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  56. </head>
    57. 

  57. <body>
    58. 

  58. 

  59. 

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

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

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

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

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

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

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

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

  68.                     <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-freerecall">Reports for: task <span class="bids-entity">freerecall</span>.</a>
    69. 

  69.                     <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-sherlockPart1">Reports for: task <span class="bids-entity">sherlockPart1</span>.</a>
    70. 

  70.                     <a class="dropdown-item" href="#datatype-func_desc-summary_suffix-bold_task-sherlockPart2">Reports for: task <span class="bids-entity">sherlockPart2</span>.</a>
    71. 

  71.             </div>
    72. 

  72.         </li>
    73. 

  73.         <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
    74. 

  74.         <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
    75. 

  75.         <li class="nav-item"><a class="nav-link" href="#errors">Errors</a></li>
    76. 

  76.     </ul>
    77. 

  77. </div>
    78. 

  78. </nav>
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  79. <noscript>
    80. 

  80.     <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
    81. 

  81. </noscript>
    82. 

  82. 

  83.     <div id="Summary">
    84. 

  84.     <h1 class="sub-report-title">Summary</h1>
    85. 

  85.         <div id="datatype-anat_desc-summary_suffix-T1w">
    86. 

  86.                     <ul class="elem-desc">
    87. 

  87. 		<li>Subject ID: 12</li>
    88. 

  88. 		<li>Structural images: 1 T1-weighted </li>
    89. 

  89. 		<li>Functional series: 3</li>
    90. 

  90. 		<ul class="elem-desc">
    91. 

  91. 			<li>Task: freerecall (1 run)</li>
    92. 

  92. 			<li>Task: sherlockPart1 (1 run)</li>
    93. 

  93. 			<li>Task: sherlockPart2 (1 run)</li>
    94. 

  94. 		</ul>
    95. 

  95. 		<li>Standard output spaces: MNI152NLin2009cAsym</li>
    96. 

  96. 		<li>Non-standard output spaces: </li>
    97. 

  97. 		<li>FreeSurfer reconstruction: Not run</li>
    98. 

  98. 	</ul>
    99. 

  99.         </div>
    100. 

  100.     </div>
    101. 

  101.     <div id="Anatomical">
    102. 

  102.     <h1 class="sub-report-title">Anatomical</h1>
    103. 

  103.         <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
    104. 

  104.                     <h3 class="elem-title">Anatomical Conformation</h3>
    105. 

  105. 		<ul class="elem-desc">
    106. 

  106. 			<li>Input T1w images: 1</li>
    107. 

  107. 			<li>Output orientation: RAS</li>
    108. 

  108. 			<li>Output dimensions: 192x250x216</li>
    109. 

  109. 			<li>Output voxel size: 0.9mm x 0.86mm x 0.86mm</li>
    110. 

  110. 			<li>Discarded images: 0</li>
    111. 

  111. 

  112. 		</ul>
    113. 

  113.         </div>
    114. 

  114.         <div id="datatype-anat_suffix-dseg">
    115. 

  115. <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%" />
    116. 

  116. </div>
    117. 

  117. <div class="elem-filename">
    118. 

  118.     Get figure file: <a href="./sub-12/figures/sub-12_dseg.svg" target="_blank">sub-12/figures/sub-12_dseg.svg</a>
    119. 

  119. </div>
    120. 

  120. 

  121.         </div>
    122. 

  122.         <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
    123. 

  123. <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">
    124. 

  124. 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>
    125. 

  125. </div>
    126. 

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

  127.     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>
    128. 

  128. </div>
    129. 

  129. 

  130.         </div>
    131. 

  131.     </div>
    132. 

  132.     <div id="Functional">
    133. 

  133.     <h1 class="sub-report-title">Functional</h1>
    134. 

  134.         <div id="datatype-func_desc-summary_suffix-bold_task-freerecall">
    135. 

  135. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">freerecall</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    136. 

  136. 		<ul class="elem-desc">
    137. 

  137. 			<li>Repetition time (TR): 1.5s</li>
    138. 

  138. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    139. 

  139. 			<li>Slice timing correction: Not applied</li>
    140. 

  140. 			<li>Susceptibility distortion correction: None</li>
    141. 

  141. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    142. 

  142. 			<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, 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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, 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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, 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>
    143. 

