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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-localizer">Reports for: task <span class="bids-entity">localizer</span>.</a>
    69. 

  69.             </div>
    70. 

  70.         </li>
    71. 

  71.         <li class="nav-item"><a class="nav-link" href="#About">About</a></li>
    72. 

  72.         <li class="nav-item"><a class="nav-link" href="#boilerplate">Methods</a></li>
    73. 

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

  74.     </ul>
    75. 

  75. </div>
    76. 

  76. </nav>
    77. 

  77. <noscript>
    78. 

  78.     <h1 class="text-danger"> The navigation menu uses Javascript. Without it this report might not work as expected </h1>
    79. 

  79. </noscript>
    80. 

  80. 

  81.     <div id="Summary">
    82. 

  82.     <h1 class="sub-report-title">Summary</h1>
    83. 

  83.         <div id="datatype-anat_desc-summary_suffix-T1w">
    84. 

  84.                     <ul class="elem-desc">
    85. 

  85. 		<li>Subject ID: S49</li>
    86. 

  86. 		<li>Structural images: 1 T1-weighted </li>
    87. 

  87. 		<li>Functional series: 1</li>
    88. 

  88. 		<ul class="elem-desc">
    89. 

  89. 			<li>Task: localizer (1 run)</li>
    90. 

  90. 		</ul>
    91. 

  91. 		<li>Standard output spaces: MNI152NLin2009cAsym</li>
    92. 

  92. 		<li>Non-standard output spaces: </li>
    93. 

  93. 		<li>FreeSurfer reconstruction: Not run</li>
    94. 

  94. 	</ul>
    95. 

  95.         </div>
    96. 

  96.     </div>
    97. 

  97.     <div id="Anatomical">
    98. 

  98.     <h1 class="sub-report-title">Anatomical</h1>
    99. 

  99.         <div id="datatype-anat_desc-conform_extension-['.html']_suffix-T1w">
    100. 

  100.                     <h3 class="elem-title">Anatomical Conformation</h3>
    101. 

  101. 		<ul class="elem-desc">
    102. 

  102. 			<li>Input T1w images: 1</li>
    103. 

  103. 			<li>Output orientation: RAS</li>
    104. 

  104. 			<li>Output dimensions: 192x256x128</li>
    105. 

  105. 			<li>Output voxel size: 1mm x 1mm x 1.2mm</li>
    106. 

  106. 			<li>Discarded images: 0</li>
    107. 

  107. 

  108. 		</ul>
    109. 

  109.         </div>
    110. 

  110.         <div id="datatype-anat_suffix-dseg">
    111. 

  111. <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-S49/figures/sub-S49_dseg.svg" style="width: 100%" />
    112. 

  112. </div>
    113. 

  113. <div class="elem-filename">
    114. 

  114.     Get figure file: <a href="./sub-S49/figures/sub-S49_dseg.svg" target="_blank">sub-S49/figures/sub-S49_dseg.svg</a>
    115. 

  115. </div>
    116. 

  116. 

  117.         </div>
    118. 

  118.         <div id="datatype-anat_regex_search-True_space-.*_suffix-T1w">
    119. 

  119. <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-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg">
    120. 

  120. Problem loading figure sub-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg. If the link below works, please try reloading the report in your browser.</object>
    121. 

  121. </div>
    122. 

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

  123.     Get figure file: <a href="./sub-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg" target="_blank">sub-S49/figures/sub-S49_space-MNI152NLin2009cAsym_T1w.svg</a>
    124. 

  124. </div>
    125. 

  125. 

  126.         </div>
    127. 

  127.     </div>
    128. 

  128.     <div id="Functional">
    129. 

  129.     <h1 class="sub-report-title">Functional</h1>
    130. 

  130.         <div id="datatype-func_desc-summary_suffix-bold_task-localizer">
    131. 

  131. <h2 class="sub-report-group">Reports for: task <span class="bids-entity">localizer</span>.</h2>                    <h3 class="elem-title">Summary</h3>
    132. 

  132. 		<ul class="elem-desc">
    133. 

  133. 			<li>Repetition time (TR): 2.4s</li>
    134. 

  134. 			<li>Phase-encoding (PE) direction: MISSING - Assuming Anterior-Posterior</li>
    135. 

  135. 			<li>Slice timing correction: Not applied</li>
    136. 

  136. 			<li>Susceptibility distortion correction: None</li>
    137. 

  137. 			<li>Registration: FSL <code>flirt</code> with boundary-based registration (BBR) metric - 6 dof</li>
    138. 

  138. 			<li>Confounds collected: csf, csf_derivative1, csf_derivative1_power2, csf_power2, white_matter, white_matter_derivative1, white_matter_power2, white_matter_derivative1_power2, global_signal, global_signal_derivative1, global_signal_power2, global_signal_derivative1_power2, std_dvars, dvars, framewise_displacement, t_comp_cor_00, t_comp_cor_01, t_comp_cor_02, t_comp_cor_03, 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, cosine00, cosine01, cosine02, trans_x, trans_x_derivative1, trans_x_power2, trans_x_derivative1_power2, trans_y, trans_y_derivative1, trans_y_derivative1_power2, trans_y_power2, trans_z, trans_z_derivative1, trans_z_derivative1_power2, trans_z_power2, rot_x, rot_x_derivative1, rot_x_power2, rot_x_derivative1_power2, rot_y, rot_y_derivative1, rot_y_derivative1_power2, rot_y_power2, rot_z, rot_z_derivative1, rot_z_power2, rot_z_derivative1_power2</li>
    139. 

  139. 			<li>Non-steady-state volumes: 0</li>
    140. 

  140. 		</ul>
    141. 

  141.         </div>
    142. 

  142.         <div id="datatype-func_desc-validation_suffix-bold_task-localizer">
    143. 

  143.                     <h3 class="elem-title">Note on orientation: qform matrix overwritten</h3>
    144. 

  144. 			    <p class="elem-desc">
    145. 

  145. 			    The qform has been copied from sform.
    146. 

  146. 			    The difference in angle is 0.
    147. 

  147. 			    The difference in translation is 0.
    148. 

  148. 			    </p>
    149. 

  149.         </div>
    150. 

  150.         <div id="datatype-func_desc-flirtbbr_suffix-bold_task-localizer">
    151. 

  151. <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-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg">
    152. 

  152. Problem loading figure sub-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg. If the link below works, please try reloading the report in your browser.</object>
    153. 

  153. </div>
    154. 

  154. <div class="elem-filename">
    155. 

  155.     Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-flirtbbr_bold.svg</a>
    156. 

  156. </div>
    157. 

  157. 

  158.         </div>
    159. 

  159.         <div id="datatype-func_desc-rois_suffix-bold_task-localizer">
    160. 

  160. <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-S49/figures/sub-S49_task-localizer_desc-rois_bold.svg" style="width: 100%" />
    161. 

