sub-12.html 99 KB

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  50. <body>
  51. <nav class="navbar fixed-top navbar-expand-lg navbar-light bg-light">
  52. <div class="collapse navbar-collapse">
  53. <ul class="navbar-nav">
  54. <li class="nav-item"><a class="nav-link" href="#Summary">Summary</a></li>
  55. <li class="nav-item"><a class="nav-link" href="#Anatomical">Anatomical</a></li>
  56. <li class="nav-item dropdown">
  57. <a class="nav-link dropdown-toggle" id="navbarFunctional" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false" href="#">Functional</a>
  58. <div class="dropdown-menu" aria-labelledby="navbarFunctional">
  59. <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-freerecall">Reports for: task <span class="bids-entity">freerecall</span>.</a>
  60. <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-sherlockPart1">Reports for: task <span class="bids-entity">sherlockPart1</span>.</a>
  61. <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-sherlockPart2">Reports for: task <span class="bids-entity">sherlockPart2</span>.</a>
  62. </div>
  63. </li>
  64. <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
  65. <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
  66. <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
  67. </ul>
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  70. <noscript>
  71. <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
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  73. <div id="Summary">
  74. <h1 class="sub-report-title">Summary</h1>
  75. <div id="datatype-anat_desc-summary_suffix-T1w">
  76. <ul class="elem-desc">
  77. <li>Subject ID: 12</li>
  78. <li>Structural images: 1 T1-weighted </li>
  79. <li>Functional series: 3</li>
  80. <ul class="elem-desc">
  81. <li>Task: freerecall (1 run)</li>
  82. <li>Task: sherlockPart1 (1 run)</li>
  83. <li>Task: sherlockPart2 (1 run)</li>
  84. </ul>
  85. <li>Standard output spaces: MNI152NLin2009cAsym</li>
  86. <li>Non-standard output spaces: </li>
  87. <li>FreeSurfer reconstruction: Not run</li>
  88. </ul>
  89. </div>
  90. </div>
  91. <div id="Anatomical">
  92. <h1 class="sub-report-title">Anatomical</h1>
  93. <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
  94. <h3 class="elem-title">Anatomical Conformation</h3>
  95. <ul class="elem-desc">
  96. <li>Input T1w images: 1</li>
  97. <li>Output orientation: RAS</li>
  98. <li>Output dimensions: 192x250x216</li>
  99. <li>Output voxel size: 0.9mm x 0.86mm x 0.86mm</li>
  100. <li>Discarded images: 0</li>
  101. </ul>
  102. </div>
  103. <div id="datatype-anat_suffix-dseg">
  104. <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-12/figures/sub-12_dseg.svg" style="width: 100%" />
  105. </div>
  106. <div class="elem-filename">
  107. Get figure file: <a href="./sub-12/figures/sub-12_dseg.svg" target="_blank">sub-12/figures/sub-12_dseg.svg</a>
  108. </div>
  109. </div>
  110. <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
  111. <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-12/figures/sub-12_space-MNI152NLin2009cAsym_T1w.svg">
  112. Problem loading figure sub-12/figures/sub-12_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
  113. </div>
  114. <div class="elem-filename">
  115. Get figure file: <a href="./sub-12/figures/sub-12_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-12/figures/sub-12_space-MNI152NLin2009cAsym_T1w.svg</a>
  116. </div>
  117. </div>
  118. </div>
  119. <div id="Functional">
  120. <h1 class="sub-report-title">Functional</h1>
  121. <div id="datatype-func_desc-summary_suffix-bold_task-freerecall">
  122. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">freerecall</span>.</h2> <h3 class="elem-title">Summary</h3>
  123. <ul class="elem-desc">
  124. <li>Repetition time (TR): 1.5s</li>
  125. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  126. <li>Slice timing correction: Not applied</li>
  127. <li>Susceptibility distortion correction: None</li>
  128. <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