  143. 			<li>Non-steady-state volumes: 0</li>
    144. 

  144. 		</ul>
    145. 

  145.         </div>
    146. 

  146.         <div id="datatype-func_desc-validation_suffix-bold_task-freerecall">
    147. 

  147.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    148. 

  148. 			    <p class="elem-desc">
    149. 

  149. 			    The qform has been copied from sform.
    150. 

  150. 			    The difference in angle is -1.9e-06.
    151. 

  151. 			    The difference in translation is 3.4e-15.
    152. 

  152. 			    </p>
    153. 

  153.         </div>
    154. 

  154.         <div id="datatype-func_desc-flirtbbr_suffix-bold_task-freerecall">
    155. 

  155. <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">
    156. 

  156. 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>
    157. 

  157. </div>
    158. 

  158. <div class="elem-filename">
    159. 

  159.     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>
    160. 

  160. </div>
    161. 

  161. 

  162.         </div>
    163. 

  163.         <div id="datatype-func_desc-rois_suffix-bold_task-freerecall">
    164. 

  164. <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%" />
    165. 

  165. </div>
    166. 

  166. <div class="elem-filename">
    167. 

  167.     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>
    168. 

  168. </div>
    169. 

  169. 

  170.         </div>
    171. 

  171.         <div id="datatype-func_desc-compcorvar_suffix-bold_task-freerecall">
    172. 

  172. <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%" />
    173. 

  173. </div>
    174. 

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

  175.     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>
    176. 

  176. </div>
    177. 

  177. 

  178.         </div>
    179. 

  179.         <div id="datatype-func_desc-carpetplot_suffix-bold_task-freerecall">
    180. 

  180. <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%" />
    181. 

  181. </div>
    182. 

  182. <div class="elem-filename">
    183. 

  183.     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>
    184. 

  184. </div>
    185. 

  185. 

  186.         </div>
    187. 

  187.         <div id="datatype-func_desc-confoundcorr_suffix-bold_task-freerecall">
    188. 

  188. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    189. 

  189. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    190. 

  190. Right: magnitude of the correlation between each confound time series and the
    191. 

  191. mean global signal. Strong correlations might be indicative of partial volume
    192. 

  192. effects and can inform decisions about feature orthogonalization prior to
    193. 

  193. confound regression.
    194. 

  194. </p>                    <img class="svg-reportlet" src="./sub-12/figures/sub-12_task-freerecall_desc-confoundcorr_bold.svg" style="width: 100%" />
    195. 

  195. </div>
    196. 

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

  197.     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>
    198. 

  198. </div>
    199. 

  199. 

  200.         </div>
    201. 

  201.         <div id="datatype-func_desc-summary_suffix-bold_task-sherlockPart1">
    202. 

  202. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">sherlockPart1</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    203. 

  203. 		<ul class="elem-desc">
    204. 

  204. 			<li>Repetition time (TR): 1.5s</li>
    205. 

  205. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    206. 

  206. 			<li>Slice timing correction: Not applied</li>
    207. 

  207. 			<li>Susceptibility distortion correction: None</li>
    208. 

  208. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    209. 

  209. 			<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>
    210. 

  210. 			<li>Non-steady-state volumes: 0</li>
    211. 

  211. 		</ul>
    212. 

  212.         </div>
    213. 

  213.         <div id="datatype-func_desc-validation_suffix-bold_task-sherlockPart1">
    214. 

  214.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    215. 

  215. 			    <p class="elem-desc">
    216. 

  216. 			    The qform has been copied from sform.
    217. 

  217. 			    The difference in angle is -1.9e-06.
    218. 

  218. 			    The difference in translation is 3.4e-15.
    219. 

  219. 			    </p>
    220. 

  220.         </div>
    221. 

  221.         <div id="datatype-func_desc-flirtbbr_suffix-bold_task-sherlockPart1">
    222. 

  222. <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">
    223. 

  223. 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>
    224. 

  224. </div>
    225. 

  225. <div class="elem-filename">
    226. 

  226.     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>
    227. 

  227. </div>
    228. 

  228. 

  229.         </div>
    230. 

  230.         <div id="datatype-func_desc-rois_suffix-bold_task-sherlockPart1">
    231. 

  231. <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%" />
    232. 

  232. </div>
    233. 

  233. <div class="elem-filename">
    234. 

  234.     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>
    235. 