  161. </div>
    162. 

  162. <div class="elem-filename">
    163. 

  163.     Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-rois_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-rois_bold.svg</a>
    164. 

  164. </div>
    165. 

  165. 

  166.         </div>
    167. 

  167.         <div id="datatype-func_desc-compcorvar_suffix-bold_task-localizer">
    168. 

  168. <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-S49/figures/sub-S49_task-localizer_desc-compcorvar_bold.svg" style="width: 100%" />
    169. 

  169. </div>
    170. 

  170. <div class="elem-filename">
    171. 

  171.     Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-compcorvar_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-compcorvar_bold.svg</a>
    172. 

  172. </div>
    173. 

  173. 

  174.         </div>
    175. 

  175.         <div id="datatype-func_desc-carpetplot_suffix-bold_task-localizer">
    176. 

  176. <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-S49/figures/sub-S49_task-localizer_desc-carpetplot_bold.svg" style="width: 100%" />
    177. 

  177. </div>
    178. 

  178. <div class="elem-filename">
    179. 

  179.     Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-carpetplot_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-carpetplot_bold.svg</a>
    180. 

  180. </div>
    181. 

  181. 

  182.         </div>
    183. 

  183.         <div id="datatype-func_desc-confoundcorr_suffix-bold_task-localizer">
    184. 

  184. <h3 class="run-title">Correlations among nuisance regressors</h3><p class="elem-caption">Left: Heatmap summarizing the correlation structure among confound variables.
    185. 

  185. (Cosine bases and PCA-derived CompCor components are inherently orthogonal.)
    186. 

  186. Right: magnitude of the correlation between each confound time series and the
    187. 

  187. mean global signal. Strong correlations might be indicative of partial volume
    188. 

  188. effects and can inform decisions about feature orthogonalization prior to
    189. 

  189. confound regression.
    190. 

  190. </p>                    <img class="svg-reportlet" src="./sub-S49/figures/sub-S49_task-localizer_desc-confoundcorr_bold.svg" style="width: 100%" />
    191. 

  191. </div>
    192. 

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

  193.     Get figure file: <a href="./sub-S49/figures/sub-S49_task-localizer_desc-confoundcorr_bold.svg" target="_blank">sub-S49/figures/sub-S49_task-localizer_desc-confoundcorr_bold.svg</a>
    194. 

  194. </div>
    195. 

  195. 

  196.         </div>
    197. 

  197.     </div>
    198. 

  198.     <div id="About">
    199. 

  199.     <h1 class="sub-report-title">About</h1>
    200. 

  200.         <div id="datatype-anat_desc-about_suffix-T1w">
    201. 

  201.                     <ul>
    202. 

  202. 		<li>fMRIPrep version: 20.0.6</li>
    203. 

  203. 		<li>fMRIPrep command: <code>/usr/local/miniconda/bin/fmriprep /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/raw/ /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/preprocessed/ --fs-license-file /dartfs/rc/lab/D/DBIC/cosanlab/datax/Projects/pinel_localizer/license.txt participant --participant-label sub-S49 --nthreads 16 --omp-nthreads 16 --write-graph --fs-no-reconall --notrack</code></li>
    204. 

  204. 		<li>Date preprocessed: 2020-05-14 13:29:09 -0400</li>
    205. 

  205. 	</ul>
    206. 

  206. </div>
    207. 

  207.         </div>
    208. 

  208.     </div>
    209. 

  209. 

  210. <div id="boilerplate">
    211. 

  211.     <h1 class="sub-report-title">Methods</h1>
    212. 

  212.     <p>We kindly ask to report results preprocessed with this tool using the following
    213. 

  213.        boilerplate.</p>
    214. 

  214.     <ul class="nav nav-tabs" id="myTab" role="tablist">
    215. 

  215.         <li class="nav-item">
    216. 

  216.             <a class="nav-link active" id="HTML-tab" data-toggle="tab" href="#HTML" role="tab" aria-controls="HTML" aria-selected="true">HTML</a>
    217. 

  217.         </li>
    218. 

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    219. 

  219.             <a class="nav-link " id="Markdown-tab" data-toggle="tab" href="#Markdown" role="tab" aria-controls="Markdown" aria-selected="false">Markdown</a>
    220. 

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    222. 

  222.             <a class="nav-link " id="LaTeX-tab" data-toggle="tab" href="#LaTeX" role="tab" aria-controls="LaTeX" aria-selected="false">LaTeX</a>
    223. 

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    225. 

  225.     <div class="tab-content" id="myTabContent">
    226. 

  226.       <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>
    227. 

  227. <dl>
    228. 

  228. <dt>Anatomical data preprocessing</dt>
    229. 

  229. <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>
    230. 

  230. </dd>
    231. 

  231. <dt>Functional data preprocessing</dt>
    232. 

  232. <dd><p>For each of the 1 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>
    233. 

  233. </dd>
    234. 

  234. </dl>
    235. 

  235. <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>
    236. 

  236. <h3 id="copyright-waiver">Copyright Waiver</h3>
    237. 

  237. <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>
    238. 

  238. <h3 id="references" class="unnumbered">References</h3>
    239. 

  239. <div id="refs" class="references">
    240. 

  240. <div id="ref-nilearn">
    241. 

  241. <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>
    242. 

  242. </div>
    243. 

  243. <div id="ref-ants">
    244. 

  244. <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>
    245. 

  245. </div>
    246. 

  246. <div id="ref-compcor">
    247. 

  247. <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>
    248. 

  248. </div>
    249. 

  249. <div id="ref-fmriprep2">
    250. 

  250. <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>
    251. 

  251. </div>
    252. 

  252. <div id="ref-fmriprep1">
    253. 

  253. <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>
    254. 

  254. </div>
    255. 

  255. <div id="ref-mni152nlin2009casym">
    256. 

  256. <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>
    257. 

  257. </div>
    258. 

  258. <div id="ref-nipype1">
    259. 

  259. <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>
    260. 

  260. </div>
    261. 

  261. <div id="ref-nipype2">
    262. 

  262. <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>
    263. 

  263. </div>
    264. 

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

  265. <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>
    266. 

  266. </div>
    267. 

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

  268. <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>
    269. 

  269. </div>
    270. 

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

  271. <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>
    272. 

  272. </div>
    273. 

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

  274. <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>
    275. 

  275. </div>
    276. 

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

  277. <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>
    278. 

  278. </div>
    279. 

  279. <div id="ref-confounds_satterthwaite_2013">
    280. 

  280. <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>
    281. 

  281. </div>
    282. 

  282. <div id="ref-n4">
    283. 

  283. <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>
    284. 

  284. </div>
    285. 

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

  286. <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>
    287. 

  287. </div>
    288. 

  288. </div></div></div>
    289. 

  289.       <div class="tab-pane fade " id="Markdown" role="tabpanel" aria-labelledby="Markdown-tab"><pre>
    290. 

