Well I processed the pilot data and have said that it is not recommended that
you use that for analysis. The recommended starting data is the ICA+FIX
cleaned data which has been registered with MSMAll and is named something like
this:
${StudyFolder}/${Subject}/MNINonLinear/Results/${fMRIName}/${fMRIName}_Atlas_MSMAll_hp2000_clean.dtseries.nii
If you are interested in looking at dense timeseries movies or concatenating
across runs, you will want to remove the mean image from the data. Next you
will need to decide whether you want to variance normalize the data (if you do,
you will want to multiply the bias field back into the data and divide by the
variance normalization map—these files are included in the same package as the
above). Variance normalized data has an equal chance of false positives across
space, but isn’t interpretable in terms of %BOLD deviation across space. The
included bias field correction isn’t quite optimal and we haven’t had a chance
to create the data that enables a better bias field correction.
It is indeed true that if you reconstruct the data from the signal ICA
components that a given run has, it will look a lot smoother spatially and
temporally, because you will have thrown out all of the unstructured noise.
This might be okay for individual subject analysis, but you wouldn’t want to do
this for group analysis (because you would not be able to get the benefits of
cross-subject averaging for bringing out weaker components). I’ve ended up
doing something different which we call “Wishart Rolloff” which is described in
this document:
http://www.humanconnectome.org/documentation/mound-and-moat-effect.html
This makes a bit fewer assumptions than does the ICA-based reconstruction
approach, and so I have moved away from the ICA-recon approach. In any case, I
don’t really understand exactly what you are trying to do with the data…
Peace,
Matt.
From: Amrit Kashyap <[email protected]<mailto:[email protected]>>
Date: Monday, August 15, 2016 at 10:35 AM
To: Matt Glasser <[email protected]<mailto:[email protected]>>
Cc: "Elam, Jennifer" <[email protected]<mailto:[email protected]>>,
"[email protected]<mailto:[email protected]>"
<[email protected]<mailto:[email protected]>>
Subject: Re: [HCP-Users] Workbench Tutorial Data
[https://ssl.gstatic.com/docs/doclist/images/icon_10_generic_list.png]
HCPfilterdNorm.mpg<https://drive.google.com/file/d/0B21oKdbljNLvaThnSy11anFqQzA/view?usp=drive_web>[X]
[https://ssl.gstatic.com/docs/doclist/images/icon_10_generic_list.png]
HCPicacleaned.mpg<https://drive.google.com/file/d/0B21oKdbljNLvOE9ISlVLdGt3cmc/view?usp=drive_web>[X]
[https://ssl.gstatic.com/docs/doclist/images/icon_10_generic_list.png]
HCPMSALLraw.mpg<https://drive.google.com/file/d/0B21oKdbljNLveUE1SzNHaFZrQzg/view?usp=drive_web>[X]
[https://ssl.gstatic.com/docs/doclist/images/icon_10_generic_list.png]
HCPpilot.mpg<https://drive.google.com/file/d/0B21oKdbljNLvYTlxR1dXbjlXY2M/view?usp=drive_web>[X]
Hey HCP,
I have downloaded the raw data that you have suggested and I tried many
techniques unsuccessfully (filtering, smoothing, normalizing, ... etc) to try
to transform the data into the signal that I have observed in the scene 4 data.
I have included 4 files below to show the difference between the HCP pilot
processing vs the raw data and vs my own processing.
I believe that this signal (HCP pilot) is probably closer to the true signal
that we are interested in, because it seems like the dynamics of the
spatio-temporal pattern has normalized correctly over the entire brain,
resulting in a smooth signal that transitions from one region to another. The
raw signal (MSMAII in this case but ICA+fix is very similar) is not normalized
across the brain space and shows most of the activity clustered around regions
that inherently have more activity. Normalizing and filtering results in a
signal that looks similar to the HCP pilot, but contains large amounts of
spatial noise that is not seen in the HCP pilot data. I believe that probably
the HCP pilot has been spatially filtered but I am not sure. I tried projecting
the data into the 300 ICA components as well, and then reconstructing the
signal from these components to clean the signal and it results in the fourth
file which looks interesting but does not have the smooth dynamics seen from
the pilot data.
I think that someone has done an amazing job in post-processing the HCP pilot
data and their work is a great interest for the scientific community. The last
few weeks I have attempted to guess what has been done to the signal and try to
apply it myself but this is not getting me very far. If you have any
documentation about the pilot study or the code that generated the processing
for the HCP pilot data I would be incredibly grateful since I am struggling to
reproduce its results. For my present work, I am trying to extend the spatial
functional connectivity maps into a temporal domain. For this I need very
smooth signals both spatially as well as temporally and the HCP pilot data
gives me promising results in decomposing the dynamics.
Thanks so much for all the help you have already provided and appologize for
the long email,
Cheers
Amrit
On Wed, Jul 27, 2016 at 9:46 AM, Glasser, Matthew
<[email protected]<mailto:[email protected]>> wrote:
ICA+FIX for all usecases, but otherwise it depends on what you are doing.
