Hey David,
The difference in signal is normal, it's caused by the drop out patterns
for the two different phase encodings (LR and RL). Certain regions, such as
the OFC, have considerably worse signal drop out in either hemisphere
depending on the phase encoding.
If you plan to concatenate the data, you should consider mean centering or
whitening each timeseries separately first. If you don't, it will appear as
a step function due to the average intensity differences between the two
runs.
Best,
Thomas Campbell Arnold
On Tue, Jul 12, 2016 at 4:43 AM, <[email protected]>
wrote:
> Send HCP-Users mailing list submissions to
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> Today's Topics:
>
> 1. Data download via script (Miriam Klein-Fl?gge)
> 2. Re: create cortical thickness maps for multiple subjects in
> the same template space (Timothy Coalson)
> 3. fMRI time series intensity normalization (Chao Zhang)
> 4. Re: Data download via script (Glasser, Matthew)
> 5. Re: fMRI time series intensity normalization (Glasser, Matthew)
> 6. Re: fMRI time series intensity normalization (Glasser, Matthew)
> 7. Re: Extracting ROI data from HCP resting state data - 2400
> data points instead of 1200 ? (David Hofmann)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 11 Jul 2016 21:46:31 +0100
> From: Miriam Klein-Fl?gge <[email protected]>
> Subject: [HCP-Users] Data download via script
> To: [email protected]
> Message-ID: <[email protected]>
> Content-Type: text/plain; charset=utf-8; format=flowed
>
> Hi all,
>
> We are trying to script the download of several participants and
> folders, but it seems that individual *files* rather than *folders* need
> to be specified when looking at this description:
>
>
> https://wiki.humanconnectome.org/display/PublicData/How+To+Access+Subject+Data+via+REST
>
> Since we need the resting state (fix-cleaned and unprocessed) as well as
> the structural folders of several participants, we wondered if there is
> a more efficient way of organizing the download of the data?
>
> Thanks,
>
> Miriam
>
>
>
> ------------------------------
>
> Message: 2
> Date: Mon, 11 Jul 2016 15:55:48 -0500
> From: Timothy Coalson <[email protected]>
> Subject: Re: [HCP-Users] create cortical thickness maps for multiple
> subjects in the same template space
> To: Dev vasu <[email protected]>
> Cc: "<[email protected]>" <[email protected]>
> Message-ID:
> <CAK_=tawYEbeiMU1tFRUBjDP6r--UO-Hgp=
> [email protected]>
> Content-Type: text/plain; charset="utf-8"
>
> If you already have both white and pial surfaces for the subjects as
> generated by freesurfer, calculation of thickness is just the distance
> between corresponding vertices, and freesurfer should have already
> calculated this (I don't know freesurfer's file naming conventions, so I
> can't say what file it would be in). It is not defined or calculated in
> the volume, so it is never mapped from volume to surface.
>
> If you want/need to recalculate it for some reason, you can use
> wb_command -surface-to-surface-3d-distance .
>
> Tim
>
>
> On Mon, Jul 11, 2016 at 5:20 AM, Dev vasu <
> [email protected]> wrote:
>
> > Dear Sir,
> >
> >
> > can we used myelin style as a mapping method for Volume to surface
> mapping
> > in wb_command to produce cortical thickness files,Unfortunately i only
> have
> > T1w images and we have not acquired T2w images, i would like to use T1w
> > images to generate Cortical thickness , could you please let me know if
> its
> > appropriate way generate them using
> >
> > wb_command -volume-to-surface-mapping command and Myelin style mapping
> method ?.
> >
> >
> >
> > Thanks
> >
> > Vasudev
> >
> >
> > On 8 July 2016 at 17:03, Harms, Michael <[email protected]> wrote:
> >
> >>
> >> Hi,
> >> For statistical inference, I would recommend that you look into the PALM
> >> tool.
> >>
> >> http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/PALM
> >>
> >>
> >> --
> >> 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: Dev vasu <[email protected]>
> >> Date: Friday, July 8, 2016 at 9:56 AM
> >> To: "Harms, Michael" <[email protected]>
> >> Cc: "<[email protected]>" <[email protected]>
> >> Subject: Re: [HCP-Users] create cortical thickness maps for multiple
> >> subjects in the same template space
> >>
> >> Dear Sir,
> >>
> >> Is there any way to quantify the changes in cortical thickness between
> >> healthy controls and non healthy controls ?.
> >>
> >>
> >> Thanks
> >> Vasudev
> >>
> >> On 8 July 2016 at 16:47, Harms, Michael <[email protected]> wrote:
> >>
> >>>
> >>> HI,
> >>> Cortical thickness maps on the surface mesh are already created for
> >>> you. You should analyze/view the thickness on the surface; not try to
> >>> project the thickness back onto the volume.