  290. Results included in this manuscript come from preprocessing
    291. 

  291. performed using *fMRIPrep* 20.0.6
    292. 

  292. (@fmriprep1; @fmriprep2; RRID:SCR_016216),
    293. 

  293. which is based on *Nipype* 1.4.2
    294. 

  294. (@nipype1; @nipype2; RRID:SCR_002502).
    295. 

  295. 

  296. Anatomical data preprocessing
    297. 

  297. 

  298. : The T1-weighted (T1w) image was corrected for intensity non-uniformity (INU)
    299. 

  299. with `N4BiasFieldCorrection` [@n4], distributed with ANTs 2.2.0 [@ants, RRID:SCR_004757], and used as T1w-reference throughout the workflow.
    300. 

  300. The T1w-reference was then skull-stripped with a *Nipype* implementation of
    301. 

  301. the `antsBrainExtraction.sh` workflow (from ANTs), using OASIS30ANTs
    302. 

  302. as target template.
    303. 

  303. Brain tissue segmentation of cerebrospinal fluid (CSF),
    304. 

  304. white-matter (WM) and gray-matter (GM) was performed on
    305. 

  305. the brain-extracted T1w using `fast` [FSL 5.0.9, RRID:SCR_002823,
    306. 

  306. @fsl_fast].
    307. 

  307. Volume-based spatial normalization to one standard space (MNI152NLin2009cAsym) was performed through
    308. 

  308. nonlinear registration with `antsRegistration` (ANTs 2.2.0),
    309. 

  309. using brain-extracted versions of both T1w reference and the T1w template.
    310. 

  310. The following template was selected for spatial normalization:
    311. 

  311. *ICBM 152 Nonlinear Asymmetrical template version 2009c* [@mni152nlin2009casym, RRID:SCR_008796; TemplateFlow ID: MNI152NLin2009cAsym], 
    312. 

  312. 

  313. Functional data preprocessing
    314. 

  314. 

  315. : For each of the 1 BOLD runs found per subject (across all
    316. 

  316. tasks and sessions), the following preprocessing was performed.
    317. 

  317. First, a reference volume and its skull-stripped version were generated
    318. 

  318. using a custom methodology of *fMRIPrep*.
    319. 

  319. Susceptibility distortion correction (SDC) was omitted.
    320. 

  320. The BOLD reference was then co-registered to the T1w reference using
    321. 

  321. `flirt` [FSL 5.0.9, @flirt] with the boundary-based registration [@bbr]
    322. 

  322. cost-function.
    323. 

  323. Co-registration was configured with nine degrees of freedom to account
    324. 

  324. for distortions remaining in the BOLD reference.
    325. 

  325. Head-motion parameters with respect to the BOLD reference
    326. 

  326. (transformation matrices, and six corresponding rotation and translation
    327. 

  327. parameters) are estimated before any spatiotemporal filtering using
    328. 

  328. `mcflirt` [FSL 5.0.9, @mcflirt].
    329. 

  329. The BOLD time-series (including slice-timing correction when applied)
    330. 

  330. were resampled onto their original, native space by applying
    331. 

  331. the transforms to correct for head-motion.
    332. 

  332. These resampled BOLD time-series will be referred to as *preprocessed
    333. 

  333. BOLD in original space*, or just *preprocessed BOLD*.
    334. 

  334. The BOLD time-series were resampled into standard space,
    335. 

  335. generating a *preprocessed BOLD run in MNI152NLin2009cAsym space*.
    336. 

  336. First, a reference volume and its skull-stripped version were generated
    337. 

  337. using a custom methodology of *fMRIPrep*.
    338. 

  338. Several confounding time-series were calculated based on the
    339. 

  339. *preprocessed BOLD*: framewise displacement (FD), DVARS and
    340. 

  340. three region-wise global signals.
    341. 

  341. FD and DVARS are calculated for each functional run, both using their
    342. 

  342. implementations in *Nipype* [following the definitions by @power_fd_dvars].
    343. 

  343. The three global signals are extracted within the CSF, the WM, and
    344. 

  344. the whole-brain masks.
    345. 

  345. Additionally, a set of physiological regressors were extracted to
    346. 

  346. allow for component-based noise correction [*CompCor*, @compcor].
    347. 

  347. Principal components are estimated after high-pass filtering the
    348. 

  348. *preprocessed BOLD* time-series (using a discrete cosine filter with
    349. 

  349. 128s cut-off) for the two *CompCor* variants: temporal (tCompCor)
    350. 

  350. and anatomical (aCompCor).
    351. 

  351. tCompCor components are then calculated from the top 5% variable
    352. 

  352. voxels within a mask covering the subcortical regions.
    353. 

  353. This subcortical mask is obtained by heavily eroding the brain mask,
    354. 

  354. which ensures it does not include cortical GM regions.
    355. 

  355. For aCompCor, components are calculated within the intersection of
    356. 

  356. the aforementioned mask and the union of CSF and WM masks calculated
    357. 

  357. in T1w space, after their projection to the native space of each
    358. 

  358. functional run (using the inverse BOLD-to-T1w transformation). Components
    359. 

  359. are also calculated separately within the WM and CSF masks.
    360. 

  360. For each CompCor decomposition, the *k* components with the largest singular
    361. 

  361. values are retained, such that the retained components' time series are
    362. 

  362. sufficient to explain 50 percent of variance across the nuisance mask (CSF,
    363. 

  363. WM, combined, or temporal). The remaining components are dropped from
    364. 

  364. consideration.
    365. 

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

  366. placed within the corresponding confounds file.
    367. 

  367. The confound time series derived from head motion estimates and global
    368. 

  368. signals were expanded with the inclusion of temporal derivatives and
    369. 

  369. quadratic terms for each [@confounds_satterthwaite_2013].
    370. 

  370. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    371. 

  371. were annotated as motion outliers.
    372. 

  372. All resamplings can be performed with *a single interpolation
    373. 

  373. step* by composing all the pertinent transformations (i.e. head-motion
    374. 

  374. transform matrices, susceptibility distortion correction when available,
    375. 

  375. and co-registrations to anatomical and output spaces).
    376. 

  376. Gridded (volumetric) resamplings were performed using `antsApplyTransforms` (ANTs),
    377. 

  377. configured with Lanczos interpolation to minimize the smoothing
    378. 

  378. effects of other kernels [@lanczos].
    379. 

  379. Non-gridded (surface) resamplings were performed using `mri_vol2surf`
    380. 

  380. (FreeSurfer).
    381. 

  381. 

  382. 

  383. Many internal operations of *fMRIPrep* use
    384. 

  384. *Nilearn* 0.6.2 [@nilearn, RRID:SCR_001362],
    385. 

  385. mostly within the functional processing workflow.
    386. 