  129. <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_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, 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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, a_comp_cor_397, a_comp_cor_398, a_comp_cor_399, a_comp_cor_400, a_comp_cor_401, a_comp_cor_402, a_comp_cor_403, a_comp_cor_404, a_comp_cor_405, a_comp_cor_406, a_comp_cor_407, a_comp_cor_408, a_comp_cor_409, a_comp_cor_410, a_comp_cor_411, a_comp_cor_412, a_comp_cor_413, a_comp_cor_414, a_comp_cor_415, a_comp_cor_416, a_comp_cor_417, a_comp_cor_418, a_comp_cor_419, a_comp_cor_420, a_comp_cor_421, a_comp_cor_422, a_comp_cor_423, a_comp_cor_424, a_comp_cor_425, a_comp_cor_426, a_comp_cor_427, a_comp_cor_428, a_comp_cor_429, a_comp_cor_430, a_comp_cor_431, a_comp_cor_432, a_comp_cor_433, a_comp_cor_434, a_comp_cor_435, a_comp_cor_436, a_comp_cor_437, a_comp_cor_438, a_comp_cor_439, a_comp_cor_440, a_comp_cor_441, a_comp_cor_442, a_comp_cor_443, a_comp_cor_444, a_comp_cor_445, a_comp_cor_446, a_comp_cor_447, a_comp_cor_448, a_comp_cor_449, a_comp_cor_450, a_comp_cor_451, a_comp_cor_452, a_comp_cor_453, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, cosine08, cosine09, cosine10, cosine11, cosine12, cosine13, cosine14, cosine15, cosine16, cosine17, cosine18, cosine19, cosine20, cosine21, cosine22, cosine23, cosine24, cosine25, cosine26, cosine27, cosine28, cosine29, cosine30, cosine31, cosine32, cosine33, cosine34, cosine35, cosine36, cosine37, cosine38, cosine39, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17, motion_outlier18, 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</li>
  130. <li>Non-steady-state volumes: 0</li>
  131. </ul>
  132. </div>
  133. <div id="datatype-func_desc-validation_suffix-bold_task-freerecall">
  134. <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
  135. <p class="elem-desc">
  136. The qform has been copied from sform.
  137. The difference in angle is -1.9e-06.
  138. The difference in translation is 3.4e-15.
  139. </p>
  140. </div>
  141. <div id="datatype-func_desc-flirtbbr_suffix-bold_task-freerecall">
  142. <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-12/figures/sub-12_task-freerecall_desc-flirtbbr_bold.svg">
  143. Problem loading figure sub-12/figures/sub-12_task-freerecall_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  144. </div>
  145. <div class="elem-filename">
  146. Get figure file: <a href="./sub-12/figures/sub-12_task-freerecall_desc-flirtbbr_bold.svg" target="_blank">sub-12/figures/sub-12_task-freerecall_desc-flirtbbr_bold.svg</a>
  147. </div>
  148. </div>
  149. <div id="datatype-func_desc-rois_suffix-bold_task-freerecall">
  150. <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-12/figures/sub-12_task-freerecall_desc-rois_bold.svg" style="width: 100%" />
  151. </div>
  152. <div class="elem-filename">
  153. Get figure file: <a href="./sub-12/figures/sub-12_task-freerecall_desc-rois_bold.svg" target="_blank">sub-12/figures/sub-12_task-freerecall_desc-rois_bold.svg</a>
  154. </div>
  155. </div>
  156. <div id="datatype-func_desc-compcorvar_suffix-bold_task-freerecall">
  157. <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-12/figures/sub-12_task-freerecall_desc-compcorvar_bold.svg" style="width: 100%" />
  158. </div>
  159. <div class="elem-filename">
  160. Get figure file: <a href="./sub-12/figures/sub-12_task-freerecall_desc-compcorvar_bold.svg" target="_blank">sub-12/figures/sub-12_task-freerecall_desc-compcorvar_bold.svg</a>
  161. </div>
  162. </div>
  163. <div id="datatype-func_desc-carpetplot_suffix-bold_task-freerecall">
  164. <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-12/figures/sub-12_task-freerecall_desc-carpetplot_bold.svg" style="width: 100%" />
  165. </div>
  166. <div class="elem-filename">
  167. Get figure file: <a href="./sub-12/figures/sub-12_task-freerecall_desc-carpetplot_bold.svg" target="_blank">sub-12/figures/sub-12_task-freerecall_desc-carpetplot_bold.svg</a>
  168. </div>
  169. </div>
  170. <div id="datatype-func_desc-confoundcorr_suffix-bold_task-freerecall">
  171. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  172. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  173. Right: magnitude of the correlation between each confound time series and the
  174. mean global signal. Strong correlations might be indicative of partial volume
  175. effects and can inform decisions about feature orthogonalization prior to
  176. confound regression.
