310
ADHD200_CC200
NYU_9578663
Craddock 200
file
file
file
file
fMRI
Public
Siemens Allegra 3T
Duration=6:00, TR=2000ms, TE=15ms, Voxel Size=3x3x4mm
8.26
8.26
Female
Typically Developing
1
Resting state fMRI data were preprocessed using the Athena pipeline. Remove first 4 EPI volumes (AFNI:3dcalc) Slice timing correction (AFNI:3dTshift) Deoblique dataset (AFNI:3drefit) Reorient into RPI orientation (AFNI:3dresample) Motion correct EPI volumes to the first (originally 5th) image of the time series (AFNI:3dvolreg) Mask the dataset to exclude non-brain (AFNI:3dAutomask) Average the volumes to create a mean image (AFNI:3dTstat) Co-register mean EPI image to corresponding anatomic image (FSL:flirt) Write fMRI data and mean image into template space at 4 mm x 4 mm x 4 mm resolution (FSL:applywarp). Down-sample WM and CSF masks (from anatomical preprocessing) to match EPI resolution (AFNI:3dfractionize) Extract WM and CSF time-courses from EPI volumes using WM and CSF masks (AFNI:3dmaskave) Regress out WM, CSF, motion time courses (calculated from motion correction) as well as a low order polynomial (detrending) from EPI data (AFNI:3dDeconvolve). Band-pass filter (0.009 < f < 0.08 Hz) voxel timecourses to exclude frequencies not implicated in resting state functional connectivity (AFNI:3dFourier). Blur the filtered and unfiltered data using a 6-mm FWHM Gaussian filter (AFNI:3dmerge). Further details on the pipeline are available here: http://www.nitrc.org/plugins/mwiki/index.php/neurobureau:AthenaPipeline. Functional ROIs obtained using the method described in Craddock et al. 2011, requesting <= 200 unique ROIs. http://www.ncbi.nlm.nih.gov/pubmed/21769991
Data comes from the ADHD-200 sample: http://fcon_1000.projects.nitrc.org/indi/adhd200/. Funding: NIMH (R01MH083246) Autism Speaks The Stavros Niarchos Foundation The Leon Levy Foundation An endowment provided by Phyllis Green and Randolph Cowen

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