  386. For more details of the pipeline, see [the section corresponding
    387. 

  387. to workflows in *fMRIPrep*'s documentation](https://fmriprep.readthedocs.io/en/latest/workflows.html "FMRIPrep's documentation").
    388. 

  388. 

  389. 

  390. ### Copyright Waiver
    391. 

  391. 

  392. The above boilerplate text was automatically generated by fMRIPrep
    393. 

  393. with the express intention that users should copy and paste this
    394. 

  394. text into their manuscripts *unchanged*.
    395. 

  395. It is released under the [CC0](https://creativecommons.org/publicdomain/zero/1.0/) license.
    396. 

  396. 

  397. ### References
    398. 

  398. 

  399. </pre>
    400. 

  400. </div>
    401. 

  401.       <div class="tab-pane fade " id="LaTeX" role="tabpanel" aria-labelledby="LaTeX-tab"><pre>Results included in this manuscript come from preprocessing performed
    402. 

  402. using \emph{fMRIPrep} 20.0.6 (\citet{fmriprep1}; \citet{fmriprep2};
    403. 

  403. RRID:SCR\_016216), which is based on \emph{Nipype} 1.4.2
    404. 

  404. (\citet{nipype1}; \citet{nipype2}; RRID:SCR\_002502).
    405. 

  405. 

  406. \begin{description}
    407. 

  407. \item[Anatomical data preprocessing]
    408. 

  408. The T1-weighted (T1w) image was corrected for intensity non-uniformity
    409. 

  409. (INU) with \texttt{N4BiasFieldCorrection} \citep{n4}, distributed with
    410. 

  410. ANTs 2.2.0 \citep[RRID:SCR\_004757]{ants}, and used as T1w-reference
    411. 

  411. throughout the workflow. The T1w-reference was then skull-stripped with
    412. 

  412. a \emph{Nipype} implementation of the \texttt{antsBrainExtraction.sh}
    413. 

  413. workflow (from ANTs), using OASIS30ANTs as target template. Brain tissue
    414. 

  414. segmentation of cerebrospinal fluid (CSF), white-matter (WM) and
    415. 

  415. gray-matter (GM) was performed on the brain-extracted T1w using
    416. 

  416. \texttt{fast} \citep[FSL 5.0.9, RRID:SCR\_002823,][]{fsl_fast}.
    417. 

  417. Volume-based spatial normalization to one standard space
    418. 

  418. (MNI152NLin2009cAsym) was performed through nonlinear registration with
    419. 

  419. \texttt{antsRegistration} (ANTs 2.2.0), using brain-extracted versions
    420. 

  420. of both T1w reference and the T1w template. The following template was
    421. 

  421. selected for spatial normalization: \emph{ICBM 152 Nonlinear
    422. 

  422. Asymmetrical template version 2009c} {[}\citet{mni152nlin2009casym},
    423. 

  423. RRID:SCR\_008796; TemplateFlow ID: MNI152NLin2009cAsym{]},
    424. 

  424. \item[Functional data preprocessing]
    425. 

  425. For each of the 1 BOLD runs found per subject (across all tasks and
    426. 

  426. sessions), the following preprocessing was performed. First, a reference
    427. 

  427. volume and its skull-stripped version were generated using a custom
    428. 

  428. methodology of \emph{fMRIPrep}. Susceptibility distortion correction
    429. 

  429. (SDC) was omitted. The BOLD reference was then co-registered to the T1w
    430. 

  430. reference using \texttt{flirt} \citep[FSL 5.0.9,][]{flirt} with the
    431. 

  431. boundary-based registration \citep{bbr} cost-function. Co-registration
    432. 

  432. was configured with nine degrees of freedom to account for distortions
    433. 

  433. remaining in the BOLD reference. Head-motion parameters with respect to
    434. 

  434. the BOLD reference (transformation matrices, and six corresponding
    435. 

  435. rotation and translation parameters) are estimated before any
    436. 

  436. spatiotemporal filtering using \texttt{mcflirt} \citep[FSL
    437. 

  437. 5.0.9,][]{mcflirt}. The BOLD time-series (including slice-timing
    438. 

  438. correction when applied) were resampled onto their original, native
    439. 

  439. space by applying the transforms to correct for head-motion. These
    440. 

  440. resampled BOLD time-series will be referred to as \emph{preprocessed
    441. 

  441. BOLD in original space}, or just \emph{preprocessed BOLD}. The BOLD
    442. 

  442. time-series were resampled into standard space, generating a
    443. 

  443. \emph{preprocessed BOLD run in MNI152NLin2009cAsym space}. First, a
    444. 

  444. reference volume and its skull-stripped version were generated using a
    445. 

  445. custom methodology of \emph{fMRIPrep}. Several confounding time-series
    446. 

  446. were calculated based on the \emph{preprocessed BOLD}: framewise
    447. 

  447. displacement (FD), DVARS and three region-wise global signals. FD and
    448. 

  448. DVARS are calculated for each functional run, both using their
    449. 

  449. implementations in \emph{Nipype} \citep[following the definitions
    450. 

  450. by][]{power_fd_dvars}. The three global signals are extracted within the
    451. 

  451. CSF, the WM, and the whole-brain masks. Additionally, a set of
    452. 

  452. physiological regressors were extracted to allow for component-based
    453. 

  453. noise correction \citep[\emph{CompCor},][]{compcor}. Principal
    454. 

  454. components are estimated after high-pass filtering the
    455. 

  455. \emph{preprocessed BOLD} time-series (using a discrete cosine filter
    456. 

  456. with 128s cut-off) for the two \emph{CompCor} variants: temporal
    457. 

  457. (tCompCor) and anatomical (aCompCor). tCompCor components are then
    458. 

  458. calculated from the top 5\% variable voxels within a mask covering the
    459. 

  459. subcortical regions. This subcortical mask is obtained by heavily
    460. 

  460. eroding the brain mask, which ensures it does not include cortical GM
    461. 

  461. regions. For aCompCor, components are calculated within the intersection
    462. 

  462. of the aforementioned mask and the union of CSF and WM masks calculated
    463. 

  463. in T1w space, after their projection to the native space of each
    464. 

  464. functional run (using the inverse BOLD-to-T1w transformation).
    465. 

  465. Components are also calculated separately within the WM and CSF masks.
    466. 

  466. For each CompCor decomposition, the \emph{k} components with the largest
    467. 

  467. singular values are retained, such that the retained components' time
    468. 

  468. series are sufficient to explain 50 percent of variance across the
    469. 

  469. nuisance mask (CSF, WM, combined, or temporal). The remaining components
    470. 

  470. are dropped from consideration. The head-motion estimates calculated in
    471. 

  471. the correction step were also placed within the corresponding confounds
    472. 

  472. file. The confound time series derived from head motion estimates and
    473. 

  473. global signals were expanded with the inclusion of temporal derivatives
    474. 

