</ul><h3 class="elem-title">ROIs in BOLD space</h3><p class="elem-desc">Brain mask calculated on the BOLD signal (red contour), along with the masks used for a/tCompCor.<br />The aCompCor mask (magenta contour) is a conservative CSF and white-matter mask for extracting physiological and movement confounds. <br />The fCompCor mask (blue contour) contains the top 5% most variable voxels within a heavily-eroded brain-mask.<p>                            <div class="elem-image">
    190. 

  190.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story3_rois.svg">
    191. 

  191.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story3_rois.svg. If the link below works, please try reloading the report in your browser.</object>
    192. 

  192.                             </div>
    193. 

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

  194.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story3_rois.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story3_rois.svg</a>
    195. 

  195.                             </div>
    196. 

  196. <h3 class="elem-title">EPI to T1 registration</h3><p class="elem-desc">FSL <code>flirt</code> was used to generate transformations from EPI-space to T1w-space - The white matter mask calculated with FSL <code>fast</code> (brain tissue segmentation) was used for BBR<p>                            <div class="elem-image">
    197. 

  197.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story3_flirtbbr.svg">
    198. 

  198.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story3_flirtbbr.svg. If the link below works, please try reloading the report in your browser.</object>
    199. 

  199.                             </div>
    200. 

  200.                             <div class="elem-filename">
    201. 

  201.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story3_flirtbbr.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story3_flirtbbr.svg</a>
    202. 

  202.                             </div>
    203. 

  203. <h3 class="elem-title">BOLD Summary</h3><p class="elem-desc">Summary statistics are plotted, which may reveal trends or artifacts in the BOLD data. Global signals calculated within the whole-brain (GS), within the white-matter (WM) and within cerebro-spinal fluid (CSF) show the mean BOLD signal in their corresponding masks. DVARS and FD show the standardized DVARS and framewise-displacement measures for each time point.<br />A carpet plot shows the time series for all voxels within the brain mask. Voxels are grouped into cortical (blue), and subcortical (orange) gray matter, cerebellum (green) and white matter and CSF (red), indicated by the color map on the left-hand side.<p>                            <div class="elem-image">
    204. 

  204.                             <object class="svg-reportlet" type="image/svg+xml" data="./sub-tb3964/figures/sub-tb3964_task-story3_carpetplot.svg">
    205. 

  205.                             Problem loading figure sub-tb3964/figures/sub-tb3964_task-story3_carpetplot.svg. If the link below works, please try reloading the report in your browser.</object>
    206. 

  206.                             </div>
    207. 

  207.                             <div class="elem-filename">
    208. 

  208.                                 Get figure file: <a href="./sub-tb3964/figures/sub-tb3964_task-story3_carpetplot.svg" target="_blank">sub-tb3964/figures/sub-tb3964_task-story3_carpetplot.svg</a>
    209. 

  209.                             </div>
    210. 

  210.             </div>
    211. 

  211.     </div>
    212. 

  212.     <div id="About">
    213. 

  213.     <h1 class="sub-report-title">About</h1>
    214. 

  214. <ul>
    215. 

  215. 		<li>FMRIPrep version: 1.2.6-1</li>
    216. 

  216. 		<li>FMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /work/04578/delavega/lonestar/datasets/ds001338 /work/04578/delavega/lonestar/fmriprep/out/ds001338 participant --fs-license-file /work/04578/delavega/lonestar/fmriprep/license.txt --fs-no-reconall --participant-label tb3964 tb3929 tb3512 tb4547 tb3240 tb3757 tb3858 tb3592 tb4450 tb3920 tb3810 --nthreads 24 --mem_mb 64000</code></li>
    217. 

  217. 		<li>Date preprocessed: 2019-02-04 21:15:07 +0000</li>
    218. 

  218. 	</ul>
    219. 

  219. </div>    </div>
    220. 

  220. 

  221. <div id="boilerplate">
    222. 

  222.     <h1 class="sub-report-title">Methods</h1>
    223. 

  223.     <p>We kindly ask to report results preprocessed with fMRIPrep using the following
    224. 

  224.        boilerplate</p>
    225. 

