Hi all, Regarding Stephen's answer: I thought it is necessary to concatenate the LR/RL phase encoding directions together somehow or can I just treat every run seperately? What I basically want is the timecourse from a voxel or a region (from preprocessed data) which I can use for further analysis.
Regarding Matthew's answer: I'm afraid I'm not exactly sure what you mean by cleaning or removing the mean image from the data. Mean centering? I used the preprocessed datasets: *subjectcode_3T_rfMRI_REST1_preproc.zip* \MNINonLinear\Results\rfMRI_REST1_LR \MNINonLinear\Results\rfMRI_REST1_RL Is this the correct data or is it necessary to use some different datasets for my specific purposes? (Normally I'd use the netmats datasets, but in I'm especially interested in the amygdala which I'm trying to extract from the Harvard-Oxford Atlas ROI). Thanks for your answers! David 2016-07-12 12:47 GMT+02:00 Glasser, Matthew <[email protected]>: > Also it appears you haven’t either cleaned or removed the mean image from > the data. > > Matt. > > From: <[email protected]> on behalf of Stephen Smith < > [email protected]> > Date: Tuesday, July 12, 2016 at 3:48 AM > To: David Hofmann <[email protected]> > Cc: "Dierker, Donna" <[email protected]>, hcp-users < > [email protected]> > > Subject: Re: [HCP-Users] Extracting ROI data from HCP resting state data > - 2400 data points instead of 1200 ? > > Hi - no we do not (in general for resting-state) ever recommend temporal > contatenation like this before further analyses - for the reason you're > seeing here. > For example, for the HCP released netmats, we take the 4 runs, one at a > time, estimate the 4 (zstat) netmats, and average those. > Cheers. > > > > > On 12 Jul 2016, at 09:43, David Hofmann <[email protected]> wrote: > > Hi Michael, > > thanks for the reply, using a different routine works and shows 1200 > volumes. But now it seems that in some data (extracted ROI mean) there is a > huge difference between LR and RL phase encoding in the signal (see > attached picture). Is this "normal" and can I just concatenate LR and RL > together or is this not possible? > > greetings > > David > > 2016-07-11 19:43 GMT+02:00 Harms, Michael <[email protected]>: > >> >> Hi, >> Can you check the number of volumes/frames of the unpacked >> REST1_{LR,RL}.nii.gz files using something other than your Matlab/SPM >> tools? e.g., FSL’s ‘fslhd’ or ‘fslnvols’ commands. >> >> cheers, >> -MH >> >> -- >> Michael Harms, Ph.D. >> ----------------------------------------------------------- >> Conte Center for the Neuroscience of Mental Disorders >> Washington University School of Medicine >> Department of Psychiatry, Box 8134 >> 660 South Euclid Ave. Tel: 314-747-6173 >> St. Louis, MO 63110 Email: [email protected] >> >> From: <[email protected]> on behalf of David Hofmann >> <[email protected]> >> Date: Monday, July 11, 2016 at 3:15 AM >> To: "Dierker, Donna" <[email protected]> >> Cc: hcp-users <[email protected]> >> Subject: Re: [HCP-Users] Extracting ROI data from HCP resting state data >> - 2400 data points instead of 1200 ? >> >> 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 <[email protected]>: >> >>> 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 <[email protected]> >>> 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 >>> > [email protected] >>> > http://lists.humanconnectome.org/mailman/listinfo/hcp-users >>> > >>> >>> >> _______________________________________________ >> HCP-Users mailing list >> [email protected] >> http://lists.humanconnectome.org/mailman/listinfo/hcp-users >> >> >> ------------------------------ >> >> The materials in this message are private and may contain Protected >> Healthcare Information or other information of a sensitive nature. If you >> are not the intended recipient, be advised that any unauthorized use, >> disclosure, copying or the taking of any action in reliance on the contents >> of this information is strictly prohibited. If you have received this email >> in error, please immediately notify the sender via telephone or return mail. >> > > > _______________________________________________ > HCP-Users mailing list > [email protected] > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > <timecourse.png> > > > > --------------------------------------------------------------------------- > Stephen M. Smith, Professor of Biomedical Engineering > Head of Analysis, Oxford University FMRIB Centre > > FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK > +44 (0) 1865 222726 (fax 222717) > [email protected] http://www.fmrib.ox.ac.uk/~steve > --------------------------------------------------------------------------- > > Stop the cultural destruction of Tibet <http://smithinks.net> > > > > > > _______________________________________________ > HCP-Users mailing list > [email protected] > http://lists.humanconnectome.org/mailman/listinfo/hcp-users > > > ------------------------------ > > The materials in this message are private and may contain Protected > Healthcare Information or other information of a sensitive nature. If you > are not the intended recipient, be advised that any unauthorized use, > disclosure, copying or the taking of any action in reliance on the contents > of this information is strictly prohibited. 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