  474. and quadratic terms for each \citep{confounds_satterthwaite_2013}.
    475. 

  475. Frames that exceeded a threshold of 0.5 mm FD or 1.5 standardised DVARS
    476. 

  476. were annotated as motion outliers. All resamplings can be performed with
    477. 

  477. \emph{a single interpolation step} by composing all the pertinent
    478. 

  478. transformations (i.e.~head-motion transform matrices, susceptibility
    479. 

  479. distortion correction when available, and co-registrations to anatomical
    480. 

  480. and output spaces). Gridded (volumetric) resamplings were performed
    481. 

  481. using \texttt{antsApplyTransforms} (ANTs), configured with Lanczos
    482. 

  482. interpolation to minimize the smoothing effects of other kernels
    483. 

  483. \citep{lanczos}. Non-gridded (surface) resamplings were performed using
    484. 

  484. \texttt{mri\_vol2surf} (FreeSurfer).
    485. 

  485. \end{description}
    486. 

  486. 

  487. Many internal operations of \emph{fMRIPrep} use \emph{Nilearn} 0.6.2
    488. 

  488. \citep[RRID:SCR\_001362]{nilearn}, mostly within the functional
    489. 

  489. processing workflow. For more details of the pipeline, see
    490. 

  490. \href{https://fmriprep.readthedocs.io/en/latest/workflows.html}{the
    491. 

  491. section corresponding to workflows in \emph{fMRIPrep}'s documentation}.
    492. 

  492. 

  493. \hypertarget{copyright-waiver}{%
    494. 

  494. \subsubsection{Copyright Waiver}\label{copyright-waiver}}
    495. 

  495. 

  496. The above boilerplate text was automatically generated by fMRIPrep with
    497. 

  497. the express intention that users should copy and paste this text into
    498. 

  498. their manuscripts \emph{unchanged}. It is released under the
    499. 

  499. \href{https://creativecommons.org/publicdomain/zero/1.0/}{CC0} license.
    500. 

  500. 

  501. \hypertarget{references}{%
    502. 

  502. \subsubsection{References}\label{references}}
    503. 

  503. 

  504. \bibliography{/usr/local/miniconda/lib/python3.7/site-packages/fmriprep/data/boilerplate.bib}</pre>
    505. 

  505. <h3>Bibliography</h3>
    506. 

  506. <pre>@article{fmriprep1,
    507. 

  507.     author = {Esteban, Oscar and Markiewicz, Christopher and Blair, Ross W and Moodie, Craig and Isik, Ayse Ilkay and Erramuzpe Aliaga, Asier and Kent, James and Goncalves, Mathias and DuPre, Elizabeth and Snyder, Madeleine and Oya, Hiroyuki and Ghosh, Satrajit and Wright, Jessey and Durnez, Joke and Poldrack, Russell and Gorgolewski, Krzysztof Jacek},
    508. 

  508.     title = {{fMRIPrep}: a robust preprocessing pipeline for functional {MRI}},
    509. 

  509.     year = {2018},
    510. 

  510.     doi = {10.1038/s41592-018-0235-4},
    511. 

  511.     journal = {Nature Methods}
    512. 

  512. }
    513. 

  513. 

  514. @article{fmriprep2,
    515. 

  515.     author = {Esteban, Oscar and Blair, Ross and Markiewicz, Christopher J. and Berleant, Shoshana L. and Moodie, Craig and Ma, Feilong and Isik, Ayse Ilkay and Erramuzpe, Asier and Kent, James D. andGoncalves, Mathias and DuPre, Elizabeth and Sitek, Kevin R. and Gomez, Daniel E. P. and Lurie, Daniel J. and Ye, Zhifang and Poldrack, Russell A. and Gorgolewski, Krzysztof J.},
    516. 

  516.     title = {fMRIPrep},
    517. 

  517.     year = 2018,
    518. 

  518.     doi = {10.5281/zenodo.852659},
    519. 

  519.     publisher = {Zenodo},
    520. 

  520.     journal = {Software}
    521. 

  521. }
    522. 

  522. 

  523. @article{nipype1,
    524. 

  524.     author = {Gorgolewski, K. and Burns, C. D. and Madison, C. and Clark, D. and Halchenko, Y. O. and Waskom, M. L. and Ghosh, S.},
    525. 

  525.     doi = {10.3389/fninf.2011.00013},
    526. 

  526.     journal = {Frontiers in Neuroinformatics},
    527. 

  527.     pages = 13,
    528. 

  528.     shorttitle = {Nipype},
    529. 

  529.     title = {Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python},
    530. 

  530.     volume = 5,
    531. 

  531.     year = 2011
    532. 

  532. }
    533. 

  533. 

  534. @article{nipype2,
    535. 

  535.     author = {Gorgolewski, Krzysztof J. and Esteban, Oscar and Markiewicz, Christopher J. and Ziegler, Erik and Ellis, David Gage and Notter, Michael Philipp and Jarecka, Dorota and Johnson, Hans and Burns, Christopher and Manhães-Savio, Alexandre and Hamalainen, Carlo and Yvernault, Benjamin and Salo, Taylor and Jordan, Kesshi and Goncalves, Mathias and Waskom, Michael and Clark, Daniel and Wong, Jason and Loney, Fred and Modat, Marc and Dewey, Blake E and Madison, Cindee and Visconti di Oleggio Castello, Matteo and Clark, Michael G. and Dayan, Michael and Clark, Dav and Keshavan, Anisha and Pinsard, Basile and Gramfort, Alexandre and Berleant, Shoshana and Nielson, Dylan M. and Bougacha, Salma and Varoquaux, Gael and Cipollini, Ben and Markello, Ross and Rokem, Ariel and Moloney, Brendan and Halchenko, Yaroslav O. and Wassermann , Demian and Hanke, Michael and Horea, Christian and Kaczmarzyk, Jakub and Gilles de Hollander and DuPre, Elizabeth and Gillman, Ashley and Mordom, David and Buchanan, Colin and Tungaraza, Rosalia and Pauli, Wolfgang M. and Iqbal, Shariq and Sikka, Sharad and Mancini, Matteo and Schwartz, Yannick and Malone, Ian B. and Dubois, Mathieu and Frohlich, Caroline and Welch, David and Forbes, Jessica and Kent, James and Watanabe, Aimi and Cumba, Chad and Huntenburg, Julia M. and Kastman, Erik and Nichols, B. Nolan and Eshaghi, Arman and Ginsburg, Daniel and Schaefer, Alexander and Acland, Benjamin and Giavasis, Steven and Kleesiek, Jens and Erickson, Drew and Küttner, René and Haselgrove, Christian and Correa, Carlos and Ghayoor, Ali and Liem, Franz and Millman, Jarrod and Haehn, Daniel and Lai, Jeff and Zhou, Dale and Blair, Ross and Glatard, Tristan and Renfro, Mandy and Liu, Siqi and Kahn, Ari E. and Pérez-García, Fernando and Triplett, William and Lampe, Leonie and Stadler, Jörg and Kong, Xiang-Zhen and Hallquist, Michael and Chetverikov, Andrey and Salvatore, John and Park, Anne and Poldrack, Russell and Craddock, R. Cameron and Inati, Souheil and Hinds, Oliver and Cooper, Gavin and Perkins, L. Nathan and Marina, Ana and Mattfeld, Aaron and Noel, Maxime and Lukas Snoek and Matsubara, K and Cheung, Brian and Rothmei, Simon and Urchs, Sebastian and Durnez, Joke and Mertz, Fred and Geisler, Daniel and Floren, Andrew and Gerhard, Stephan and Sharp, Paul and Molina-Romero, Miguel and Weinstein, Alejandro and Broderick, William and Saase, Victor and Andberg, Sami Kristian and Harms, Robbert and Schlamp, Kai and Arias, Jaime and Papadopoulos Orfanos, Dimitri and Tarbert, Claire and Tambini, Arielle and De La Vega, Alejandro and Nickson, Thomas and Brett, Matthew and Falkiewicz, Marcel and Podranski, Kornelius and Linkersdörfer, Janosch and Flandin, Guillaume and Ort, Eduard and Shachnev, Dmitry and McNamee, Daniel and Davison, Andrew and Varada, Jan and Schwabacher, Isaac and Pellman, John and Perez-Guevara, Martin and Khanuja, Ranjeet and Pannetier, Nicolas and McDermottroe, Conor and Ghosh, Satrajit},
    536. 