  177. </p> <img class="svg-reportlet" src="./sub-12/figures/sub-12_task-freerecall_desc-confoundcorr_bold.svg" style="width: 100%" />
  178. </div>
  179. <div class="elem-filename">
  180. Get figure file: <a href="./sub-12/figures/sub-12_task-freerecall_desc-confoundcorr_bold.svg" target="_blank">sub-12/figures/sub-12_task-freerecall_desc-confoundcorr_bold.svg</a>
  181. </div>
  182. </div>
  183. <div id="datatype-func_desc-summary_suffix-bold_task-sherlockPart1">
  184. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">sherlockPart1</span>.</h2> <h3 class="elem-title">Summary</h3>
  185. <ul class="elem-desc">
  186. <li>Repetition time (TR): 1.5s</li>
  187. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  188. <li>Slice timing correction: Not applied</li>
  189. <li>Susceptibility distortion correction: None</li>
  190. <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
  191. <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_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, t_comp_cor_08, t_comp_cor_09, t_comp_cor_10, t_comp_cor_11, 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, a_comp_cor_397, a_comp_cor_398, a_comp_cor_399, a_comp_cor_400, a_comp_cor_401, a_comp_cor_402, a_comp_cor_403, a_comp_cor_404, a_comp_cor_405, a_comp_cor_406, a_comp_cor_407, a_comp_cor_408, a_comp_cor_409, a_comp_cor_410, a_comp_cor_411, a_comp_cor_412, a_comp_cor_413, a_comp_cor_414, a_comp_cor_415, a_comp_cor_416, a_comp_cor_417, a_comp_cor_418, a_comp_cor_419, a_comp_cor_420, a_comp_cor_421, a_comp_cor_422, a_comp_cor_423, a_comp_cor_424, a_comp_cor_425, a_comp_cor_426, a_comp_cor_427, a_comp_cor_428, a_comp_cor_429, a_comp_cor_430, a_comp_cor_431, a_comp_cor_432, a_comp_cor_433, a_comp_cor_434, a_comp_cor_435, a_comp_cor_436, a_comp_cor_437, a_comp_cor_438, a_comp_cor_439, a_comp_cor_440, a_comp_cor_441, a_comp_cor_442, a_comp_cor_443, a_comp_cor_444, a_comp_cor_445, a_comp_cor_446, a_comp_cor_447, a_comp_cor_448, a_comp_cor_449, a_comp_cor_450, a_comp_cor_451, a_comp_cor_452, a_comp_cor_453, a_comp_cor_454, a_comp_cor_455, a_comp_cor_456, a_comp_cor_457, a_comp_cor_458, a_comp_cor_459, a_comp_cor_460, a_comp_cor_461, a_comp_cor_462, a_comp_cor_463, a_comp_cor_464, a_comp_cor_465, a_comp_cor_466, a_comp_cor_467, a_comp_cor_468, a_comp_cor_469, a_comp_cor_470, a_comp_cor_471, a_comp_cor_472, a_comp_cor_473, a_comp_cor_474, a_comp_cor_475, a_comp_cor_476, a_comp_cor_477, a_comp_cor_478, a_comp_cor_479, a_comp_cor_480, a_comp_cor_481, a_comp_cor_482, a_comp_cor_483, a_comp_cor_484, a_comp_cor_485, a_comp_cor_486, a_comp_cor_487, a_comp_cor_488, a_comp_cor_489, a_comp_cor_490, a_comp_cor_491, a_comp_cor_492, a_comp_cor_493, a_comp_cor_494, a_comp_cor_495, a_comp_cor_496, a_comp_cor_497, a_comp_cor_498, a_comp_cor_499, a_comp_cor_500, a_comp_cor_501, a_comp_cor_502, a_comp_cor_503, a_comp_cor_504, a_comp_cor_505, a_comp_cor_506, a_comp_cor_507, a_comp_cor_508, a_comp_cor_509, a_comp_cor_510, a_comp_cor_511, a_comp_cor_512, a_comp_cor_513, a_comp_cor_514, a_comp_cor_515, a_comp_cor_516, a_comp_cor_517, a_comp_cor_518, a_comp_cor_519, a_comp_cor_520, a_comp_cor_521, a_comp_cor_522, a_comp_cor_523, a_comp_cor_524, cosine00, cosine01, cosine02, cosine03, cosine04, cosine05, cosine06, cosine07, cosine08, cosine09, cosine10, cosine11, cosine12, cosine13, cosine14, cosine15, cosine16, cosine17, cosine18, cosine19, cosine20, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09</li>