  225.     <ul class="nav nav-tabs" id="myTab" role="tablist">
    226. 

  226.         <li class="nav-item">
    227. 

  227.             <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
    228. 

  228.         </li>
    229. 

  229.         <li class="nav-item">
    230. 

  230.             <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
    231. 

  231.         </li>
    232. 

  232.         <li class="nav-item">
    233. 

  233.             <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
    234. 

  234.         </li>
    235. 

  235.     </ul>
    236. 

  236.     <div class="tab-content" id="myTabContent">
    237. 

  237.       <div class="tab-pane fade active show" id="HTML" role="tabpanel" aria-labelledby="HTML-tab"><div class="boiler-html"><p>Results included in this manuscript come from preprocessing performed using <em>fMRIPprep</em> 1.2.6-1 (<span class="citation" data-cites="fmriprep1">Esteban, Markiewicz, et al. (2018)</span>; <span class="citation" data-cites="fmriprep2">Esteban, Blair, et al. (2018)</span>; RRID:SCR_016216), which is based on <em>Nipype</em> 1.1.7 (<span class="citation" data-cites="nipype1">Gorgolewski et al. (2011)</span>; <span class="citation" data-cites="nipype2">Gorgolewski et al. (2018)</span>; RRID:SCR_002502).</p>
    238. 

  238. <dl>
    239. 

  239. <dt>Anatomical data preprocessing</dt>
    240. 

  240. <dd><p>The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU) using <code>N4BiasFieldCorrection</code> <span class="citation" data-cites="n4">(Tustison et al. 2010, ANTs 2.2.0)</span>, and used as T1w-reference throughout the workflow. The T1w-reference was then skull-stripped using <code>antsBrainExtraction.sh</code> (ANTs 2.2.0), using OASIS as target template. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical template version 2009c <span class="citation" data-cites="mni">(Fonov et al. 2009, RRID:SCR_008796)</span> was performed through nonlinear registration with <code>antsRegistration</code> <span class="citation" data-cites="ants">(ANTs 2.2.0, RRID:SCR_004757, Avants et al. 2008)</span>, using brain-extracted versions of both T1w volume and template. Brain tissue segmentation of cerebrospinal fluid (CSF), white-matter (WM) and gray-matter (GM) was performed on the brain-extracted T1w using <code>fast</code> <span class="citation" data-cites="fsl_fast">(FSL 5.0.9, RRID:SCR_002823, Zhang, Brady, and Smith 2001)</span>.</p>
    241. 

  241. </dd>
    242. 

  242. <dt>Functional data preprocessing</dt>
    243. 

