Hi Donna and others, thanks for your answer. I'm facing a difficulty with extracting data from the preprocessed files, that is they seems to each contain 2400 data points rather than 1200 like described in the documentation.
I downloaded the 10 subjects data set and used the following files: *subjectcode_3T_rfMRI_REST1_preproc.zip, *from which I assume that these are the preprocessed files. It contains two datasets LR and RL: \MNINonLinear\Results\rfMRI_REST1_LR \MNINonLinear\Results\rfMRI_REST1_RL I unpacked these files: rfMRI_REST1_LR.nii.gz rfMRI_REST1_RL.nii.gz and read them as 4D NIFTI with Matlab and an SPM function. Afterwards they each contain 2400 data points (dimension: 91 109 91 2400), but in the documention it says they each should contain only 1200 data points. So I'm not sure if I did something wrong. greetings David 2016-06-30 18:30 GMT+02:00 Dierker, Donna <do...@wustl.edu>: > Hi David, > > I hope this publication answers your questions about HCP rfMRI > preprocessing: > > Resting-state fMRI in the Human Connectome Project. > Smith SM1, Beckmann CF, Andersson J, Auerbach EJ, Bijsterbosch J, Douaud > G, Duff E, Feinberg DA, Griffanti L, Harms MP, Kelly M, Laumann T, Miller > KL, Moeller S, Petersen S, Power J, Salimi-Khorshidi G, Snyder AZ, Vu AT, > Woolrich MW, Xu J, Yacoub E, Uğurbil K, Van Essen DC, Glasser MF; WU-Minn > HCP Consortium. > http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3720828/ > > I am only used to seeing what it is in the fix extended packages, so I'm > not sure all these volumes are in the basic fix packages, but here are > NIFTI volumes in a sample subject's rfMRI subdirectories: > > > 177645/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR_hp2000_clean.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR_hp2000.ica/filtered_func_data.ica/mask.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR_hp2000.ica/filtered_func_data.ica/melodic_IC.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR_hp2000.ica/filtered_func_data.ica/melodic_oIC.nii.gz > 177645/MNINonLinear/Results/rfMRI_REST1_LR/rfMRI_REST1_LR.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST1_RL/rfMRI_REST1_RL_hp2000_clean.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST1_RL/rfMRI_REST1_RL_hp2000.ica/filtered_func_data.ica/mask.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST1_RL/rfMRI_REST1_RL_hp2000.ica/filtered_func_data.ica/melodic_IC.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST1_RL/rfMRI_REST1_RL_hp2000.ica/filtered_func_data.ica/melodic_oIC.nii.gz > 177645/MNINonLinear/Results/rfMRI_REST1_RL/rfMRI_REST1_RL.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_LR/rfMRI_REST2_LR_hp2000_clean.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_LR/rfMRI_REST2_LR_hp2000.ica/filtered_func_data.ica/mask.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_LR/rfMRI_REST2_LR_hp2000.ica/filtered_func_data.ica/melodic_IC.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_LR/rfMRI_REST2_LR_hp2000.ica/filtered_func_data.ica/melodic_oIC.nii.gz > 177645/MNINonLinear/Results/rfMRI_REST2_LR/rfMRI_REST2_LR.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_RL/rfMRI_REST2_RL_hp2000_clean.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_RL/rfMRI_REST2_RL_hp2000.ica/filtered_func_data.ica/mask.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_RL/rfMRI_REST2_RL_hp2000.ica/filtered_func_data.ica/melodic_IC.nii.gz > > 177645/MNINonLinear/Results/rfMRI_REST2_RL/rfMRI_REST2_RL_hp2000.ica/filtered_func_data.ica/melodic_oIC.nii.gz > 177645/MNINonLinear/Results/rfMRI_REST2_RL/rfMRI_REST2_RL.nii.gz > > Maybe this page will help explain those: > > http://fsl.fmrib.ox.ac.uk/fslcourse/lectures/practicals/rfMRIconnectivity/ > > But keep in mind that for neocortex, you can take advantage of the surface > data the HCP provides (e.g., fsaverage_32k/*surf.gii, *dscalar.nii and > *dtseries.nii). You can get better inter-subject registration/alignment on > the surface, if that will be a factor in your study. > > Donna > > > On Jun 28, 2016, at 6:30 PM, David Hofmann <davidhofma...@gmail.com> > wrote: > > > Hi all, > > > > I would like to extract ROI data (only neocortex) 'manually' e.g. using > a ROI from Harvard-Oxford atlas from HCP resting state data, but I'm not > sure which (nifti) files to use and where to find them. I'm also looking > for some information about the preprocessing steps applied to the resting > state data that is, if some additional steps (e.g. filtering) have to be > carried out before ROI extraction or if this has already been done. > > > > Any help on this appreciated! > > > > Thanks > > > > David > > _______________________________________________ > > HCP-Users mailing list > > HCP-Users@humanconnectome.org > > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > > > > _______________________________________________ HCP-Users mailing list HCP-Users@humanconnectome.org http://lists.humanconnectome.org/mailman/listinfo/hcp-users