  192. <li>Non-steady-state volumes: 0</li>
  193. </ul>
  194. </div>
  195. <div id="datatype-func_desc-validation_suffix-bold_task-sherlockPart1">
  196. <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
  197. <p class="elem-desc">
  198. The qform has been copied from sform.
  199. The difference in angle is -1.9e-06.
  200. The difference in translation is 3.4e-15.
  201. </p>
  202. </div>
  203. <div id="datatype-func_desc-flirtbbr_suffix-bold_task-sherlockPart1">
  204. <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-12/figures/sub-12_task-sherlockPart1_desc-flirtbbr_bold.svg">
  205. Problem loading figure sub-12/figures/sub-12_task-sherlockPart1_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  206. </div>
  207. <div class="elem-filename">
  208. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart1_desc-flirtbbr_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart1_desc-flirtbbr_bold.svg</a>
  209. </div>
  210. </div>
  211. <div id="datatype-func_desc-rois_suffix-bold_task-sherlockPart1">
  212. <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-12/figures/sub-12_task-sherlockPart1_desc-rois_bold.svg" style="width: 100%" />
  213. </div>
  214. <div class="elem-filename">
  215. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart1_desc-rois_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart1_desc-rois_bold.svg</a>
  216. </div>
  217. </div>
  218. <div id="datatype-func_desc-compcorvar_suffix-bold_task-sherlockPart1">
  219. <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-12/figures/sub-12_task-sherlockPart1_desc-compcorvar_bold.svg" style="width: 100%" />
  220. </div>
  221. <div class="elem-filename">
  222. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart1_desc-compcorvar_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart1_desc-compcorvar_bold.svg</a>
  223. </div>
  224. </div>
  225. <div id="datatype-func_desc-carpetplot_suffix-bold_task-sherlockPart1">
  226. <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-12/figures/sub-12_task-sherlockPart1_desc-carpetplot_bold.svg" style="width: 100%" />
  227. </div>
  228. <div class="elem-filename">
  229. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart1_desc-carpetplot_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart1_desc-carpetplot_bold.svg</a>
  230. </div>
  231. </div>
  232. <div id="datatype-func_desc-confoundcorr_suffix-bold_task-sherlockPart1">
  233. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  234. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  235. Right: magnitude of the correlation between each confound time series and the
  236. mean global signal. Strong correlations might be indicative of partial volume
  237. effects and can inform decisions about feature orthogonalization prior to
  238. confound regression.
  239. </p> <img class="svg-reportlet" src="./sub-12/figures/sub-12_task-sherlockPart1_desc-confoundcorr_bold.svg" style="width: 100%" />
  240. </div>
  241. <div class="elem-filename">
  242. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart1_desc-confoundcorr_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart1_desc-confoundcorr_bold.svg</a>
  243. </div>
  244. </div>
  245. <div id="datatype-func_desc-summary_suffix-bold_task-sherlockPart2">
  246. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">sherlockPart2</span>.</h2> <h3 class="elem-title">Summary</h3>
  247. <ul class="elem-desc">
  248. <li>Repetition time (TR): 1.5s</li>
  249. <li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
  250. <li>Slice timing correction: Not applied</li>