  235. </div>
    236. 

  236. 

  237.         </div>
    238. 

  238.         <div id="datatype-func_desc-compcorvar_suffix-bold_task-sherlockPart1">
    239. 

  239. <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%" />
    240. 

  240. </div>
    241. 

  241. <div class="elem-filename">
    242. 

  242.     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>
    243. 

  243. </div>
    244. 

  244. 

  245.         </div>
    246. 

  246.         <div id="datatype-func_desc-carpetplot_suffix-bold_task-sherlockPart1">
    247. 

  247. <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%" />
    248. 

  248. </div>
    249. 

  249. <div class="elem-filename">
    250. 

  250.     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>
    251. 

  251. </div>
    252. 

  252. 

  253.         </div>
    254. 

  254.         <div id="datatype-func_desc-confoundcorr_suffix-bold_task-sherlockPart1">
    255. 

  255. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    256. 

  256. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    257. 

  257. Right: magnitude of the correlation between each confound time series and the
    258. 

  258. mean global signal. Strong correlations might be indicative of partial volume
    259. 

  259. effects and can inform decisions about feature orthogonalization prior to
    260. 

  260. confound regression.
    261. 

  261. </p>                    <img class="svg-reportlet" src="./sub-12/figures/sub-12_task-sherlockPart1_desc-confoundcorr_bold.svg" style="width: 100%" />
    262. 

  262. </div>
    263. 

  263. <div class="elem-filename">
    264. 

  264.     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>
    265. 

  265. </div>
    266. 

  266. 

  267.         </div>
    268. 

  268.         <div id="datatype-func_desc-summary_suffix-bold_task-sherlockPart2">
    269. 

  269. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">sherlockPart2</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    270. 

  270. 		<ul class="elem-desc">
    271. 

  271. 			<li>Repetition time (TR): 1.5s</li>
    272. 

  272. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    273. 

  273. 			<li>Slice timing correction: Not applied</li>
    274. 

  274. 			<li>Susceptibility distortion correction: None</li>
    275. 

  275. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    276. 

  276. 			<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>
    277. 

  277. 			<li>Non-steady-state volumes: 2</li>
    278. 

  278. 		</ul>
    279. 

  279.         </div>
    280. 

  280.         <div id="datatype-func_desc-validation_suffix-bold_task-sherlockPart2">
    281. 

  281.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    282. 

  282. 			    <p class="elem-desc">
    283. 

  283. 			    The qform has been copied from sform.
    284. 

  284. 			    The difference in angle is -1.9e-06.
    285. 

  285. 			    The difference in translation is 3.4e-15.
    286. 

  286. 			    </p>
    287. 

  287.         </div>
    288. 

  288.         <div id="datatype-func_desc-flirtbbr_suffix-bold_task-sherlockPart2">
    289. 

  289. <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">
    290. 

  290. 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>
    291. 

  291. </div>
    292. 

  292. <div class="elem-filename">
    293. 

  293.     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>
    294. 

  294. </div>
    295. 

  295. 

  296.         </div>
    297. 

  297.         <div id="datatype-func_desc-rois_suffix-bold_task-sherlockPart2">
    298. 

  298. <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%" />
    299. 

  299. </div>
    300. 

  300. <div class="elem-filename">
    301. 

  301.     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>
    302. 

  302. </div>
    303. 

  303. 

  304.         </div>
    305. 

  305.         <div id="datatype-func_desc-compcorvar_suffix-bold_task-sherlockPart2">
    306. 

  306. <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%" />
    307. 

  307. </div>
    308. 

  308. <div class="elem-filename">
    309. 

  309.     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>
    310. 

  310. </div>
    311. 

  311. 

  312.         </div>
    313. 

  313.         <div id="datatype-func_desc-carpetplot_suffix-bold_task-sherlockPart2">
    314. 

  314. <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%" />
    315. 

  315. </div>
    316. 

  316. <div class="elem-filename">
    317. 

  317.     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>
    318. 

  318. </div>
    319. 

  319. 

  320.         </div>
    321. 

  321.         <div id="datatype-func_desc-confoundcorr_suffix-bold_task-sherlockPart2">
    322. 

  322. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    323. 

  323. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    324. 

  324. Right: magnitude of the correlation between each confound time series and the
    325. 

  325. mean global signal. Strong correlations might be indicative of partial volume
    326. 

