On Thu, 31 Jul 2008 18:53:54 +0100, Paul Mazaika 
<[log in to unmask]> wrote:

>Most of the previous replies on this subject described adding regressors to 
the design 
>matrix to get rid of bad outliers. An alternate approach to get rid of outliers 
is 
>interpolation, which may have certain advantages.

While I mainly discussed the method of adding regressors to the design matrix 
in my post, I implicitly mentioned a crude form of interpolation (replacing 
the "bad" volume by an average of its neighbors).

I completely agree with what you write below---not that I've tested it, but 
intuitively one would think that the effect should be small.  (And if there are 
too many bad volumes, the data should be discarded anyway.)

I'd like to add to your post by stressing as I did before that the design matrix 
modification method might be insufficient, in that without some form of 
interpolation prior to slice timing correction, the bad data can be smeared into 
neighboring volumes.  I definitely saw that in some cases of the bad data I 
attempted to ameliorate (banding effects due to intra-volume motion).

Cheers.


>
>Interpolation replaces outlier data points with an average of the nearest 
valid data points. 
>This method removes the effects of outliers from the desired estimates, the 
same as 
>adding a regressor. But instead of changing the design matrix, interpolation 
changes the 
>data going into the design matrix. Interpolation is easy to automate, does 
not involve 
>custom changing of the design matrix for each subject, and can be applied 
per volume, 
>per slice or per voxel as opposed to applying a regressor to an entire volume.
>
>While interpolation can affect the statistical maps, the effect seems to be 
minor. In my 
>experiments, the effects were far less than the algorithm choice of 
smoothing kernel, 
>high pass filter cutoff, or whether or not to use motion regressors. From an 
overall view, 
>the interpolation approximation seems to be a small component of the total 
noise budget 
>relative to scanner thermal noise, head motion, spontaneous deep breaths, 
cardiac and 
>respiratory pulsatility, magnetic susceptibility drift, HRF modeling errors, etc.
>
>For troublesome subject data, it seems the primary goal should be to catch 
ALL the 
>outliers, whether or not they were due to head motion. One implementation 
of this 
>approach is the ArtRepair Toolbox available at the SPM Extensions website. 
The program 
>suite has a visualization option to easily review all the data for outliers. The 
program also 
>can automatically detect large outliers from head motion and other causes, 
and apply an 
>interpolation repair to the data. Documentation is available at the website.
>
>While interpolation is not discussed often in the fMRI literature, it is common 
in other 
>image and signal processing domains. We've found it useful for analyzing fMRI 
data sets 
>from children and clinical subjects.
>
>  - Paul