  251. <li>Susceptibility distortion correction: None</li>
  252. <li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
  253. <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_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, t_comp_cor_08, t_comp_cor_09, t_comp_cor_10, 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, a_comp_cor_397, a_comp_cor_398, a_comp_cor_399, a_comp_cor_400, a_comp_cor_401, a_comp_cor_402, a_comp_cor_403, a_comp_cor_404, a_comp_cor_405, a_comp_cor_406, a_comp_cor_407, a_comp_cor_408, a_comp_cor_409, a_comp_cor_410, a_comp_cor_411, a_comp_cor_412, a_comp_cor_413, a_comp_cor_414, a_comp_cor_415, a_comp_cor_416, a_comp_cor_417, a_comp_cor_418, a_comp_cor_419, a_comp_cor_420, a_comp_cor_421, a_comp_cor_422, a_comp_cor_423, a_comp_cor_424, a_comp_cor_425, a_comp_cor_426, a_comp_cor_427, a_comp_cor_428, a_comp_cor_429, a_comp_cor_430, a_comp_cor_431, a_comp_cor_432, a_comp_cor_433, a_comp_cor_434, a_comp_cor_435, a_comp_cor_436, a_comp_cor_437, a_comp_cor_438, a_comp_cor_439, a_comp_cor_440, a_comp_cor_441, a_comp_cor_442, a_comp_cor_443, a_comp_cor_444, a_comp_cor_445, a_comp_cor_446, a_comp_cor_447, a_comp_cor_448, a_comp_cor_449, a_comp_cor_450, a_comp_cor_451, a_comp_cor_452, a_comp_cor_453, a_comp_cor_454, a_comp_cor_455, a_comp_cor_456, a_comp_cor_457, a_comp_cor_458, a_comp_cor_459, a_comp_cor_460, a_comp_cor_461, a_comp_cor_462, a_comp_cor_463, a_comp_cor_464, a_comp_cor_465, a_comp_cor_466, a_comp_cor_467, a_comp_cor_468, a_comp_cor_469, a_comp_cor_470, a_comp_cor_471, a_comp_cor_472, a_comp_cor_473, a_comp_cor_474, a_comp_cor_475, a_comp_cor_476, a_comp_cor_477, a_comp_cor_478, a_comp_cor_479, a_comp_cor_480, a_comp_cor_481, a_comp_cor_482, a_comp_cor_483, a_comp_cor_484, a_comp_cor_485, a_comp_cor_486, a_comp_cor_487, a_comp_cor_488, a_comp_cor_489, a_comp_cor_490, a_comp_cor_491, a_comp_cor_492, a_comp_cor_493, a_comp_cor_494, a_comp_cor_495, a_comp_cor_496, a_comp_cor_497, a_comp_cor_498, a_comp_cor_499, a_comp_cor_500, a_comp_cor_501, a_comp_cor_502, a_comp_cor_503, a_comp_cor_504, a_comp_cor_505, a_comp_cor_506, a_comp_cor_507, a_comp_cor_508, a_comp_cor_509, a_comp_cor_510, a_comp_cor_511, a_comp_cor_512, a_comp_cor_513, a_comp_cor_514, a_comp_cor_515, a_comp_cor_516, a_comp_cor_517, a_comp_cor_518, a_comp_cor_519, a_comp_cor_520, a_comp_cor_521, a_comp_cor_522, a_comp_cor_523, a_comp_cor_524, a_comp_cor_525, a_comp_cor_526, a_comp_cor_527, a_comp_cor_528, a_comp_cor_529, a_comp_cor_530, a_comp_cor_531, a_comp_cor_532, a_comp_cor_533, a_comp_cor_534, a_comp_cor_535, a_comp_cor_536, a_comp_cor_537, a_comp_cor_538, a_comp_cor_539, a_comp_cor_540, a_comp_cor_541, a_comp_cor_542, a_comp_cor_543, a_comp_cor_544, a_comp_cor_545, a_comp_cor_546, a_comp_cor_547, a_comp_cor_548, a_comp_cor_549, a_comp_cor_550, a_comp_cor_551, a_comp_cor_552, a_comp_cor_553, a_comp_cor_554, 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, non_steady_state_outlier01, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_power2, trans_z_derivative1_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_power2, rot_y_derivative1_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2, motion_outlier00, motion_outlier01, motion_outlier02, motion_outlier03, motion_outlier04, motion_outlier05, motion_outlier06, motion_outlier07, motion_outlier08, motion_outlier09, motion_outlier10, motion_outlier11, motion_outlier12, motion_outlier13, motion_outlier14, motion_outlier15, motion_outlier16, motion_outlier17, motion_outlier18, motion_outlier19, motion_outlier20, motion_outlier21, motion_outlier22, motion_outlier23</li>