  536.     title = {Nipype},
    537. 

  537.     year = 2018,
    538. 

  538.     doi = {10.5281/zenodo.596855},
    539. 

  539.     publisher = {Zenodo},
    540. 

  540.     journal = {Software}
    541. 

  541. }
    542. 

  542. 

  543. @article{n4,
    544. 

  544.     author = {Tustison, N. J. and Avants, B. B. and Cook, P. A. and Zheng, Y. and Egan, A. and Yushkevich, P. A. and Gee, J. C.},
    545. 

  545.     doi = {10.1109/TMI.2010.2046908},
    546. 

  546.     issn = {0278-0062},
    547. 

  547.     journal = {IEEE Transactions on Medical Imaging},
    548. 

  548.     number = 6,
    549. 

  549.     pages = {1310-1320},
    550. 

  550.     shorttitle = {N4ITK},
    551. 

  551.     title = {N4ITK: Improved N3 Bias Correction},
    552. 

  552.     volume = 29,
    553. 

  553.     year = 2010
    554. 

  554. }
    555. 

  555. 

  556. @article{fs_reconall,
    557. 

  557.     author = {Dale, Anders M. and Fischl, Bruce and Sereno, Martin I.},
    558. 

  558.     doi = {10.1006/nimg.1998.0395},
    559. 

  559.     issn = {1053-8119},
    560. 

  560.     journal = {NeuroImage},
    561. 

  561.     number = 2,
    562. 

  562.     pages = {179-194},
    563. 

  563.     shorttitle = {Cortical Surface-Based Analysis},
    564. 

  564.     title = {Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction},
    565. 

  565.     url = {http://www.sciencedirect.com/science/article/pii/S1053811998903950},
    566. 

  566.     volume = 9,
    567. 

  567.     year = 1999
    568. 

  568. }
    569. 

  569. 

  570. 

  571. 

  572. @article{mindboggle,
    573. 

  573.     author = {Klein, Arno and Ghosh, Satrajit S. and Bao, Forrest S. and Giard, Joachim and Häme, Yrjö and Stavsky, Eliezer and Lee, Noah and Rossa, Brian and Reuter, Martin and Neto, Elias Chaibub and Keshavan, Anisha},
    574. 

  574.     doi = {10.1371/journal.pcbi.1005350},
    575. 

  575.     issn = {1553-7358},
    576. 

  576.     journal = {PLOS Computational Biology},
    577. 

  577.     number = 2,
    578. 

  578.     pages = {e1005350},
    579. 

  579.     title = {Mindboggling morphometry of human brains},
    580. 

  580.     url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005350},
    581. 

  581.     volume = 13,
    582. 

  582.     year = 2017
    583. 

  583. }
    584. 

  584. 

  585. @article{mni152lin,
    586. 

  586.     title = {A {Probabilistic} {Atlas} of the {Human} {Brain}: {Theory} and {Rationale} for {Its} {Development}: {The} {International} {Consortium} for {Brain} {Mapping} ({ICBM})},
    587. 

  587.     author = {Mazziotta, John C. and Toga, Arthur W. and Evans, Alan and Fox, Peter and Lancaster, Jack},
    588. 

  588.     volume = {2},
    589. 

  589.     issn = {1053-8119},
    590. 

  590.     shorttitle = {A {Probabilistic} {Atlas} of the {Human} {Brain}},
    591. 

  591.     doi = {10.1006/nimg.1995.1012},
    592. 

  592.     number = {2, Part A},
    593. 

  593.     journal = {NeuroImage},
    594. 

  594.     year = {1995},
    595. 

  595.     pages = {89--101}
    596. 

  596. }
    597. 

  597. 

  598. @article{mni152nlin2009casym,
    599. 

  599.     title = {Unbiased nonlinear average age-appropriate brain templates from birth to adulthood},
    600. 

  600.     author = {Fonov, VS and Evans, AC and McKinstry, RC and Almli, CR and Collins, DL},
    601. 

  601.     doi = {10.1016/S1053-8119(09)70884-5},
    602. 

  602.     journal = {NeuroImage},
    603. 

  603.     pages = {S102},
    604. 

  604.     volume = {47, Supplement 1},
    605. 

  605.     year = 2009
    606. 

  606. }
    607. 

  607. 

  608. @article{mni152nlin6asym,
    609. 

  609.     author = {Evans, AC and Janke, AL and Collins, DL and Baillet, S},
    610. 

  610.     title = {Brain templates and atlases},
    611. 

  611.     doi = {10.1016/j.neuroimage.2012.01.024},
    612. 

  612.     journal = {NeuroImage},
    613. 

  613.     volume = {62},
    614. 

  614.     number = {2},
    615. 

  615.     pages = {911--922},
    616. 

  616.     year = 2012
    617. 

  617. }
    618. 

  618. 

  619. @article{ants,
    620. 

  620.     author = {Avants, B.B. and Epstein, C.L. and Grossman, M. and Gee, J.C.},
    621. 

  621.     doi = {10.1016/j.media.2007.06.004},
    622. 

  622.     issn = {1361-8415},
    623. 

  623.     journal = {Medical Image Analysis},
    624. 

  624.     number = 1,
    625. 

  625.     pages = {26-41},
    626. 

