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•Q˜±U |