  254. <li>Non-steady-state volumes: 2</li>
  255. </ul>
  256. </div>
  257. <div id="datatype-func_desc-validation_suffix-bold_task-sherlockPart2">
  258. <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
  259. <p class="elem-desc">
  260. The qform has been copied from sform.
  261. The difference in angle is -1.9e-06.
  262. The difference in translation is 3.4e-15.
  263. </p>
  264. </div>
  265. <div id="datatype-func_desc-flirtbbr_suffix-bold_task-sherlockPart2">
  266. <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-12/figures/sub-12_task-sherlockPart2_desc-flirtbbr_bold.svg">
  267. Problem loading figure sub-12/figures/sub-12_task-sherlockPart2_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
  268. </div>
  269. <div class="elem-filename">
  270. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart2_desc-flirtbbr_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart2_desc-flirtbbr_bold.svg</a>
  271. </div>
  272. </div>
  273. <div id="datatype-func_desc-rois_suffix-bold_task-sherlockPart2">
  274. <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-12/figures/sub-12_task-sherlockPart2_desc-rois_bold.svg" style="width: 100%" />
  275. </div>
  276. <div class="elem-filename">
  277. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart2_desc-rois_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart2_desc-rois_bold.svg</a>
  278. </div>
  279. </div>
  280. <div id="datatype-func_desc-compcorvar_suffix-bold_task-sherlockPart2">
  281. <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-12/figures/sub-12_task-sherlockPart2_desc-compcorvar_bold.svg" style="width: 100%" />
  282. </div>
  283. <div class="elem-filename">
  284. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart2_desc-compcorvar_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart2_desc-compcorvar_bold.svg</a>
  285. </div>
  286. </div>
  287. <div id="datatype-func_desc-carpetplot_suffix-bold_task-sherlockPart2">
  288. <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-12/figures/sub-12_task-sherlockPart2_desc-carpetplot_bold.svg" style="width: 100%" />
  289. </div>
  290. <div class="elem-filename">
  291. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart2_desc-carpetplot_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart2_desc-carpetplot_bold.svg</a>
  292. </div>
  293. </div>
  294. <div id="datatype-func_desc-confoundcorr_suffix-bold_task-sherlockPart2">
  295. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
  296. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
  297. Right: magnitude of the correlation between each confound time series and the
  298. mean global signal. Strong correlations might be indicative of partial volume
  299. effects and can inform decisions about feature orthogonalization prior to
  300. confound regression.
  301. </p> <img class="svg-reportlet" src="./sub-12/figures/sub-12_task-sherlockPart2_desc-confoundcorr_bold.svg" style="width: 100%" />
  302. </div>
  303. <div class="elem-filename">
  304. Get figure file: <a href="./sub-12/figures/sub-12_task-sherlockPart2_desc-confoundcorr_bold.svg" target="_blank">sub-12/figures/sub-12_task-sherlockPart2_desc-confoundcorr_bold.svg</a>
  305. </div>
  306. </div>
  307. </div>
  308. <div id="About">
  309. <h1 class="sub-report-title">About</h1>
  310. <div id="datatype-anat_desc-about_suffix-T1w">
  311. <ul>
  312. <li>fMRIPrep version: 20.0.6</li>
  313. <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 participant --participant-label sub-12 -w /work --nthreads 16 --omp-nthreads 16 --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Resources/fmriprep/license.txt --write-graph --fs-no-reconall --notrack</code></li>
  314. <li>Date preprocessed: 2020-05-18 23:33:30 -0400</li>
  315. </ul>
  316. </div>
  317. </div>
  318. </div>
  319. <div id="boilerplate">
  320. <h1 class="sub-report-title">Methods</h1>
  321. <p>We kindly ask to report results preprocessed with this tool using the following
  322. boilerplate.</p>
  323. <ul class="nav nav-tabs" id="myTab" role="tablist">
  324. <li class="nav-item">
  325. <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
  326. </li>
  327. <li class="nav-item">
  328. <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
  329. </li>
  330. <li class="nav-item">
  331. <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
  332. </li>
  333. </ul>
  334. <div class="tab-content" id="myTabContent">
  335. <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>
  336. <dl>
  337. <dt>Anatomical data preprocessing</dt>
  338. <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>
  339. </dd>
  340. <dt>Functional data preprocessing</dt>
  341. <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>
  342. </dd>
  343. </dl>
  344. <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>
  345. <h3 id="copyright-waiver">Copyright Waiver</h3>
  346. <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>
  347. <h3 id="references" class="unnumbered">References</h3>
  348. <div id="refs" class="references">
  349. <div id="ref-nilearn">
  350. <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>
  351. </div>
  352. <div id="ref-ants">
  353. <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>
  354. </div>
  355. <div id="ref-compcor">
  356. <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>
  357. </div>
  358. <div id="ref-fmriprep2">
  359. <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>
  360. </div>
  361. <div id="ref-fmriprep1">
  362. <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>
  363. </div>
  364. <div id="ref-mni152nlin2009casym">
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  366. </div>
  367. <div id="ref-nipype1">
  368. <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>
  369. </div>
  370. <div id="ref-nipype2">
  371. <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>
  372. </div>
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  374. <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>
  375. </div>
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  380. <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>
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  383. <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>
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  386. <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>
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  392. <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>
  393. </div>
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  396. </div>
  397. </div></div></div>
  398. <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
  399. Results included in this manuscript come from preprocessing
  400. performed using *fMRIPrep* 20.0.6
  401. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
  402. which is based on *Nipype* 1.4.2
  403. (@nipype1; @nipype2; RRID:SCR_002502).