  326. effects and can inform decisions about feature orthogonalization prior to
    327. 

  327. confound regression.
    328. 

  328. </p>                    <img class="svg-reportlet" src="./sub-12/figures/sub-12_task-sherlockPart2_desc-confoundcorr_bold.svg" style="width: 100%" />
    329. 

  329. </div>
    330. 

  330. <div class="elem-filename">
    331. 

  331.     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>
    332. 

  332. </div>
    333. 

  333. 

  334.         </div>
    335. 

  335.     </div>
    336. 

  336.     <div id="About">
    337. 

  337.     <h1 class="sub-report-title">About</h1>
    338. 

  338.         <div id="datatype-anat_desc-about_suffix-T1w">
    339. 

  339.                     <ul>
    340. 

  340. 		<li>fMRIPrep version: 20.0.6</li>
    341. 

  341. 		<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>
    342. 

  342. 		<li>Date preprocessed: 2020-05-18 23:33:30 -0400</li>
    343. 

  343. 	</ul>
    344. 

  344. </div>
    345. 

  345.         </div>
    346. 

  346.     </div>
    347. 

  347. 

  348. <div id="boilerplate">
    349. 

  349.     <h1 class="sub-report-title">Methods</h1>
    350. 

  350.     <p>We kindly ask to report results preprocessed with this tool using the following
    351. 

  351.        boilerplate.</p>
    352. 

  352.     <ul class="nav nav-tabs" id="myTab" role="tablist">
    353. 

  353.         <li class="nav-item">
    354. 

  354.             <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
    355. 

  355.         </li>
    356. 

  356.         <li class="nav-item">
    357. 

  357.             <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
    358. 

  358.         </li>
    359. 

  359.         <li class="nav-item">
    360. 

  360.             <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
    361. 

  361.         </li>
    362. 

  362.     </ul>
    363. 

  363.     <div class="tab-content" id="myTabContent">
    364. 

  364.       <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>
    365. 

  365. <dl>
    366. 

  366. <dt>Anatomical data preprocessing</dt>
    367. 

  367. <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>
    368. 

  368. </dd>
    369. 

  369. <dt>Functional data preprocessing</dt>
    370. 

  370. <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>
    371. 

  371. </dd>
    372. 

  372. </dl>
    373. 

  373. <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>
    374. 

  374. <h3 id="copyright-waiver">Copyright Waiver</h3>
    375. 

  375. <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>
    376. 

  376. <h3 id="references" class="unnumbered">References</h3>
    377. 

  377. <div id="refs" class="references">
    378. 

  378. <div id="ref-nilearn">
    379. 

  379. <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>
    380. 

  380. </div>
    381. 

  381. <div id="ref-ants">
    382. 

  382. <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>
    383. 

  383. </div>
    384. 

  384. <div id="ref-compcor">
    385. 

  385. <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>
    386. 

  386. </div>
    387. 

  387. <div id="ref-fmriprep2">
    388. 

  388. <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>
    389. 

  389. </div>
    390. 

  390. <div id="ref-fmriprep1">
    391. 

  391. <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>
    392. 

  392. </div>
    393. 

  393. <div id="ref-mni152nlin2009casym">
    394. 

  394. <p>Fonov, VS, AC Evans, RC McKinstry, CR Almli, and DL Collins. 2009. “Unbiased Nonlinear Average Age-Appropriate Brain Templates from Birth to Adulthood.” <em>NeuroImage</em> 47, Supplement 1: S102. <a href="https://doi.org/10.1016/S1053-8119(09)70884-5" class="uri">https://doi.org/10.1016/S1053-8119(09)70884-5</a>.</p>
    395. 

  395. </div>
    396. 

  396. <div id="ref-nipype1">
    397. 

  397. <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>
    398. 

  398. </div>
    399. 

  399. <div id="ref-nipype2">
    400. 

  400. <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>
    401. 

  401. </div>
    402. 

  402. <div id="ref-bbr">
    403. 

  403. <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>
    404. 

  404. </div>
    405. 

  405. <div id="ref-mcflirt">
    406. 

  406. <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>
    407. 

  407. </div>
    408. 

  408. <div id="ref-flirt">
    409. 

  409. <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>
    410. 

  410. </div>
    411. 

  411. <div id="ref-lanczos">
    412. 

