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Subject:

Re: Multi-masking for Multiple Comparison Correction

From:

"Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>

Reply-To:

Fromm, Stephen (NIH/NIMH) [C]

Date:

Thu, 2 Oct 2008 08:56:18 -0400

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (145 lines)

Roberto,

Sorry if this isn't addressing your points; the original poster's
question was a little unclear to me, because I wasn't 100% sure what she
meant by "multi-masking."

All I mean is that if you use a mask defined by the same functional data
that you're applying the mask to, the significance will possibly be
inflated.

As for definitions, an example which is somewhat conceptually related is
stepwise regression.  The paper at
    http://publish.uwo.ca/~harshman/ssc2006a.pdf
states, "When model modifications are selected using post-hoc
information (e.g., in stepwise regression) standard estimates of
p-values become biased."  Ultimately, I think my use of "bias" here is
correct, based on definitions given at Wikipedia.

So, what I'm saying here is that the use of a functionally defined mask
can lead to corrected p-values which are likely to be too small.  The
simplest example is using a contrast to mask itself.  The uncorrected
p-values are obviously unaffected by this procedure.  And the corrected
p-values are obviously decreased.

My comment "the mathematics dictates that there is no bias":  I mean
that I assume that there are situations where there's enough
independence (e.g, perhaps between the masking contrast and the contrast
you're masking) that the bias either doesn't exist or is probably
negligible, but I haven't had time to think up rigorous examples.

Best regards,

S

-----Original Message-----
From: [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 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
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>>> 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.
>>>
>
>
>

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