  404. Anatomical data preprocessing
  405. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
  406. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
  407. The T1w-reference was then skull-stripped with a *Nipype* implementation of
  408. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
  409. as target template.
  410. Brain tissue segmentation of cerebrospinal fluid (CSF),
  411. white-matter (WM) and gray-matter (GM) was performed on
  412. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
  413. @fsl_fast].
  414. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
  415. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
  416. using brain-extracted versions of both T1w reference and the T1w template.
  417. The following template was selected for spatial normalization:
  418. *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym],
  419. Functional data preprocessing
  420. : For each of the 3 BOLD runs found per subject (across all
  421. tasks and sessions), the following preprocessing was performed.
  422. First, a reference volume and its skull-stripped version were generated
  423. using a custom methodology of *fMRIPrep*.
  424. Susceptibility distortion correction (SDC) was omitted.
  425. The BOLD reference was then co-registered to the T1w reference using
  426. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
  427. cost-function.
  428. Co-registration was configured with nine degrees of freedom to account
  429. for distortions remaining in the BOLD reference.
  430. Head-motion parameters with respect to the BOLD reference
  431. (transformation matrices, and six corresponding rotation and translation
  432. parameters) are estimated before any spatiotemporal filtering using
  433. `mcflirt` [FSL 5.0.9, @mcflirt].
  434. The BOLD time-series (including slice-timing correction when applied)
  435. were resampled onto their original, native space by applying
  436. the transforms to correct for head-motion.
  437. These resampled BOLD time-series will be referred to as *preprocessed
  438. BOLD in original space*, or just *preprocessed BOLD*.
  439. The BOLD time-series were resampled into standard space,
  440. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
  441. First, a reference volume and its skull-stripped version were generated
  442. using a custom methodology of *fMRIPrep*.
  443. Several confounding time-series were calculated based on the
  444. *preprocessed BOLD*: framewise displacement (FD), DVARS and
  445. three region-wise global signals.
  446. FD and DVARS are calculated for each functional run, both using their
  447. implementations in *Nipype* [following the definitions by @power_fd_dvars].
  448. The three global signals are extracted within the CSF, the WM, and
  449. the whole-brain masks.
  450. Additionally, a set of physiological regressors were extracted to
  451. allow for component-based noise correction [*CompCor*, @compcor].
  452. Principal components are estimated after high-pass filtering the
  453. *preprocessed BOLD* time-series (using a discrete cosine filter with
  454. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
  455. and anatomical (aCompCor).
  456. tCompCor components are then calculated from the top 5% variable
  457. voxels within a mask covering the subcortical regions.
  458. This subcortical mask is obtained by heavily eroding the brain mask,
  459. which ensures it does not include cortical GM regions.
  460. For aCompCor, components are calculated within the intersection of
  461. the aforementioned mask and the union of CSF and WM masks calculated
  462. in T1w space, after their projection to the native space of each
  463. functional run (using the inverse BOLD-to-T1w transformation). Components
  464. are also calculated separately within the WM and CSF masks.
  465. For each CompCor decomposition, the *k* components with the largest singular
  466. values are retained, such that the retained components' time series are
  467. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
  468. WM, combined, or temporal). The remaining components are dropped from
  469. consideration.
  470. The head-motion estimates calculated in the correction step were also
  471. placed within the corresponding confounds file.
  472. The confound time series derived from head motion estimates and global
  473. signals were expanded with the inclusion of temporal derivatives and
  474. quadratic terms for each [@confounds_satterthwaite_2013].
  475. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
  476. were annotated as motion outliers.
  477. All resamplings can be performed with *a single interpolation
  478. step* by composing all the pertinent transformations (i.e. head-motion
  479. transform matrices, susceptibility distortion correction when available,
  480. and co-registrations to anatomical and output spaces).
  481. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
  482. configured with Lanczos interpolation to minimize the smoothing
  483. effects of other kernels [@lanczos].
  484. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
  485. (FreeSurfer).
  486. Many internal operations of *fMRIPrep* use
  487. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
  488. mostly within the functional processing workflow.
  489. For more details of the pipeline, see [the section corresponding
  490. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
  491. ### Copyright Waiver
  492. The above boilerplate text was automatically generated by fMRIPrep
  493. with the express intention that users should copy and paste this
  494. text into their manuscripts *unchanged*.