  243. <dd><p>For each of the 3 BOLD runs found per subject (across all tasks and sessions), the following preprocessing was performed. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. The BOLD reference was then co-registered to the T1w reference using <code>flirt</code> <span class="citation" data-cites="flirt">(FSL 5.0.9, Jenkinson and Smith 2001)</span> with the boundary-based registration <span class="citation" data-cites="bbr">(Greve and Fischl 2009)</span> cost-function. Co-registration was configured with nine degrees of freedom to account for distortions remaining in the BOLD reference. Head-motion parameters with respect to the BOLD reference (transformation matrices, and six corresponding rotation and translation parameters) are estimated before any spatiotemporal filtering using <code>mcflirt</code> <span class="citation" data-cites="mcflirt">(FSL 5.0.9, Jenkinson et al. 2002)</span>. The BOLD time-series (including slice-timing correction when applied) were resampled onto their original, native space by applying a single, composite transform to correct for head-motion and susceptibility distortions. These resampled BOLD time-series will be referred to as <em>preprocessed BOLD in original space</em>, or just <em>preprocessed BOLD</em>. The BOLD time-series were resampled to MNI152NLin2009cAsym standard space, generating a <em>preprocessed BOLD run in MNI152NLin2009cAsym space</em>. First, a reference volume and its skull-stripped version were generated using a custom methodology of <em>fMRIPrep</em>. Several confounding time-series were calculated based on the <em>preprocessed BOLD</em>: framewise displacement (FD), DVARS and three region-wise global signals. FD and DVARS are calculated for each functional run, both using their implementations in <em>Nipype</em> <span class="citation" data-cites="power_fd_dvars">(following the definitions by Power et al. 2014)</span>. The three global signals are extracted within the CSF, the WM, and the whole-brain masks. Additionally, a set of physiological regressors were extracted to allow for component-based noise correction <span class="citation" data-cites="compcor">(<em>CompCor</em>, Behzadi et al. 2007)</span>. Principal components are estimated after high-pass filtering the <em>preprocessed BOLD</em> time-series (using a discrete cosine filter with 128s cut-off) for the two <em>CompCor</em> variants: temporal (tCompCor) and anatomical (aCompCor). Six tCompCor components are then calculated from the top 5% variable voxels within a mask covering the subcortical regions. This subcortical mask is obtained by heavily eroding the brain mask, which ensures it does not include cortical GM regions. For aCompCor, six components are calculated within the intersection of the aforementioned mask and the union of CSF and WM masks calculated in T1w space, after their projection to the native space of each functional run (using the inverse BOLD-to-T1w transformation). The head-motion estimates calculated in the correction step were also placed within the corresponding confounds file. All resamplings can be performed with <em>a single interpolation step</em> by composing all the pertinent transformations (i.e. head-motion transform matrices, susceptibility distortion correction when available, and co-registrations to anatomical and template spaces). Gridded (volumetric) resamplings were performed using <code>antsApplyTransforms</code> (ANTs), configured with Lanczos interpolation to minimize the smoothing effects of other kernels <span class="citation" data-cites="lanczos">(Lanczos 1964)</span>. Non-gridded (surface) resamplings were performed using <code>mri_vol2surf</code> (FreeSurfer).</p>
    244. 

  244. </dd>
    245. 

  245. </dl>
    246. 

  246. <p>Many internal operations of <em>fMRIPrep</em> use <em>Nilearn</em> 0.5.0 <span class="citation" data-cites="nilearn">(Abraham et al. 2014, RRID:SCR_001362)</span>, mostly within the functional processing workflow. For more details of the pipeline, see <a href="https://fmriprep.readthedocs.io/en/latest/workflows.html" title="FMRIPrep&#39;s documentation">the section corresponding to workflows in <em>fMRIPrep</em>’s documentation</a>.</p>
    247. 

  247. <h3 id="references" class="unnumbered">References</h3>
    248. 

  248. <div id="refs" class="references">
    249. 

  249. <div id="ref-nilearn">
    250. 

  250. <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>
    251. 

  251. </div>
    252. 

  252. <div id="ref-ants">
    253. 

  253. <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>
    254. 

  254. </div>
    255. 

  255. <div id="ref-compcor">
    256. 

  256. <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>
    257. 

  257. </div>
    258. 

  258. <div id="ref-fmriprep2">
    259. 

  259. <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>
    260. 

  260. </div>
    261. 

  261. <div id="ref-fmriprep1">
    262. 

  262. <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>
    263. 

  263. </div>
    264. 

  264. <div id="ref-mni">
    265. 

  265. <p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em>, Organization for human brain mapping 2009 annual meeting, 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
    266. 

  266. </div>
    267. 

  267. <div id="ref-nipype1">
    268. 

  268. <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>
    269. 

  269. </div>
    270. 

  270. <div id="ref-nipype2">
    271. 

  271. <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>
    272. 

  272. </div>
    273. 

  273. <div id="ref-bbr">
    274. 

  274. <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>
    275. 

  275. </div>
    276. 

  276. <div id="ref-mcflirt">
    277. 

  277. <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>
    278. 

  278. </div>
    279. 

  279. <div id="ref-flirt">
    280. 

  280. <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>
    281. 

  281. </div>
    282. 

  282. <div id="ref-lanczos">
    283. 

  283. <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>
    284. 

  284. </div>
    285. 

  285. <div id="ref-power_fd_dvars">
    286. 

  286. <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>
    287. 

  287. </div>
    288. 

  288. <div id="ref-n4">
    289. 

  289. <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>
    290. 

  290. </div>
    291. 

  291. <div id="ref-fsl_fast">
    292. 

  292. <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>
    293. 

  293. </div>
    294. 

  294. </div></div></div>
    295. 