> >>>
> >>> 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 Dev vasu <
> >>> [email protected]>
> >>> Date: Friday, July 8, 2016 at 9:35 AM
> >>> To: "<[email protected]>" <[email protected]>
> >>> Subject: [HCP-Users] create cortical thickness maps for multiple
> >>> subjects in the same template space
> >>>
> >>> Dear Sir /madam,
> >>>
> >>> I am able to successfully run Structural preprocessing pipeline on a
> >>> group of 24 subjects, I would like to know how i can create cortical
> >>> thickness maps for all the subjects in MNI152_T1_0.7mm.nii.gz template.
> >>>
> >>>
> >>>
> >>> Thanks
> >>> Vasudev
> >>>
> >>>
> >>> _______________________________________________
> >>> 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.
> >>>
> >>
> >>
> >> ------------------------------
> >>
> >> 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
> >
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> ------------------------------
>
> Message: 3
> Date: Mon, 11 Jul 2016 17:42:53 -0400
> From: Chao Zhang <[email protected]>
> Subject: [HCP-Users] fMRI time series intensity normalization
> To: [email protected]
> Message-ID:
> <CAJ04tc0c3cjDvxT_OhbBkq=
> [email protected]>
> Content-Type: text/plain; charset="utf-8"
>
> Hi,
>
> I am using the FIX fMRI dataset. However I have a question about the time
> series preprocessing:
>
> Step 6 in fMRIVolume pipeline said that '6. Intensity normalization to mean
> of 10000 (like in FEAT) and bias field removal. Brain mask based on
> FreeSurfer segmentation.'. But the mean values are not 10000, e.g. I
> checked one subject for which the mean ranges from -115 to 27000.
>
> So this becomes an issue when deriving the ROI/regional time series, what I
> did was first convert to z-score for each time series and then calculate
> the average across time series within the ROI.
>
> I came to recognize that this may not be correct. Another idea is to keep
> all original data and do the average within the ROIs. However, if one ROI
> is very big, then the large difference of the baselines (the mean values)
> of different voxels may make this choice not valid.
>
> I wonder why the the mean value is not actually 10000 and what is the
> correct way to derive the ROI time series. Thanks very much,
>
> Best,
> Chao
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>
> ------------------------------
>
> Message: 4
> Date: Mon, 11 Jul 2016 21:49:01 +0000
> From: "Glasser, Matthew" <[email protected]>
> Subject: Re: [HCP-Users] Data download via script
> To: Miriam Klein-Fl?gge <[email protected]>,
> "[email protected]" <[email protected]>
> Message-ID: <d3a97e70.11e370%[email protected]>
> Content-Type: text/plain; charset="iso-8859-1"
>
> Why not just download the packages?
>
> Peace,
>
> Matt.
>
> On 7/11/16, 3:46 PM, "[email protected] on behalf of
> Miriam Klein-Fl?gge" <[email protected] on behalf of
> [email protected]> wrote:
>
> >Hi all,
> >
> >We are trying to script the download of several participants and
> >folders, but it seems that individual *files* rather than *folders* need
> >to be specified when looking at this description:
> >
> >
> https://wiki.humanconnectome.org/display/PublicData/How+To+Access+Subject+
> >Data+via+REST
> >
> >Since we need the resting state (fix-cleaned and unprocessed) as well as
> >the structural folders of several participants, we wondered if there is
> >a more efficient way of organizing the download of the data?
> >
> >Thanks,
> >
> >Miriam
> >
> >_______________________________________________
> >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.
>
>
>
> ------------------------------
>
> Message: 5
> Date: Mon, 11 Jul 2016 22:01:22 +0000
> From: "Glasser, Matthew" <[email protected]>
> Subject: Re: [HCP-Users] fMRI time series intensity normalization
> To: Chao Zhang <[email protected]>, "[email protected]"
> <[email protected]>
> Message-ID: <d3a98138.11e397%[email protected]>
> Content-Type: text/plain; charset="iso-8859-1"
>
> I don't understand how you are computing the mean. The mean across the
> entire 4D dataset inside the brain mask will be 10000.
>
> Peace,
>
> Matt.
>
> From: <[email protected]<mailto:
> [email protected]>> on behalf of Chao Zhang <
> [email protected]<mailto:[email protected]>>
> Date: Monday, July 11, 2016 at 4:42 PM
> To: "[email protected]<mailto:[email protected]>"
> <[email protected]<mailto:[email protected]>>
> Subject: [HCP-Users] fMRI time series intensity normalization
>
> Hi,
>
> I am using the FIX fMRI dataset. However I have a question about the time
> series preprocessing:
>
> Step 6 in fMRIVolume pipeline said that '6. Intensity normalization to
> mean of 10000 (like in FEAT) and bias field removal. Brain mask based on
> FreeSurfer segmentation.'. But the mean values are not 10000, e.g. I
> checked one subject for which the mean ranges from -115 to 27000.
>
> So this becomes an issue when deriving the ROI/regional time series, what
> I did was first convert to z-score for each time series and then calculate
> the average across time series within the ROI.