  412. <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>
    413. 

  413. </div>
    414. 

  414. <div id="ref-power_fd_dvars">
    415. 

  415. <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>
    416. 

  416. </div>
    417. 

  417. <div id="ref-confounds_satterthwaite_2013">
    418. 

  418. <p>Satterthwaite, Theodore D., Mark A. Elliott, Raphael T. Gerraty, Kosha Ruparel, James Loughead, Monica E. Calkins, Simon B. Eickhoff, et al. 2013. “An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data.” <em>NeuroImage</em> 64 (1): 240–56. <a href="https://doi.org/10.1016/j.neuroimage.2012.08.052" class="uri">https://doi.org/10.1016/j.neuroimage.2012.08.052</a>.</p>
    419. 

  419. </div>
    420. 

  420. <div id="ref-n4">
    421. 

  421. <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>
    422. 

  422. </div>
    423. 

  423. <div id="ref-fsl_fast">
    424. 

  424. <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>
    425. 

  425. </div>
    426. 

  426. </div></div></div>
    427. 

  427.       <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
    428. 

  428. Results included in this manuscript come from preprocessing
    429. 

  429. performed using *fMRIPrep* 20.0.6
    430. 

  430. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    431. 

  431. which is based on *Nipype* 1.4.2
    432. 

  432. (@nipype1; @nipype2; RRID:SCR_002502).
    433. 

  433. 

  434. Anatomical data preprocessing
    435. 

  435. 

  436. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
    437. 

  437. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
    438. 

  438. The T1w-reference was then skull-stripped with a *Nipype* implementation of
    439. 

  439. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
    440. 

  440. as target template.
    441. 

  441. Brain tissue segmentation of cerebrospinal fluid (CSF),
    442. 

  442. white-matter (WM) and gray-matter (GM) was performed on
    443. 

  443. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
    444. 

  444. @fsl_fast].
    445. 

  445. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
    446. 

  446. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
    447. 

  447. using brain-extracted versions of both T1w reference and the T1w template.
    448. 

  448. The following template was selected for spatial normalization:
    449. 

  449. *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
    450. 

  450. 

  451. Functional data preprocessing
    452. 

  452. 

  453. : For each of the 3 BOLD runs found per subject (across all
    454. 

  454. tasks and sessions), the following preprocessing was performed.
    455. 

  455. First, a reference volume and its skull-stripped version were generated
    456. 

  456. using a custom methodology of *fMRIPrep*.
    457. 

  457. Susceptibility distortion correction (SDC) was omitted.
    458. 

  458. The BOLD reference was then co-registered to the T1w reference using
    459. 

  459. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
    460. 

  460. cost-function.
    461. 

  461. Co-registration was configured with nine degrees of freedom to account
    462. 

  462. for distortions remaining in the BOLD reference.
    463. 

  463. Head-motion parameters with respect to the BOLD reference
    464. 

  464. (transformation matrices, and six corresponding rotation and translation
    465. 

  465. parameters) are estimated before any spatiotemporal filtering using
    466. 

  466. `mcflirt` [FSL 5.0.9, @mcflirt].
    467. 

  467. The BOLD time-series (including slice-timing correction when applied)
    468. 

  468. were resampled onto their original, native space by applying
    469. 

  469. the transforms to correct for head-motion.
    470. 

  470. These resampled BOLD time-series will be referred to as *preprocessed
    471. 

  471. BOLD in original space*, or just *preprocessed BOLD*.
    472. 

  472. The BOLD time-series were resampled into standard space,
    473. 

  473. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
    474. 

  474. First, a reference volume and its skull-stripped version were generated
    475. 

  475. using a custom methodology of *fMRIPrep*.
    476. 

  476. Several confounding time-series were calculated based on the
    477. 

  477. *preprocessed BOLD*: framewise displacement (FD), DVARS and
    478. 

  478. three region-wise global signals.
    479. 

  479. FD and DVARS are calculated for each functional run, both using their
    480. 

  480. implementations in *Nipype* [following the definitions by @power_fd_dvars].
    481. 

  481. The three global signals are extracted within the CSF, the WM, and
    482. 

  482. the whole-brain masks.
    483. 

  483. Additionally, a set of physiological regressors were extracted to
    484. 

  484. allow for component-based noise correction [*CompCor*, @compcor].
    485. 

