Select which options you have run FMRIPREP with to generate custom language we recommend to include in your paper.
With Freesurfer:
Suceptibility Distortion Correction:
none
TOPUP
FUGUE
SyN
With AROMA:
Skullstrip template:
OASIS
NKI
With slicetime correction:
Results included in this manuscript come from preprocessing performed using FMRIPREP version latest [1] a Nipype [2,3] based tool. Each T1 weighted volume was corrected for bias field using N4BiasFieldCorrection v2.1.0 [4] and skullstripped using antsBrainExtraction.sh v2.1.0 (using OASIS NKI template). Cortical surface was estimated using FreeSurfer v6.0.0 [5]. The skullstripped T1w volume was coregistered to skullstripped ICBM 152 Nonlinear Asymmetrical template version 2009c [6] using nonlinear transformation implemented in ANTs v2.1.0 [7].
<p style="font-style: italic;">Functional data was <span class="slicetime_text_true">slice time corrected using AFNI [10] and </span>motion corrected using MCFLIRT v5.0.9 [8]. <span class="SDC_text_TOPUP" style="display: none">Distortion correction was performed using an implementation of the TOPUP technique [9] using 3dQwarp v16.2.07 distributed as part of AFNI [10]. </span> <span class="SDC_text_FUGUE" style="display: none">Distortion correction was performed using fieldmaps processed by the FUGUE v5.0.9 [11] tool. </span> <span class="SDC_text_SyN" style="display: none">Distortion correction was performed by estimating ANTs based nonlinear co-registration of the functional image to intensity inverted T1w image [12,13] constrained with an average fieldmap template [14].</span> This was followed by co-registration to the corresponding T1-weighted volume using boundary based registration 9 degrees of freedom - implemented in <span class="freesurfer_text_true">FreeSurfer v6.0.0 [15]</span> <span class="freesurfer_text_false" style="display: none">FSL [15]</span> . Motion correcting transformations, <span class="SDC_text_TOPUP" style="display: none">field distortion correcting warp, </span> <span class="SDC_text_FUGUE" style="display: none">field distortion correcting warp, </span> <span class="SDC_text_SyN" style="display: none">field distortion correcting warp, </span> T1 weighted transformation and MNI template warp were applied in a single step using antsApplyTransformations v2.1.0 with Lanczos interpolation. </p> <p style="font-style: italic;"> Three tissue classes were extracted from T1w images using FSL FAST v5.0.9 [16]. Voxels from cerebrospinal fluid and white matter were used to create a mask in turn used to extract physiological noise regressors using aCompCor [17]. Mask was eroded and limited to subcortical regions to limit overlap with gray matter, six principal components were estimated. Frame-wise displacement [18] was calculated for each functional run using Nipype implementation. <span class="AROMA_text_true" style="display: none">ICA-based Automatic Removal Of Motion Artifacts (AROMA) was used to generate aggressive noise regressors as well as creating a variant of data that was non-aggressively denoised [19].</span></p> <p style="font-style: italic;"> For more details of the pipeline see <a id="workflow_url" href="http://fmriprep.readthedocs.io/en/latest/workflows.html">http://fmriprep.readthedocs.io/en/latest/workflows.html</a>. </p> <p> <span id="fmriprep_citation">FMRIPREP</span> Available from: <a id="fmriprep_doi_url" href="https://doi.org/10.5281/zenodo.852659">10.5281/zenodo.852659</a> <img src onerror='fillCitation()'> </p> <p> 2. Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO, Waskom ML, Ghosh SS. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front Neuroinform [Internet]. 2011 Aug 22;5(August):13. doi:<a href="https://doi.org/10.3389/fninf.2011.00013">10.3389/fninf.2011.00013</a> </p> <p> 3. Gorgolewski KJ, Esteban O, Ellis DG, Notter MP, Ziegler E, Johnson H, Hamalainen C, Yvernault B, Burns C, Manhães-Savio A, Jarecka D, Markiewicz CJ, Salo T, Clark D, Waskom M, Wong J, Modat M, Dewey BE, Clark MG, Dayan M, Loney F, Madison C, Gramfort A, Keshavan A, Berleant S, Pinsard B, Goncalves M, Clark D, Cipollini B, Varoquaux G, Wassermann D, Rokem A, Halchenko YO, Forbes J, Moloney B, Malone IB, Hanke M, Mordom D, Buchanan C, Pauli WM, Huntenburg JM, Horea C, Schwartz Y, Tungaraza R, Iqbal S, Kleesiek J, Sikka S, Frohlich C, Kent J, Perez-Guevara M, Watanabe A, Welch D, Cumba C, Ginsburg D, Eshaghi A, Kastman E, Bougacha S, Blair R, Acland B, Gillman A, Schaefer A, Nichols BN, Giavasis S, Erickson D, Correa C, Ghayoor A, Küttner R, Haselgrove C, Zhou D, Craddock RC, Haehn D, Lampe L, Millman J, Lai J, Renfro M, Liu S, Stadler J, Glatard T, Kahn AE, Kong X-Z, Triplett W, Park A, McDermottroe C, Hallquist M, Poldrack R, Perkins LN, Noel M, Gerhard S, Salvatore J, Mertz F, Broderick W, Inati S, Hinds O, Brett M, Durnez J, Tambini A, Rothmei S, Andberg SK, Cooper G, Marina A, Mattfeld A, Urchs S, Sharp P, Matsubara K, Geisler D, Cheung B, Floren A, Nickson T, Pannetier N, Weinstein A, Dubois M, Arias J, Tarbert C, Schlamp K, Jordan K, Liem F, Saase V, Harms R, Khanuja R, Podranski K, Flandin G, Papadopoulos Orfanos D, Schwabacher I, McNamee D, Falkiewicz M, Pellman J, Linkersdörfer J, Varada J, Pérez-García F, Davison A, Shachnev D, Ghosh S. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python [Internet]. 2017. doi:<a href="https://doi.org/10.5281/zenodo.581704">10.5281/zenodo.581704</a> </p> <p> 4. Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging [Internet]. 2010 Jun;29(6):1310–20. doi:<a href="https://doi.org/10.1109/TMI.2010.2046908">10.1109/TMI.2010.2046908</a> </p> <p> 5. Dale A, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. Neuroimage. 1999;9(2):179–94. doi:<a href="https://doi.org/10.1006/nimg.1998.0395">10.1006/nimg.1998.0395</a> </p> <p> 6. Fonov VS, Evans AC, McKinstry RC, Almli CR, Collins DL. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage; Amsterdam [Internet]. 2009 Jul 1;47:S102. doi:<a href="https://doi.org/10.1016/S1053-8119(09)70884-5">10.1016/S1053-8119(09)70884-5</a> </p> <p> 7. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal [Internet]. 2008 Feb;12(1):26–41. doi:<a href="https://doi.org/10.1016/j.media.2007.06.004">10.1016/j.media.2007.06.004</a> </p> <p> 8. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage [Internet]. 2002 Oct;17(2):825–41. doi:<a href="https://doi.org/10.1006/nimg.2002.1132">10.1006/nimg.2002.1132</a> </p> <p> 9. Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage [Internet]. 2003 Oct;20(2):870–88. doi:<a href="https://doi.org/10.1016/S1053-8119(03)00336-7">10.1016/S1053-8119(03)00336-7</a> </p> <p> 10. Cox RW. 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- Organization for Human Brain Mapping 2017 (pdf)
