Could you be more specific? I can't see what you mean by "the
mathematics dictates that there is no bias". It's important to avoid
misunderstandings about the terminology: bias is a technical term,
defined on the power function of the test, and does not mean just
wrong in some way. You should be sure that when you mention bias you
do not mean "conditional on the functional data", as I mentioned in my
> Except in certain circumstances, where you could show that the mathematics
> dictates that there's no bias, defining regions based on the functional data
> itself can definitely bias results, regardless of whether the
> contrast is defined
> a priori.
> Perhaps one can argue that the bias is slight; and it's certainly common
> practice in the neuroimaging community. But, again, procedures that look to
> the data can lead to bias.
> Of course, if one uses separately acquired data to create the contrast-
> defined ROI, that's a different matter.
>> In some specific instance, using the mask approach follows a clear
>> substantive logic. For example, if you are investigating individual
>> differences in cognitive capacity, you may be justified in carrying
>> out a contrast first, and then look at how individual differences
>> modulate the activation say, in prefrontal and parietal areas.
>> You do have to pay for the increased power (if the procedure is really
>> a priori), the price being that you potentially miss an effect in the
>> voxels outside the mask.
>> I do not see any simple way in which the concept of bias relates to
>> this specific situation; I'd rather say that these tests are
>> conditional on the a priori criterion. If the criterion is not a
>> priori, they have wrong significance values (too small), with inflated
>> type I errors.
>> When you use a cluster approach, you also have to specify a priori a
>> cluster definition threshold. Your p values are conditional on this
>> threshold. If you try several thresholds, your test will have wrong p
>> All the best,
>> Roberto Viviani
>> University of Ulm, Germany
>> Quoting Amy Clements <[log in to unmask]>:
>>> Dear Experts,
>>> I am pretty far away from having statistical expertise, which is why
>>> I am posing my question to the group. Recently, I have seen a
>>> multitude of papers that are using a multi-masking approach to deal
>>> with corrections for multiple comparisons (using main effect or
>>> other effects of interest contrasts masks). While on the surface
>>> this appears to seem like an optimal approach because you are
>>> restricting the number of voxels included in the multiple
>>> comparison, it seems like an opportunity for biasing the data and
>>> obtained results--especially if you are not masking the data based
>>> from a priori hypotheses (e.g., using a previously defined
>>> functional ROI mask because you're interested in face processing).
>>> I'm not sure that I've articulated this is the best way. It seems,
>>> like I mentioned previously, to have the potential to bias results,
>>> but would greatly appreciate feedback. The questions typically
>>> asked from the lab that I've worked in have been better suited to
>>> utilizing a cluster-based approach; however, could also be served by
>>> Amy Stephens
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