  295.       <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
    296. 

  296. Results included in this manuscript come from preprocessing
    297. 

  297. performed using *fMRIPprep* 1.2.6-1
    298. 

  298. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    299. 

  299. which is based on *Nipype* 1.1.7
    300. 

  300. (@nipype1; @nipype2; RRID:SCR_002502).
    301. 

  301. 

  302. Anatomical data preprocessing
    303. 

  303. 

  304. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
    305. 

  305. using `N4BiasFieldCorrection` [@n4, ANTs 2.2.0],
    306. 

  306. and used as T1w-reference throughout the workflow.
    307. 

  307. The T1w-reference was then skull-stripped using `antsBrainExtraction.sh`
    308. 

  308. (ANTs 2.2.0), using OASIS as target template.
    309. 

  309. Spatial normalization to the ICBM 152 Nonlinear Asymmetrical
    310. 

  310. template version 2009c [@mni, RRID:SCR_008796] was performed
    311. 

  311. through nonlinear registration with `antsRegistration`
    312. 

  312. [ANTs 2.2.0, RRID:SCR_004757, @ants], using
    313. 

  313. brain-extracted versions of both T1w volume and template.
    314. 

  314. Brain tissue segmentation of cerebrospinal fluid (CSF),
    315. 

  315. white-matter (WM) and gray-matter (GM) was performed on
    316. 

  316. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
    317. 

  317. @fsl_fast].
    318. 

  318. 

  319. 

  320. Functional data preprocessing
    321. 

  321. 

  322. : For each of the 3 BOLD runs found per subject (across all
    323. 

  323. tasks and sessions), the following preprocessing was performed.
    324. 

  324. First, a reference volume and its skull-stripped version were generated
    325. 

  325. using a custom methodology of *fMRIPrep*.
    326. 

  326. The BOLD reference was then co-registered to the T1w reference using
    327. 

  327. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
    328. 

  328. cost-function.
    329. 

  329. Co-registration was configured with nine degrees of freedom to account
    330. 

  330. for distortions remaining in the BOLD reference.
    331. 

  331. Head-motion parameters with respect to the BOLD reference
    332. 

  332. (transformation matrices, and six corresponding rotation and translation
    333. 

  333. parameters) are estimated before any spatiotemporal filtering using
    334. 

  334. `mcflirt` [FSL 5.0.9, @mcflirt].
    335. 

  335. The BOLD time-series (including slice-timing correction when applied)
    336. 

  336. were resampled onto their original, native space by applying
    337. 

  337. a single, composite transform to correct for head-motion and
    338. 

  338. susceptibility distortions.
    339. 

  339. These resampled BOLD time-series will be referred to as *preprocessed
    340. 

  340. BOLD in original space*, or just *preprocessed BOLD*.
    341. 

  341. The BOLD time-series were resampled to MNI152NLin2009cAsym standard space,
    342. 

  342. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
    343. 

  343. First, a reference volume and its skull-stripped version were generated
    344. 

  344. using a custom methodology of *fMRIPrep*.
    345. 

  345. Several confounding time-series were calculated based on the
    346. 

  346. *preprocessed BOLD*: framewise displacement (FD), DVARS and
    347. 

  347. three region-wise global signals.
    348. 

  348. FD and DVARS are calculated for each functional run, both using their
    349. 

  349. implementations in *Nipype* [following the definitions by @power_fd_dvars].
    350. 

  350. The three global signals are extracted within the CSF, the WM, and
    351. 

  351. the whole-brain masks.
    352. 

  352. Additionally, a set of physiological regressors were extracted to
    353. 

  353. allow for component-based noise correction [*CompCor*, @compcor].
    354. 

  354. Principal components are estimated after high-pass filtering the
    355. 

  355. *preprocessed BOLD* time-series (using a discrete cosine filter with
    356. 

  356. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    357. 

  357. and anatomical (aCompCor).
    358. 

  358. Six tCompCor components are then calculated from the top 5% variable
    359. 

  359. voxels within a mask covering the subcortical regions.
    360. 

  360. This subcortical mask is obtained by heavily eroding the brain mask,
    361. 

  361. which ensures it does not include cortical GM regions.
    362. 

