Interesting,
 thanks for the numbers. I am using FEAT and ICA+FIX as my preprocessing as
well, so that should be less of an issue.

Of note, my impression from the wb_command help was that
correlation-gradient was doing BOTH correlation and gradient->average...
wrapped up into one tool. is this not correct and one needs to run
cifti-correlation prior???

(also, any idea regarding the click-view of correlation maps? Donna?)

-Alex


----------------------------------------------------------------
*Alexander Li Cohen, M.D., Ph.D.*
Resident Physician
Child and Adolescent Neurology
E-mail: [email protected] (Medical/Science Email)
E-mail: [email protected] (Lifetime Email)

*Mayo Clinic*
200 First Street SW
Rochester, MN 55905
mayoclinic.org
----------------------------------------------------------------

On Wed, Apr 22, 2015 at 2:45 PM, Glasser, Matthew <[email protected]>
wrote:

>  Hi Alex,
>
>  For group data I usually use a gradient pre-smoothing of 1mm sigma,
> though for some modalities like myelin maps, even this reduces some detail
> (you might try both ways…).  For seed exclusion I use 2mm (the acquisition
> resolution), basically the goal is to exclude the central spike in
> correlation.  Recently we’ve found that the fisher z transform distorts
> gradients some (relative to other modalities) and global signal
> regression/MGTR distorts them more (basically moving gradients towards
> cortical areas that are more correlated with the global sensory systems
> signal).   This is one of the reasons we’ve stopped doing global signal
> regression/MGTR, though there remains debate on this issue in some quarters
> of the HCP.  To avoid using things like global signal regression, use of a
> denoising approach like ICA+FIX is absolutely critical (and is important
> anyway, as past approaches to denoising did not address spatially specific
> temporal artifacts in a data driven way, possibly leading to things like
> distance dependent artifacts that are possibly then exacerbated by using
> global signal regression).
>
>  There is also a difference of opinion on edge detection and we don’t use
> it (preferring to stay closer to the original data and avoid thresholding),
> though others do.  We have not implemented edge detection in Connectome
> Workbench, but I believe someone has code that uses wb_command outputs and
> computes this in matlab.
>
>  This is how you visualize CIFTI dense connectomes .dconn.nii, and so
> this is a core feature of Connectome Workbench.  Note that one uses
> -cifti-correlation to compute the dense connectome and then
> -cifti-correlation-gradient to compute the gradients.
>
>  Peace,
>
>  Matt.
>
>   From: Alex Cohen <[email protected]>
> Reply-To: "[email protected]" <[email protected]>
> Date: Wednesday, April 22, 2015 at 2:19 PM
> To: "[email protected]" <[email protected]>
> Subject: [HCP-Users] cifti-correlation-gradient settings and
> surface-based math?
>
>   Hi all,
> Two quick (ok three) fcMRI questions:
>
>  - What settings have you all been using for calculating surface-based
> gradients with resting BOLD data for surface presmoothing, seed exclusion
> and the like?
>
>  - Is there a a list of methods using wb_command for doing surface-based
> image processing beyond simple gradient (e.g., like edge detection, etc...)
> I know there is extrema detection, but just wondering what else is
> available... and/or how to implement things like this...
>
>  - As a side-note, back in the Caret days there was a function where you
> could click on the surface and the associated correlation map would appear
> on the surface (assuming it was already made)... Did this make it into
> wb_view? and/or was adapted for CIFTI files containing correlation maps?
>
>  -Alex
>
> ----------------------------------------------------------------
> *Alexander Li Cohen, M.D., Ph.D.*
> Resident Physician
> Child and Adolescent Neurology
> E-mail: [email protected] (Medical/Science Email)
> E-mail: [email protected] (Lifetime Email)
>
> *Mayo Clinic*
> 200 First Street SW
> Rochester, MN 55905
> mayoclinic.org
> ----------------------------------------------------------------
>
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