Subject: | | Re: Multi-masking for Multiple Comparison Correction |
From: | | Roberto Viviani <[log in to unmask]> |
Reply-To: | | [log in to unmask][log in to unmask] [mailto:[log in to unmask]] >> Sent: Thursday, October 02, 2008 8:16 AM >> To: Fromm, Stephen (NIH/NIMH) [C] >> Cc: [log in to unmask] >> Subject: Re: Multi-masking for Multiple Comparison Correction >> >> 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 >> mail. >> >> R.V. >> >> <snip> >>> 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 >>>> values. >>>> >>>> All the best, >>>> Roberto Viviani >>>> University·êU |
Date: | | Wed, 1 Oct 2008 06:43:35 +0200 |
Content-Type: | | text/plain |
Parts/Attachments: |
|
|
|
|
Hallo Amy,
I do not see why using an a priori predefined ROI is any different
from using a restriction to an a priori predefined contrast. It all
depends on whether the a priori criterion is really a priori, or is in
reality ex post masqueraded as a priori.
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
values.
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
> multi-masking.
>
> Thanks!
>
>
> Amy Stephens
>
>
>
>
>
> Disclaimer:
> The materials in this e-mail are private and may contain Protected
> Health Information. Please note that e-mail is not necessarily
> confidential or secure. Your use of e-mail constitutes your
> acknowledgment of these confidentiality and security limitations. If
> you are not the intended recipient, be advised that any
> unauthorized use, disclosure, copying, distribution, or the taking
> of any action in reliance on the contents of this information is
> strictly prohibited. If you have received this e-mail in error,
> please immediately notify the sender via telephone or return e-mail.
>
|
|
|
|