  362. For aCompCor, six components are calculated within the intersection of
    363. 

  363. the aforementioned mask and the union of CSF and WM masks calculated
    364. 

  364. in T1w space, after their projection to the native space of each
    365. 

  365. functional run (using the inverse BOLD-to-T1w transformation).
    366. 

  366. The head-motion estimates calculated in the correction step were also
    367. 

  367. placed within the corresponding confounds file.
    368. 

  368. All resamplings can be performed with *a single interpolation
    369. 

  369. step* by composing all the pertinent transformations (i.e. head-motion
    370. 

  370. transform matrices, susceptibility distortion correction when available,
    371. 

  371. and co-registrations to anatomical and template spaces).
    372. 

  372. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
    373. 

  373. configured with Lanczos interpolation to minimize the smoothing
    374. 

  374. effects of other kernels [@lanczos].
    375. 

  375. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
    376. 

  376. (FreeSurfer).
    377. 

  377. 

  378. 

  379. Many internal operations of *fMRIPrep* use
    380. 

  380. *Nilearn* 0.5.0 [@nilearn, RRID:SCR_001362],
    381. 

  381. mostly within the functional processing workflow.
    382. 

  382. For more details of the pipeline, see [the section corresponding
    383. 

  383. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
    384. 

  384. 

  385. 

  386. ### References
    387. 

  387. 

  388. </pre>
    389. 

  389. </div>
    390. 

  390.       <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
    391. 

  391. using \emph{fMRIPprep} 1.2.6-1 (\citet{fmriprep1}; \citet{fmriprep2};
    392. 

  392. RRID:SCR\_016216), which is based on \emph{Nipype} 1.1.7
    393. 

  393. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
    394. 

  394. 

  395. \begin{description}
    396. 

  396. \item[Anatomical data preprocessing]
    397. 

  397. The T1-weighted (T1w) image was corrected for intensity non-uniformity
    398. 

  398. (INU) using \texttt{N4BiasFieldCorrection} \citep[ANTs 2.2.0]{n4}, and
    399. 

  399. used as T1w-reference throughout the workflow. The T1w-reference was
    400. 

  400. then skull-stripped using \texttt{antsBrainExtraction.sh} (ANTs 2.2.0),
    401. 

  401. using OASIS as target template. Spatial normalization to the ICBM 152
    402. 

  402. Nonlinear Asymmetrical template version 2009c
    403. 

  403. \citep[RRID:SCR\_008796]{mni} was performed through nonlinear
    404. 

  404. registration with \texttt{antsRegistration} \citep[ANTs 2.2.0,
    405. 

  405. RRID:SCR\_004757,][]{ants}, using brain-extracted versions of both T1w
    406. 

  406. volume and template. Brain tissue segmentation of cerebrospinal fluid
    407. 

  407. (CSF), white-matter (WM) and gray-matter (GM) was performed on the
    408. 

  408. brain-extracted T1w using \texttt{fast} \citep[FSL 5.0.9,
    409. 

  409. RRID:SCR\_002823,][]{fsl_fast}.
    410. 

  410. \item[Functional data preprocessing]
    411. 

  411. For each of the 3 BOLD runs found per subject (across all tasks and
    412. 

  412. sessions), the following preprocessing was performed. First, a reference
    413. 

  413. volume and its skull-stripped version were generated using a custom
    414. 

  414. methodology of \emph{fMRIPrep}. The BOLD reference was then
    415. 

  415. co-registered to the T1w reference using \texttt{flirt} \citep[FSL
    416. 

  416. 5.0.9,][]{flirt} with the boundary-based registration \citep{bbr}
    417. 

  417. cost-function. Co-registration was configured with nine degrees of
    418. 

  418. freedom to account for distortions remaining in the BOLD reference.
    419. 

  419. Head-motion parameters with respect to the BOLD reference
    420. 

  420. (transformation matrices, and six corresponding rotation and translation
    421. 

  421. parameters) are estimated before any spatiotemporal filtering using
    422. 

  422. \texttt{mcflirt} \citep[FSL 5.0.9,][]{mcflirt}. The BOLD time-series
    423. 

