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Subject:

second-level analyses and sensitivity

From:

Karl Friston <[log in to unmask]>

Reply-To:

Karl Friston <[log in to unmask]>

Date:

Fri, 1 Oct 1999 15:18:28 +0100 (BST)

Content-Type:

text/plain

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text/plain (48 lines)

Dear Jack,

> As I was reading Andreas question's, I felt concerned with this
> problem.  The original study consist of 250 scans * 12 subjects * 2
> sessions (6000 scans) As I thought previously that SPM99b may have the
> same limitations as winSPM (~800 scans), I first performed a "data
> reduction" namely by making 1 mean image out of 10 after realignment,
> and before performing the rest of the analysis (~500 scans remaining).
> 
> Unfortunately as there is a large amount of covariates, there is a 
> tremendous loss of degree of freedom with some influence on the level of 
> statistical inference (~250 residual ddl).
> 
> We have interesting results that looks very stable using even high
> uncorrected thresholds (less statistically significant). Unfortunately
> the used thresholds remain weak (alfa < 0.001 uncorrected for the main
> subtraction, alfa < 0.025 for the factorial design - gloups !).  Sure
> that the right way should be to perform a true random effect study, but
> it may be that some of the interesting results of the factorial study
> may not survive to this further loss of df.

You can perform second-level analyes on epoch-specific data.  It sounds
as if you have emulated this sort of analysis.  The sphere of inference
then generalizes to all possible epochs in that session[s] for that
subject[s].
> 
> This will probably sounds absurd for statisticians, but I was wandering
> whether if could be possible to reset the df at 10* the number of
> scans - the covariates.  As it should only be used while performing the
> {T} statistic, it could be of limited work to change it ?  This may
> sounds as tinkering, but is it really that absurd ?  Thanks for any
> comment.

I am afraid it is probably 'absurd'.  Generally activations should be
detected with 64 or more df.  If you have ~250 then this is more than
enough.  If you have too many df then any slight [biologically
meaningless] departure from the null hypothesis will give you
significant results.

I would focus on anatomically constrained interpretations of your data.
If you get a result at p < 0.025, in a region predicted in advance,
then this is sufficient to reject the null hypothesis.

I hope this helps - Karl


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