  495. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
  496. ### References
  497. </pre>
  498. </div>
  499. <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
  500. using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
  501. RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
  502. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
  503. \begin{description}
  504. \item[Anatomical data preprocessing]
  505. The T1-weighted (T1w) image was corrected for intensity non-uniformity
  506. (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
  507. ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
  508. throughout the workflow. The T1w-reference was then skull-stripped with
  509. a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
  510. workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
  511. segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
  512. gray-matter (GM) was performed on the brain-extracted T1w using
  513. \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
  514. Volume-based spatial normalization to one standard space
  515. (MNI152NLin2009cAsym) was performed through nonlinear registration with
  516. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
  517. of both T1w reference and the T1w template. The following template was
  518. selected for spatial normalization: \emph{ICBM 152 Nonlinear
  519. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
  520. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
  521. \item[Functional data preprocessing]
  522. For each of the 3 BOLD runs found per subject (across all tasks and
  523. sessions), the following preprocessing was performed. First, a reference
  524. volume and its skull-stripped version were generated using a custom
  525. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
  526. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
  527. reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
  528. boundary-based registration \citep{bbr} cost-function. Co-registration
  529. was configured with nine degrees of freedom to account for distortions
  530. remaining in the BOLD reference. Head-motion parameters with respect to
  531. the BOLD reference (transformation matrices, and six corresponding
  532. rotation and translation parameters) are estimated before any
  533. spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
  534. 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
  535. correction when applied) were resampled onto their original, native
  536. space by applying the transforms to correct for head-motion. These
  537. resampled BOLD time-series will be referred to as \emph{preprocessed
  538. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
  539. time-series were resampled into standard space, generating a
  540. \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
  541. reference volume and its skull-stripped version were generated using a
  542. custom methodology of \emph{fMRIPrep}. Several confounding time-series
  543. were calculated based on the \emph{preprocessed BOLD}: framewise
  544. displacement (FD), DVARS and three region-wise global signals. FD and
  545. DVARS are calculated for each functional run, both using their
  546. implementations in \emph{Nipype} \citep[following the definitions
  547. by][]{power_fd_dvars}. The three global signals are extracted within the
  548. CSF, the WM, and the whole-brain masks. Additionally, a set of
  549. physiological regressors were extracted to allow for component-based
  550. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
  551. components are estimated after high-pass filtering the
  552. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
  553. with 128s cut-off) for the two \emph{CompCor} variants: temporal
  554. (tCompCor) and anatomical (aCompCor). tCompCor components are then
  555. calculated from the top 5\% variable voxels within a mask covering the
  556. subcortical regions. This subcortical mask is obtained by heavily
  557. eroding the brain mask, which ensures it does not include cortical GM
  558. regions. For aCompCor, components are calculated within the intersection
  559. of the aforementioned mask and the union of CSF and WM masks calculated
  560. in T1w space, after their projection to the native space of each
  561. functional run (using the inverse BOLD-to-T1w transformation).
  562. Components are also calculated separately within the WM and CSF masks.
  563. For each CompCor decomposition, the \emph{k} components with the largest
  564. singular values are retained, such that the retained components' time
  565. series are sufficient to explain 50 percent of variance across the
  566. nuisance mask (CSF, WM, combined, or temporal). The remaining components
  567. are dropped from consideration. The head-motion estimates calculated in
  568. the correction step were also placed within the corresponding confounds
  569. file. The confound time series derived from head motion estimates and
  570. global signals were expanded with the inclusion of temporal derivatives
  571. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
  572. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
  573. were annotated as motion outliers. All resamplings can be performed with
  574. \emph{a single interpolation step} by composing all the pertinent
  575. transformations (i.e.~head-motion transform matrices, susceptibility
  576. distortion correction when available, and co-registrations to anatomical
  577. and output spaces). Gridded (volumetric) resamplings were performed
  578. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
  579. interpolation to minimize the smoothing effects of other kernels
  580. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
  581. \texttt{mri\_vol2surf} (FreeSurfer).
  582. \end{description}
  583. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
  584. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
  585. processing workflow. For more details of the pipeline, see
  586. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
  587. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
  588. \hypertarget{copyright-waiver}{%
  589. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
  590. The above boilerplate text was automatically generated by fMRIPrep with
  591. the express intention that users should copy and paste this text into
  592. their manuscripts \emph{unchanged}. It is released under the
  593. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
  594. \hypertarget{references}{%
  595. \subsubsection{References}\label{references}}
  596. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
  597. <h3>Bibliography</h3>
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  907. </pre>
  908. </div>
  909. </div>
  910. </div>
  911. <div id="errors">
  912. <h1 class="sub-report-title">Errors</h1>
  913. <p>No errors to report!</p>
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