  485. Principal components are estimated after high-pass filtering the
    486. 

  486. *preprocessed BOLD* time-series (using a discrete cosine filter with
    487. 

  487. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    488. 

  488. and anatomical (aCompCor).
    489. 

  489. tCompCor components are then calculated from the top 5% variable
    490. 

  490. voxels within a mask covering the subcortical regions.
    491. 

  491. This subcortical mask is obtained by heavily eroding the brain mask,
    492. 

  492. which ensures it does not include cortical GM regions.
    493. 

  493. For aCompCor, components are calculated within the intersection of
    494. 

  494. the aforementioned mask and the union of CSF and WM masks calculated
    495. 

  495. in T1w space, after their projection to the native space of each
    496. 

  496. functional run (using the inverse BOLD-to-T1w transformation). Components
    497. 

  497. are also calculated separately within the WM and CSF masks.
    498. 

  498. For each CompCor decomposition, the *k* components with the largest singular
    499. 

  499. values are retained, such that the retained components' time series are
    500. 

  500. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
    501. 

  501. WM, combined, or temporal). The remaining components are dropped from
    502. 

  502. consideration.
    503. 

  503. The head-motion estimates calculated in the correction step were also
    504. 

  504. placed within the corresponding confounds file.
    505. 

  505. The confound time series derived from head motion estimates and global
    506. 

  506. signals were expanded with the inclusion of temporal derivatives and
    507. 

  507. quadratic terms for each [@confounds_satterthwaite_2013].
    508. 

  508. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    509. 

  509. were annotated as motion outliers.
    510. 

  510. All resamplings can be performed with *a single interpolation
    511. 

  511. step* by composing all the pertinent transformations (i.e. head-motion
    512. 

  512. transform matrices, susceptibility distortion correction when available,
    513. 

  513. and co-registrations to anatomical and output spaces).
    514. 

  514. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
    515. 

  515. configured with Lanczos interpolation to minimize the smoothing
    516. 

  516. effects of other kernels [@lanczos].
    517. 

  517. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
    518. 

  518. (FreeSurfer).
    519. 

  519. 

  520. 

  521. Many internal operations of *fMRIPrep* use
    522. 

  522. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
    523. 

  523. mostly within the functional processing workflow.
    524. 

  524. For more details of the pipeline, see [the section corresponding
    525. 

  525. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
    526. 

  526. 

  527. 

  528. ### Copyright Waiver
    529. 

  529. 

  530. The above boilerplate text was automatically generated by fMRIPrep
    531. 

  531. with the express intention that users should copy and paste this
    532. 

  532. text into their manuscripts *unchanged*.
    533. 

  533. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
    534. 

  534. 

  535. ### References
    536. 

  536. 

  537. </pre>
    538. 

  538. </div>
    539. 

  539.       <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
    540. 

  540. using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
    541. 

  541. RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
    542. 

  542. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
    543. 

  543. 

  544. \begin{description}
    545. 

  545. \item[Anatomical data preprocessing]
    546. 

  546. The T1-weighted (T1w) image was corrected for intensity non-uniformity
    547. 

  547. (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
    548. 

  548. ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
    549. 

  549. throughout the workflow. The T1w-reference was then skull-stripped with
    550. 

  550. a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
    551. 

  551. workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
    552. 

  552. segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
    553. 

  553. gray-matter (GM) was performed on the brain-extracted T1w using
    554. 

  554. \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
    555. 

  555. Volume-based spatial normalization to one standard space
    556. 

  556. (MNI152NLin2009cAsym) was performed through nonlinear registration with
    557. 

  557. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
    558. 

  558. of both T1w reference and the T1w template. The following template was
    559. 

  559. selected for spatial normalization: \emph{ICBM 152 Nonlinear
    560. 

  560. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
    561. 

  561. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
    562. 

  562. \item[Functional data preprocessing]
    563. 

  563. For each of the 3 BOLD runs found per subject (across all tasks and
    564. 

  564. sessions), the following preprocessing was performed. First, a reference
    565. 

  565. volume and its skull-stripped version were generated using a custom
    566. 

  566. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
    567. 

  567. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
    568. 

  568. reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
    569. 

  569. boundary-based registration \citep{bbr} cost-function. Co-registration
    570. 

  570. was configured with nine degrees of freedom to account for distortions
    571. 

