Hi Matthew,

thank you for the help!

I did the following:

1. I used the "clean" datasets now and extracted my ROI data from the LR
and RL phase encodings (first two sessions)
2. I calculated the mean across time for every ROI and substracted it, i.e.
I have a 1200 by 112 matrix and calculated the row mean (over time) and
substracted it from each column (ROIs).
3. I did this for LR and RL seperately and then concatenated them together

Can you confirm that this is the correct?

thanks again!

David

2016-07-13 0:55 GMT+02:00 Glasser, Matthew <[email protected]>:

> I would recommend using the data with _hp2000_clean in the name.  I a
> referring to taking the mean across time at each point in space and
> subtracting that from the data.
>
> Peace,
>
> Matt.
>
> From: David Hofmann <[email protected]>
> Date: Tuesday, July 12, 2016 at 6:56 AM
> To: Matt Glasser <[email protected]>
> Cc: Stephen Smith <[email protected]>, "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 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
>>>> >
>>>>
>>>>
>>> _______________________________________________
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>>
>>
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>> <timecourse.png>
>>
>>
>>
>>
>> ---------------------------------------------------------------------------
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>> 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
>>
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>>
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