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Subject:

AW: [FSL] TICA on resting state

From:

Andreas Bartsch <[log in to unmask]>

Reply-To:

FSL - FMRIB's Software Library <[log in to unmask]>

Date:

Wed, 18 Jun 2008 14:30:50 +0200

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Hi,
 
it depends what you want but yes, possibly.
You can use fslspec to get the power spectra of your 4D data prior to feeding them into TICA. If it runs too slow for you use matlab or R with the appropriate import / export.
>does this mean there is power spectrum information in each voxel? 
Yes.
>Are networks then identified by shared power spectra? 
Yes.
>And when group differences are evaluated in contrasts, a significant difference means that the spatial maps identified within the power spectrum for that component are different between groups?
No!;) It means that the power spectrum of a given component is attenuated or augmented, i.e. that there is significantly more (or less) power to that very component. It can be due to more / less prominent peaks, phase shifts, contaminating frequencies aside from the main peaks ... Note that quantifying power spectra across subjects is certainly tricky. So what you would typically do is to go back to your data and extract the relevant information (using either fslmaths/meants and / or a formal GLM testing).
Hope that helps,
Cheers-
Andreas

________________________________

Von: FSL - FMRIB's Software Library im Auftrag von Christina Hugenschmidt
Gesendet: Mi 18.06.2008 12:51
An: [log in to unmask]
Betreff: [FSL] TICA on resting state



Hi all (and Christian in particular),

I am interested in running a TICA analysis comparing resting state and
also video watching between two groups. From reading the posts and
information on the FSL website, I think I should be using the same
methodology described in the Damoiseaux 2007 Cerebral Cortex paper, where
the data is fourier transformed prior to running the TICA analysis. I was
wondering if you could tell me the process used to transform the data
prior to running the TICA?

I would also like to be sure that I understand exactly what is being
compared between groups in this analysis. When the data is transformed,
does this mean there is power spectrum information in each voxel? Are
networks then identified by shared power spectra? And when group
differences are evaluated in contrasts, a significant difference means
that the spatial maps identified within the power spectrum for that
component are different between groups?

Thanks for any insights you can offer,

Christina

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