fractional anisotropy describes how anisotropic the movement of water is. In
white matter, because of the hindering of water diffusion by the myelin
sheaths and by the organization of tissue, FA is higher than in gray matter.
MS patients will typically have low FA as compared to controls and high mean
diffusivity in the plaque, but also in other areas of normal appearing white
matter. In fact these reductions of FA have been correlated with disability
in MS patients. Stefano
-----Original Message-----
From: Darren Weber [mailto:[log in to unmask]]
Sent: Thursday, October 31, 2002 4:30 PM
To: [log in to unmask]
Subject: Re: [FSL] FEAToutput, ANALYZE hdr and HRF parameters.
So what conclusions can be made from a group comparison on fractional
anisotropy (FA)? Might you expect differences between normals and multiple
sclerosis patients, due to differences in white matter density?
----- Original Message -----
From: "Marenco, Stefano (NIMH)" <[log in to unmask]>
To: <[log in to unmask]>
Sent: Friday, November 01, 2002 7:30 AM
Subject: Re: FEAToutput, ANALYZE hdr and HRF parameters.
> stand for fractional anisotropy, a measure commonly used in Diffusion
tensor
> imaging (DTI). Stefano
>
> -----Original Message-----
> From: Darren Weber [mailto:[log in to unmask]]
> Sent: Thursday, October 31, 2002 3:55 PM
> To: [log in to unmask]
> Subject: Re: [FSL] FEAToutput, ANALYZE hdr and HRF parameters.
>
>
> What is "FA" data?
>
> ----- Original Message -----
> From: "Erik-Jan Vlieger" <[log in to unmask]>
> To: <[log in to unmask]>
> Sent: Friday, November 01, 2002 1:00 AM
> Subject: Re: FEAToutput, ANALYZE hdr and HRF parameters.
>
>
> > > > Hi - yes, you can easily turn the grand-mean scaling off by editing
> > > > fsl/tcl/feat.tcl and commenting-out/deleting the line
> > > > set thecommand "$thecommand -I $global_mean"
> > > > I'm not sure why you would want to turn this off though?
>
> > > We are analyzing DTI images with Feat (I know what to do with the
design
> > > matrix and the issues on pre-whitening to make it valid), and I would
> like
> > > to see the original FA values.
>
> > I would be interested in doing the same. Could you send me a set of
> > instructions on how to analyze FA data?
>
> Certainly. I would love to get some comments on this design!
>
> In the following design, you look for areas for which patients have higher
> gray-values than controls. If you want to look for areas with lower
> gray-values, in step (2), put the controls at the front, the patients at
the
> end, and correct for this when specifying the model.
>
>
> 1) Perform spatial normalizing on all the FA scans of alle the subjects (I
> use
> flirt for this, with the MNI templates. First I BET a 3D T1W scan, then
> register the B0 image to the betted 3D scan. The betted 3D scan is
> registered
> to the template. After that the combined transformation is applied to the
FA
> image).
> 2) Merge these normalized scans into a 4D data-set (avwmerge), e.g. 30
> controls and 32 patients. Put the controls at the beginning.
> 3) Use Feat for the analysis:
> Data: Set the TR to 1.0
> Pre-Stats: No slice time correction
> No motion correction
> No BET
> Quite some smoothing (I would not know an optimum)
> No intensity normalization (FA are absolute values!)
> No temporal filtering
> Stats: No film prewhitening
> Full Model setup:
> Skip : 0
> Off: 30 (controls)
> On: 32 (patients)
> Convolution: none
> NO temporal filtering
> NO temporal derivative
> Post-stats: whatever you want
> Registration: turn it all off (you already did this).
>
> GO!
>
> Erik-Jan
>
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