  626.     shorttitle = {Symmetric diffeomorphic image registration with cross-correlation},
    627. 

  627.     title = {Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain},
    628. 

  628.     url = {http://www.sciencedirect.com/science/article/pii/S1361841507000606},
    629. 

  629.     volume = 12,
    630. 

  630.     year = 2008
    631. 

  631. }
    632. 

  632. 

  633. @article{fsl_fast,
    634. 

  634.     author = {Zhang, Y. and Brady, M. and Smith, S.},
    635. 

  635.     doi = {10.1109/42.906424},
    636. 

  636.     issn = {0278-0062},
    637. 

  637.     journal = {IEEE Transactions on Medical Imaging},
    638. 

  638.     number = 1,
    639. 

  639.     pages = {45-57},
    640. 

  640.     title = {Segmentation of brain {MR} images through a hidden Markov random field model and the expectation-maximization algorithm},
    641. 

  641.     volume = 20,
    642. 

  642.     year = 2001
    643. 

  643. }
    644. 

  644. 

  645. 

  646. @article{fieldmapless1,
    647. 

  647.     author = {Wang, Sijia and Peterson, Daniel J. and Gatenby, J. C. and Li, Wenbin and Grabowski, Thomas J. and Madhyastha, Tara M.},
    648. 

  648.     doi = {10.3389/fninf.2017.00017},
    649. 

  649.     issn = {1662-5196},
    650. 

  650.     journal = {Frontiers in Neuroinformatics},
    651. 

  651.     language = {English},
    652. 

  652.     title = {Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion {MRI}},
    653. 

  653.     url = {http://journal.frontiersin.org/article/10.3389/fninf.2017.00017/full},
    654. 

  654.     volume = 11,
    655. 

  655.     year = 2017
    656. 

  656. }
    657. 

  657. 

  658. @phdthesis{fieldmapless2,
    659. 

  659.     address = {Berlin},
    660. 

  660.     author = {Huntenburg, Julia M.},
    661. 

  661.     language = {eng},
    662. 

  662.     school = {Freie Universität},
    663. 

  663.     title = {Evaluating nonlinear coregistration of {BOLD} {EPI} and T1w images},
    664. 

  664.     type = {Master's Thesis},
    665. 

  665.     url = {http://hdl.handle.net/11858/00-001M-0000-002B-1CB5-A},
    666. 

  666.     year = 2014
    667. 

  667. }
    668. 

  668. 

  669. @article{fieldmapless3,
    670. 

  670.     author = {Treiber, Jeffrey Mark and White, Nathan S. and Steed, Tyler Christian and Bartsch, Hauke and Holland, Dominic and Farid, Nikdokht and McDonald, Carrie R. and Carter, Bob S. and Dale, Anders Martin and Chen, Clark C.},
    671. 

  671.     doi = {10.1371/journal.pone.0152472},
    672. 

  672.     issn = {1932-6203},
    673. 

  673.     journal = {PLOS ONE},
    674. 

  674.     number = 3,
    675. 

  675.     pages = {e0152472},
    676. 

  676.     title = {Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images},
    677. 

  677.     url = {http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0152472},
    678. 

  678.     volume = 11,
    679. 

  679.     year = 2016
    680. 

  680. }
    681. 

  681. 

  682. @article{flirt,
    683. 

  683.     title = {A global optimisation method for robust affine registration of brain images},
    684. 

  684.     volume = {5},
    685. 

  685.     issn = {1361-8415},
    686. 

  686.     url = {http://www.sciencedirect.com/science/article/pii/S1361841501000366},
    687. 

  687.     doi = {10.1016/S1361-8415(01)00036-6},
    688. 

  688.     number = {2},
    689. 

  689.     urldate = {2018-07-27},
    690. 

  690.     journal = {Medical Image Analysis},
    691. 

  691.     author = {Jenkinson, Mark and Smith, Stephen},
    692. 

  692.     year = {2001},
    693. 

  693.     keywords = {Affine transformation, flirt, fsl, Global optimisation, Multi-resolution search, Multimodal registration, Robustness},
    694. 

  694.     pages = {143--156}
    695. 

  695. }
    696. 

  696. 

  697. @article{mcflirt,
    698. 

  698.     author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen},
    699. 

  699.     doi = {10.1006/nimg.2002.1132},
    700. 

  700.     issn = {1053-8119},
    701. 

  701.     journal = {NeuroImage},
    702. 

  702.     number = 2,
    703. 

  703.     pages = {825-841},
    704. 

  704.     title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images},
    705. 

  705.     url = {http://www.sciencedirect.com/science/article/pii/S1053811902911328},
    706. 

  706.     volume = 17,
    707. 

  707.     year = 2002
    708. 

  708. }
    709. 

  709. 

  710. @article{bbr,
    711. 

  711.     author = {Greve, Douglas N and Fischl, Bruce},
    712. 

  712.     doi = {10.1016/j.neuroimage.2009.06.060},
    713. 

  713.     issn = {1095-9572},
    714. 

  714.     journal = {NeuroImage},
    715. 

  715.     number = 1,
    716. 

  716.     pages = {63-72},
    717. 

  717.     title = {Accurate and robust brain image alignment using boundary-based registration},
    718. 

  718.     volume = 48,
    719. 

  719.     year = 2009
    720. 

  720. }
    721. 

  721. 

  722. @article{aroma,
    723. 

  723.     author = {Pruim, Raimon H. R. and Mennes, Maarten and van Rooij, Daan and Llera, Alberto and Buitelaar, Jan K. and Beckmann, Christian F.},
    724. 

  724.     doi = {10.1016/j.neuroimage.2015.02.064},
    725. 

  725.     issn = {1053-8119},
    726. 

  726.     journal = {NeuroImage},
    727. 

  727.     number = {Supplement C},
    728. 

  728.     pages = {267-277},
    729. 

  729.     shorttitle = {ICA-AROMA},
    730. 

  730.     title = {ICA-{AROMA}: A robust {ICA}-based strategy for removing motion artifacts from fMRI data},
    731. 

  731.     url = {http://www.sciencedirect.com/science/article/pii/S1053811915001822},
    732. 

  732.     volume = 112,
    733. 

  733.     year = 2015
    734. 

  734. }
    735. 

  735. 

  736. @article{power_fd_dvars,
    737. 

  737.     author = {Power, Jonathan D. and Mitra, Anish and Laumann, Timothy O. and Snyder, Abraham Z. and Schlaggar, Bradley L. and Petersen, Steven E.},
    738. 

  738.     doi = {10.1016/j.neuroimage.2013.08.048},
    739. 

  739.     issn = {1053-8119},
    740. 

  740.     journal = {NeuroImage},
    741. 

  741.     number = {Supplement C},
    742. 

  742.     pages = {320-341},
    743. 

  743.     title = {Methods to detect, characterize, and remove motion artifact in resting state fMRI},
    744. 