>
> I came to recognize that this may not be correct. Another idea is to keep
> all original data and do the average within the ROIs. However, if one ROI
> is very big, then the large difference of the baselines (the mean values)
> of different voxels may make this choice not valid.
>
> I wonder why the the mean value is not actually 10000 and what is the
> correct way to derive the ROI time series. Thanks very much,
>
> Best,
> Chao
>
> _______________________________________________
> HCP-Users mailing list
> [email protected]<mailto:[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.
> -------------- next part --------------
> An HTML attachment was scrubbed...
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> http://lists.humanconnectome.org/pipermail/hcp-users/attachments/20160711/92a578ae/attachment-0001.html
>
> ------------------------------
>
> Message: 6
> Date: Mon, 11 Jul 2016 23:48:33 +0000
> From: "Glasser, Matthew" <[email protected]>
> Subject: Re: [HCP-Users] fMRI time series intensity normalization
> To: Chao Zhang <[email protected]>, "[email protected]"
> <[email protected]>
> Message-ID: <d3a99a58.11e3d4%[email protected]>
> Content-Type: text/plain; charset="iso-8859-1"
>
> This is why people typical subtract the mean image across time for many
> kinds of analyses (or if they concatenate multiple runs).
>
> Peace,
>
> Matt.
>
> From: Chao Zhang <[email protected]<mailto:[email protected]>>
> Date: Monday, July 11, 2016 at 5:57 PM
> To: Matt Glasser <[email protected]<mailto:[email protected]>>
> Subject: Re: [HCP-Users] fMRI time series intensity normalization
>
> Hi Matt,
>
> Sorry I did it wrong. Now the mean of all voxels within one frame is 10000.
>
> What I am concerning about is the mean values of each voxel across time
> (mean of the time series) are very different across the brain. So if one
> ROI includes voxels with very different baselines, can I calculate the
> average time series directly or do I need to do some normalization first?
> For example, I attached one brain image showing the map of mean values of
> each time series, the circle represent one ROI/region (include voxels with
> very different brightness/mean value) for which I want to get the
> representative time series, can I do the average directly? Thanks,
>
> Best,
> Chao
>
> On Mon, Jul 11, 2016 at 6:01 PM, Glasser, Matthew <[email protected]
> <mailto:[email protected]>> wrote:
> I don't understand how you are computing the mean. The mean across the
> entire 4D dataset inside the brain mask will be 10000.
>
> Peace,
>
> Matt.
>
> From: <[email protected]<mailto:
> [email protected]>> on behalf of Chao Zhang <
> [email protected]<mailto:[email protected]>>
> Date: Monday, July 11, 2016 at 4:42 PM
> To: "[email protected]<mailto:[email protected]>"
> <[email protected]<mailto:[email protected]>>
> Subject: [HCP-Users] fMRI time series intensity normalization
>
> Hi,
>
> I am using the FIX fMRI dataset. However I have a question about the time
> series preprocessing:
>
> Step 6 in fMRIVolume pipeline said that '6. Intensity normalization to
> mean of 10000 (like in FEAT) and bias field removal. Brain mask based on
> FreeSurfer segmentation.'. But the mean values are not 10000, e.g. I
> checked one subject for which the mean ranges from -115 to 27000.
>
> So this becomes an issue when deriving the ROI/regional time series, what
> I did was first convert to z-score for each time series and then calculate
> the average across time series within the ROI.
>
> I came to recognize that this may not be correct. Another idea is to keep
> all original data and do the average within the ROIs. However, if one ROI
> is very big, then the large difference of the baselines (the mean values)
> of different voxels may make this choice not valid.
>
> I wonder why the the mean value is not actually 10000 and what is the
> correct way to derive the ROI time series. Thanks very much,
>
> Best,
> Chao
>
> _______________________________________________
> HCP-Users mailing list
> [email protected]<mailto:[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.
>
>
> ________________________________
> 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.
> -------------- next part --------------
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> ------------------------------
>
> Message: 7
> Date: Tue, 12 Jul 2016 10:43:41 +0200
> From: David Hofmann <[email protected]>
> Subject: Re: [HCP-Users] Extracting ROI data from HCP resting state
> data - 2400 data points instead of 1200 ?
> To: "Harms, Michael" <[email protected]>
> Cc: hcp-users <[email protected]>, "Dierker, Donna"
> <[email protected]>
> Message-ID:
> <CAEtivj3_hyjaDhNQKaQzU-S5ygx0tRYy=
> [email protected]>
> Content-Type: text/plain; charset="utf-8"
>
> 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, U?urbil 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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Content preview: Hey David, The difference in signal is normal, it's caused
by the drop out patterns for the two different phase encodings (LR and RL).
Certain regions, such as the OFC, have considerably worse signal drop out
in either hemisphere depending on the phase encoding. [...]
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