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
>


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