  744.     url = {http://www.sciencedirect.com/science/article/pii/S1053811913009117},
    745. 

  745.     volume = 84,
    746. 

  746.     year = 2014
    747. 

  747. }
    748. 

  748. 

  749. @article{confounds_satterthwaite_2013,
    750. 

  750.     author = {Satterthwaite, Theodore D. and Elliott, Mark A. and Gerraty, Raphael T. and Ruparel, Kosha and Loughead, James and Calkins, Monica E. and Eickhoff, Simon B. and Hakonarson, Hakon and Gur, Ruben C. and Gur, Raquel E. and Wolf, Daniel H.},
    751. 

  751.     doi = {10.1016/j.neuroimage.2012.08.052},
    752. 

  752.     issn = {10538119},
    753. 

  753.     journal = {NeuroImage},
    754. 

  754.     number = 1,
    755. 

  755.     pages = {240--256},
    756. 

  756.     title = {{An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data}},
    757. 

  757.     url = {http://linkinghub.elsevier.com/retrieve/pii/S1053811912008609},
    758. 

  758.     volume = 64,
    759. 

  759.     year = 2013
    760. 

  760. }
    761. 

  761. 

  762. 

  763. @article{nilearn,
    764. 

  764.     author = {Abraham, Alexandre and Pedregosa, Fabian and Eickenberg, Michael and Gervais, Philippe and Mueller, Andreas and Kossaifi, Jean and Gramfort, Alexandre and Thirion, Bertrand and Varoquaux, Gael},
    765. 

  765.     doi = {10.3389/fninf.2014.00014},
    766. 

  766.     issn = {1662-5196},
    767. 

  767.     journal = {Frontiers in Neuroinformatics},
    768. 

  768.     language = {English},
    769. 

  769.     title = {Machine learning for neuroimaging with scikit-learn},
    770. 

  770.     url = {https://www.frontiersin.org/articles/10.3389/fninf.2014.00014/full},
    771. 

  771.     volume = 8,
    772. 

  772.     year = 2014
    773. 

  773. }
    774. 

  774. 

  775. @article{lanczos,
    776. 

  776.     author = {Lanczos, C.},
    777. 

  777.     doi = {10.1137/0701007},
    778. 

  778.     issn = {0887-459X},
    779. 

  779.     journal = {Journal of the Society for Industrial and Applied Mathematics Series B Numerical Analysis},
    780. 

  780.     number = 1,
    781. 

  781.     pages = {76-85},
    782. 

  782.     title = {Evaluation of Noisy Data},
    783. 

  783.     url = {http://epubs.siam.org/doi/10.1137/0701007},
    784. 

  784.     volume = 1,
    785. 

  785.     year = 1964
    786. 

  786. }
    787. 

  787. 

  788. @article{compcor,
    789. 

  789.     author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
    790. 

  790.     doi = {10.1016/j.neuroimage.2007.04.042},
    791. 

  791.     issn = {1053-8119},
    792. 

  792.     journal = {NeuroImage},
    793. 

  793.     number = 1,
    794. 

  794.     pages = {90-101},
    795. 

  795.     title = {A component based noise correction method ({CompCor}) for {BOLD} and perfusion based fMRI},
    796. 

  796.     url = {http://www.sciencedirect.com/science/article/pii/S1053811907003837},
    797. 

  797.     volume = 37,
    798. 

  798.     year = 2007
    799. 

  799. }
    800. 

  800. 

  801. @article{hcppipelines,
    802. 

  802.     author = {Glasser, Matthew F. and Sotiropoulos, Stamatios N. and Wilson, J. Anthony and Coalson, Timothy S. and Fischl, Bruce and Andersson, Jesper L. and Xu, Junqian and Jbabdi, Saad and Webster, Matthew and Polimeni, Jonathan R. and Van Essen, David C. and Jenkinson, Mark},
    803. 

  803.     doi = {10.1016/j.neuroimage.2013.04.127},
    804. 

  804.     issn = {1053-8119},
    805. 

  805.     journal = {NeuroImage},
    806. 

  806.     pages = {105-124},
    807. 

  807.     series = {Mapping the Connectome},
    808. 

  808.     title = {The minimal preprocessing pipelines for the Human Connectome Project},
    809. 

  809.     url = {http://www.sciencedirect.com/science/article/pii/S1053811913005053},
    810. 

  810.     volume = 80,
    811. 

  811.     year = 2013
    812. 

  812. }
    813. 

  813. 

  814. @article{fs_template,
    815. 

  815.     author = {Reuter, Martin and Rosas, Herminia Diana and Fischl, Bruce},
    816. 

  816.     doi = {10.1016/j.neuroimage.2010.07.020},
    817. 

  817.     journal = {NeuroImage},
    818. 

  818.     number = 4,
    819. 

  819.     pages = {1181-1196},
    820. 

  820.     title = {Highly accurate inverse consistent registration: A robust approach},
    821. 

  821.     volume = 53,
    822. 

  822.     year = 2010
    823. 

  823. }
    824. 

  824. 

  825. @article{afni,
    826. 

  826.     author = {Cox, Robert W. and Hyde, James S.},
    827. 

  827.     doi = {10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L},
    828. 

  828.     journal = {NMR in Biomedicine},
    829. 

  829.     number = {4-5},
    830. 

  830.     pages = {171-178},
    831. 

  831.     title = {Software tools for analysis and visualization of fMRI data},
    832. 

  832.     volume = 10,
    833. 

  833.     year = 1997
    834. 

  834. }
    835. 

  835. 

  836. @article{posse_t2s,
    837. 

  837.     author = {Posse, Stefan and Wiese, Stefan and Gembris, Daniel and Mathiak, Klaus and Kessler, Christoph and Grosse-Ruyken, Maria-Liisa and Elghahwagi, Barbara and Richards, Todd and Dager, Stephen R. and Kiselev, Valerij G.},
    838. 

  838.     doi = {10.1002/(SICI)1522-2594(199907)42:1<87::AID-MRM13>3.0.CO;2-O},
    839. 

  839.     journal = {Magnetic Resonance in Medicine},
    840. 

  840.     number = 1,
    841. 

  841.     pages = {87-97},
    842. 

  842.     title = {Enhancement of {BOLD}-contrast sensitivity by single-shot multi-echo functional {MR} imaging},
    843. 

  843.     volume = 42,
    844. 

  844.     year = 1999
    845. 

  845. }
    846. 

  846. </pre>
    847. 

  847. </div>
    848. 

  848.     </div>
    849. 

  849. </div>
    850. 

  850. 

  851. <div id="errors">
    852. 

  852.     <h1 class="sub-report-title">Errors</h1>
    853. 

  853.     <p>No errors to report!</p>
    854. 

  854. </div>
    855. 

  855. 

  856. 

  857. <script type="text/javascript">
    858. 

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    859. 

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