  423. (including slice-timing correction when applied) were resampled onto
    424. 

  424. their original, native space by applying a single, composite transform
    425. 

  425. to correct for head-motion and susceptibility distortions. These
    426. 

  426. resampled BOLD time-series will be referred to as \emph{preprocessed
    427. 

  427. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
    428. 

  428. time-series were resampled to MNI152NLin2009cAsym standard space,
    429. 

  429. generating a \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}.
    430. 

  430. First, a reference volume and its skull-stripped version were generated
    431. 

  431. using a custom methodology of \emph{fMRIPrep}. Several confounding
    432. 

  432. time-series were calculated based on the \emph{preprocessed BOLD}:
    433. 

  433. framewise displacement (FD), DVARS and three region-wise global signals.
    434. 

  434. FD and DVARS are calculated for each functional run, both using their
    435. 

  435. implementations in \emph{Nipype} \citep[following the definitions
    436. 

  436. by][]{power_fd_dvars}. The three global signals are extracted within the
    437. 

  437. CSF, the WM, and the whole-brain masks. Additionally, a set of
    438. 

  438. physiological regressors were extracted to allow for component-based
    439. 

  439. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
    440. 

  440. components are estimated after high-pass filtering the
    441. 

  441. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
    442. 

  442. with 128s cut-off) for the two \emph{CompCor} variants: temporal
    443. 

  443. (tCompCor) and anatomical (aCompCor). Six tCompCor components are then
    444. 

  444. calculated from the top 5\% variable voxels within a mask covering the
    445. 

  445. subcortical regions. This subcortical mask is obtained by heavily
    446. 

  446. eroding the brain mask, which ensures it does not include cortical GM
    447. 

  447. regions. For aCompCor, six components are calculated within the
    448. 

  448. intersection of the aforementioned mask and the union of CSF and WM
    449. 

  449. masks calculated in T1w space, after their projection to the native
    450. 

  450. space of each functional run (using the inverse BOLD-to-T1w
    451. 

  451. transformation). The head-motion estimates calculated in the correction
    452. 

  452. step were also placed within the corresponding confounds file. All
    453. 

  453. resamplings can be performed with \emph{a single interpolation step} by
    454. 

  454. composing all the pertinent transformations (i.e.~head-motion transform
    455. 

  455. matrices, susceptibility distortion correction when available, and
    456. 

  456. co-registrations to anatomical and template spaces). Gridded
    457. 

  457. (volumetric) resamplings were performed using
    458. 

  458. \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
    459. 

  459. interpolation to minimize the smoothing effects of other kernels
    460. 

  460. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
    461. 

  461. \texttt{mri\_vol2surf} (FreeSurfer).
    462. 

  462. \end{description}
    463. 

  463. 

  464. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.5.0
    465. 

  465. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
    466. 

  466. processing workflow. For more details of the pipeline, see
    467. 

  467. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
    468. 

  468. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
    469. 

  469. 

  470. \hypertarget{references}{%
    471. 

  471. \subsubsection{References}\label{references}}
    472. 

  472. 

  473. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
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    742. 

  742.     title = {The minimal preprocessing pipelines for the Human Connectome Project},
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  780. </pre>
    781. 

  781. </div>
    782. 

  782.     </div>
    783. 

  783.     <p>Alternatively, an interactive <a href="http://fmriprep.readthedocs.io/en/latest/citing.html">boilerplate generator</a> is available in the <a href="https://fmriprep.org">documentation website</a>.</p>
    784. 

  784. </div>
    785. 

  785. 

  786. <div id="errors">
    787. 

  787.     <h1 class="sub-report-title">Errors</h1>
    788. 

  788.     <ul>
    789. 

  789.         <li>No errors to report!</li>
    790. 

  790.     </ul>
    791. 

  791. </div>
    792. 

  792. 

  793. 

  794. <script type="text/javascript">
    795. 

  795.     function toggle(id) {
    796. 

  796.         var element = document.getElementById(id);
    797. 

  797.         if(element.style.display == 'block')
    798. 

  798.             element.style.display = 'none';
    799. 

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    800. 

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    801. 

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    802. 

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    803. 

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    804. 

  804. </html>
    805.