Peace,
Matt.
________________________________
From: Amrit Kashyap <[email protected]<mailto:[email protected]>>
Sent: Wednesday, July 27, 2016 8:20:51 AM
To: Glasser, Matthew
Cc: Elam, Jennifer;
[email protected]<mailto:[email protected]>
Subject: Re: [HCP-Users] Workbench Tutorial Data
Awesome, thanks a bunch I will try it out. Just out of curiosity what
preprocessing would you recommend to clean up the signal (filtering,
interpolation, downsampling)?
Cheers
Amrit
On Tue, Jul 26, 2016 at 1:50 PM, Glasser, Matthew
<[email protected]<mailto:[email protected]>> wrote:
There is a wb_command function that does this, wb_command -cifti-smoothing. We
don’t recommend smoothing as a general preprocessing practice however.
Peace,
Matt.
From: Amrit Kashyap <[email protected]<mailto:[email protected]>>
Date: Tuesday, July 26, 2016 at 12:47 PM
To: "Elam, Jennifer" <[email protected]<mailto:[email protected]>>
Cc: Matt Glasser <[email protected]<mailto:[email protected]>>,
"[email protected]<mailto:[email protected]>"
<[email protected]<mailto:[email protected]>>
Subject: Re: [HCP-Users] Workbench Tutorial Data
Okay, I will use the regular data then and try to process it so that my
algorithm runs on it. Do you guys know by any chance a good way to project the
surface data contained in the cifti-files into a flat 2D map given in the flat
gifti files? (I am trying to get the cifti data into a 2D matrix so I can
convolve with a Gaussian kernel).
On Tue, Jul 26, 2016 at 12:46 PM, Elam, Jennifer
<[email protected]<mailto:[email protected]>> wrote:
Hi Amrit,
The data in Scene 4 of the WB Tutorial data is almost 5 years old now, CP10101
is a subject from the pilot phase of the HCP, well before we were releasing
data and well before we were cleaning the data with FIX. The reason that scene
is there is simply to display the concept of BOLD fluctuations in an fMRI time
series-- it is not the really meant to be data that you use any analysis tools
on since we have since released several improved versions of the fMRI data on
now over 800 subjects.
The Group Average Workbench dataset available in
ConnectomeDB<https://db.humanconnectome.org/data/projects/HCP_900> is up to
date with data from the current 900 Subjects Release (of December 2015)
Yes, there is extensive documentation on the many individual subject and group
average analyzed data currently available for download in ConnectomeDB in the
900 Subjects Reference
Manual<https://www.humanconnectome.org/documentation/S900/HCP_S900_Release_Reference_Manual.pdf>
(you can also get to it in the Documentation section of the HCP public
website) and more info in the publication references therein. It's a long
document, but there's a lot of valuable information there.
Best,
Jenn
Jennifer Elam, Ph.D.
Scientific Outreach, Human Connectome Project
Washington University School of Medicine
Department of Neuroscience, Box 8108
660 South Euclid Avenue
St. Louis, MO 63110
314-362-9387<tel:314-362-9387>
[email protected]<mailto:[email protected]>
www.humanconnectome.org<http://www.humanconnectome.org/>
________________________________
From:[email protected]<mailto:[email protected]>
<[email protected]<mailto:[email protected]>>
on behalf of Amrit Kashyap <[email protected]<mailto:[email protected]>>
Sent: Tuesday, July 26, 2016 11:34:48 AM
To: Glasser, Matthew
Cc: [email protected]<mailto:[email protected]>
Subject: Re: [HCP-Users] Workbench Tutorial Data
Yes, I am using that because I thought that was the most processed data
available. I think someone might have spatially smoothed this raw signal and
then added it to the tutorial data?
On Tue, Jul 26, 2016 at 11:08 AM, Glasser, Matthew
<[email protected]<mailto:[email protected]>> wrote:
Are you using the FIX cleaned HCP data? It will have _hp2000_clean in the file
name.
Peace,
Matt.
From:
<[email protected]<mailto:[email protected]>>
on behalf of Amrit Kashyap <[email protected]<mailto:[email protected]>>
Date: Tuesday, July 26, 2016 at 9:52 AM
To: "[email protected]<mailto:[email protected]>"
<[email protected]<mailto:[email protected]>>
Subject: [HCP-Users] Workbench Tutorial Data
Hey HCP, this might not be the correct place to post this, but I have been
using the rsfMRI data provided by the HCP workbench tutorial in scene 4
(CP10101). It looks like there has been some preprocessing done to the rsfMRI
dtseries but I am not sure what it is. My own analysis techniques seem to work
pretty well on this processed data but perform pretty poorly on the raw data.
Is there any documentation/paper you could direct me to?
Thanks
Amrit
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The materials in this message are private and may contain Protected Healthcare
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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.
________________________________
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.
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