Craddock 200
Siemens TrioTim 3T
Duration=8:06, TR=2000ms, TE=30ms, Voxel Size=3.1x3.1x3.0mm
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: The Commonwealth Sciences Foundation, Ministry of Health, China (200802073) The National Foundation, Ministry of Science and Technology, China (2007BAI17B03) The National Natural Sciences Foundation, China (30970802) The Funds for International Cooperation of the National Natural Science Foundation of China (81020108022) The National Natural Science Foundation of China (8100059) Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning

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