  571. remaining in the BOLD reference. Head-motion parameters with respect to
    572. 

  572. the BOLD reference (transformation matrices, and six corresponding
    573. 

  573. rotation and translation parameters) are estimated before any
    574. 

  574. spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
    575. 

  575. 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
    576. 

  576. correction when applied) were resampled onto their original, native
    577. 

  577. space by applying the transforms to correct for head-motion. These
    578. 

  578. resampled BOLD time-series will be referred to as \emph{preprocessed
    579. 

  579. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
    580. 

  580. time-series were resampled into standard space, generating a
    581. 

  581. \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
    582. 

  582. reference volume and its skull-stripped version were generated using a
    583. 

  583. custom methodology of \emph{fMRIPrep}. Several confounding time-series
    584. 

  584. were calculated based on the \emph{preprocessed BOLD}: framewise
    585. 

  585. displacement (FD), DVARS and three region-wise global signals. FD and
    586. 

  586. DVARS are calculated for each functional run, both using their
    587. 

  587. implementations in \emph{Nipype} \citep[following the definitions
    588. 

  588. by][]{power_fd_dvars}. The three global signals are extracted within the
    589. 

  589. CSF, the WM, and the whole-brain masks. Additionally, a set of
    590. 

  590. physiological regressors were extracted to allow for component-based
    591. 

  591. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
    592. 

  592. components are estimated after high-pass filtering the
    593. 

  593. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
    594. 

  594. with 128s cut-off) for the two \emph{CompCor} variants: temporal
    595. 

  595. (tCompCor) and anatomical (aCompCor). tCompCor components are then
    596. 

  596. calculated from the top 5\% variable voxels within a mask covering the
    597. 

  597. subcortical regions. This subcortical mask is obtained by heavily
    598. 

  598. eroding the brain mask, which ensures it does not include cortical GM
    599. 

  599. regions. For aCompCor, components are calculated within the intersection
    600. 

  600. of the aforementioned mask and the union of CSF and WM masks calculated
    601. 

  601. in T1w space, after their projection to the native space of each
    602. 

  602. functional run (using the inverse BOLD-to-T1w transformation).
    603. 

  603. Components are also calculated separately within the WM and CSF masks.
    604. 

  604. For each CompCor decomposition, the \emph{k} components with the largest
    605. 

  605. singular values are retained, such that the retained components' time
    606. 

  606. series are sufficient to explain 50 percent of variance across the
    607. 

  607. nuisance mask (CSF, WM, combined, or temporal). The remaining components
    608. 

  608. are dropped from consideration. The head-motion estimates calculated in
    609. 

  609. the correction step were also placed within the corresponding confounds
    610. 

  610. file. The confound time series derived from head motion estimates and
    611. 

  611. global signals were expanded with the inclusion of temporal derivatives
    612. 

  612. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
    613. 

  613. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    614. 

  614. were annotated as motion outliers. All resamplings can be performed with
    615. 

  615. \emph{a single interpolation step} by composing all the pertinent
    616. 

  616. transformations (i.e.~head-motion transform matrices, susceptibility
    617. 

  617. distortion correction when available, and co-registrations to anatomical
    618. 

  618. and output spaces). Gridded (volumetric) resamplings were performed
    619. 

  619. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
    620. 

  620. interpolation to minimize the smoothing effects of other kernels
    621. 

  621. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
    622. 

  622. \texttt{mri\_vol2surf} (FreeSurfer).
    623. 

  623. \end{description}
    624. 

  624. 

  625. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
    626. 

  626. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
    627. 

  627. processing workflow. For more details of the pipeline, see
    628. 

  628. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
    629. 

  629. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
    630. 

  630. 

  631. \hypertarget{copyright-waiver}{%
    632. 

  632. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
    633. 

  633. 

  634. The above boilerplate text was automatically generated by fMRIPrep with
    635. 

  635. the express intention that users should copy and paste this text into
    636. 

  636. their manuscripts \emph{unchanged}. It is released under the
    637. 

  637. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
    638. 

  638. 

  639. \hypertarget{references}{%
    640. 

  640. \subsubsection{References}\label{references}}
    641. 

  641. 

  642. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
    643. 

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  988. 

  989. <div id="errors">
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  990.     <h1 class="sub-report-title">Errors</h1>
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  992. </div>
    993. 

  993. 

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