Also it appears you haven’t either cleaned or removed the mean image from the 
data.

Matt.

From: 
<hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>>
 on behalf of Stephen Smith <st...@fmrib.ox.ac.uk<mailto:st...@fmrib.ox.ac.uk>>
Date: Tuesday, July 12, 2016 at 3:48 AM
To: David Hofmann <davidhofma...@gmail.com<mailto:davidhofma...@gmail.com>>
Cc: "Dierker, Donna" <do...@wustl.edu<mailto:do...@wustl.edu>>, hcp-users 
<hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>
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 
<davidhofma...@gmail.com<mailto:davidhofma...@gmail.com>> 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 
<mha...@wustl.edu<mailto:mha...@wustl.edu>>:

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<tel:314-747-6173>
St. Louis, MO  63110 Email: mha...@wustl.edu<mailto:mha...@wustl.edu>

From: 
<hcp-users-boun...@humanconnectome.org<mailto:hcp-users-boun...@humanconnectome.org>>
 on behalf of David Hofmann 
<davidhofma...@gmail.com<mailto:davidhofma...@gmail.com>>
Date: Monday, July 11, 2016 at 3:15 AM
To: "Dierker, Donna" <do...@wustl.edu<mailto:do...@wustl.edu>>
Cc: hcp-users 
<hcp-users@humanconnectome.org<mailto:hcp-users@humanconnectome.org>>
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 
<do...@wustl.edu<mailto: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<mailto: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<mailto:HCP-Users@humanconnectome.org>
> http://lists.humanconnectome.org/mailman/listinfo/hcp-users
>



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