Dear Rik,
thanks for your explanations, that all makes sense. And thanks for
commenting further on the stringent criteria of forward inference, I
will try to be as close as possible to this aspiration !
Sincerely,
Elise
Le 21/05/2010 10:39, Rik Henson a écrit :
>> Dear Rik,
>>
>> Thank you very much for your detailed and clear response, it helps a
>> lot. If you don't mind I have a quick related question which actually
>> concerns the use of unbiased statistics in the context of forward
>> inferences. Let's say I identified, based on a whole-brain analysis, 2
>> regions that respond to opposite contrasts (i.e. A>B in region 1 and B>A
>> in region 2). If I want to make the point of a qualitative difference
>> between regions 1& 2, I need to show a common activation/deactivation
>> of A or B vs C in both regions 1& 2 (eg A>C in regions 1& 2).
>>
> Well, that's what I would argue, yes! (but others might argue that these criteria are too stringent...)
>
>
>
>> Now if I correctly understand your previous response, it seems that testing the
>> A>C difference in regions 1& 2 only (by means of an ROI analysis) is
>> not correct, and that I should instead use a whole-brain mask of the
>> A>C contrast to avoid any statistical bias. Am I correct ?
>>
> Well, ideally, your two regions would be identified independently by, eg, anatomy, a prior functional study, or a contrast on the same data that is orthogonal to both the [1 -1 0] contrast on A vs B, AND the [1 0 -1] or [0 1 -1] contrasts on A/B vs C (eg [1/3 1/3 1/3 -1] relative to some fourth baseline condition D). Sorry! I realise this is a tough set of constraints to meet (that I have not yet met myself!), so a qualitative difference as defined in those 2005/2006 papers should perhaps be viewed as an aspiration rather than strict necessity! (in reality, I think evidence is a continuum of weaker to stronger data patterns, rather than black-or-white). Ho hum.
>
> Best wishes
> Rik
>
>
>
>
>
>> Thanks a lot again for your time and help, I really appreciate it.
>> Sincerely,
>> Elise
>>
>>
>> Le 20/05/2010 21:11, Rik Henson a écrit :
>>
>>> Elise -
>>>
>>> In the ideal case where the regressors (columns) of your design
>>>
>> matrix are orthogonal (e.g, if you have a 2nd-level design with equal
>> numbers of subjects in each condition A to C), and the error is i.i.d
>> (white), then all that matters is whether your contrast weights are
>> orthogonal.
>>
>>> If so, then taking your first example, and assuming your conditions
>>>
>> are ordered [A B C], then you cannot use contrast weights [1 -1 0] to
>> select a region from which to extract data (or use for SVC) for a
>> second contrast with weights [1 0 -1] without inducing some statistical
>> bias (since [1 -1 0]*[1 0 -1]' ~= 0).
>>
>>> In your second example, where you split condition A into two halves
>>>
>> A1 and A2, and use contrast weights [1/2 1/2 0 -1] (ordered [A1 A2 B
>> C]) to select a region for subsequent testing of [1 -1 0 0], then the
>> contrast weights (and regressors) are orthogonal, but the error may not
>> be i.i.d (variance associated with the A1 and A2 estimates is likely to
>> be greater). The deviation from i.i.d. is unlikely to be large though.
>>
>>> If you have correlated regressors (e.g, in a 1st-level fMRI design),
>>>
>> then you could use the conjunction option to create orthogonal
>> contrasts (contrasts are a function of the contrast weights and the
>> design matrix). There may also be (auto)correlation in the error, but
>> again this should be small. For a more precise answer, see Karl's
>> email:
>>
>>> https://www.jiscmail.ac.uk/cgi-
>>>
>> bin/wa.exe?A2=ind0904&L=SPM&P=R89389&I=-3
>>
>>> Best wishes
>>> Rik
>>>
>>> ________________________________________
>>> From: SPM (Statistical Parametric Mapping) [[log in to unmask]] on
>>>
>> behalf of elise metereau [[log in to unmask]]
>>
>>> Sent: 20 May 2010 18:59
>>> To: [log in to unmask]
>>> Subject: [SPM] non independent analyses ?
>>>
>>> Dear SPM experts,
>>>
>>> I have made some reading on the issue of non-independent analyses and
>>> "double dipping", and now I'm wondering about non independence -and
>>>
>> thus
>>
>>> validity- of the following analyses:
>>>
>>> 1) Let's say I have an fMRI experiment with 3 conditions A, B and C.
>>>
>> The
>>
>>> contrast A>B gives a significant cluster. Now I want to make sure
>>>
>> that
>>
>>> the contrast A>C is also significant in this region, and to this aim
>>>
>> I
>>
>>> extract the % signal change from the above cluster (separately for
>>>
>> each
>>
>>> particpant and each condition) and compare the A and C conditions
>>>
>> using
>>
>>> a paired t-test. Is such an ROI analysis valid or considered a case
>>>
>> of
>>
>>> non independence ? (I could also use a whole-brain mask, but
>>>
>> considering
>>
>>> I'm focusing on one particular cluster, I'm wondering whether the
>>>
>> less
>>
>>> conservative ROI approach might not be sufficient)
>>>
>>> 2) Let's say I'm comparing condition A versus a control condition C
>>>
>> and
>>
>>> find a significant cluster. Let's now imagine that condition A can
>>> actually be split into 2 categories A1 and A2, and that I want to
>>> determine whether activity in the previous cluster is modulated by
>>>
>> these
>>
>>> categories. Similarly, is it valid to compare A1 versus A2 in this
>>>
>> ROI,
>>
>>> or is it a case of non independence ?
>>>
>>> Any insight will be greatly appreciated.
>>> Best,
>>>
>>> Elise
>>>
>
> -------------------------------------------------------
> Dr Richard Henson
> MRC Cognition& Brain Sciences Unit
> 15 Chaucer Road
> Cambridge
> CB2 7EF, UK
>
> Office: +44 (0)1223 355 294 x522
> Mob: +44 (0)794 1377 345
> Fax: +44 (0)1223 359 062
>
> http://www.mrc-cbu.cam.ac.uk/people/rik.henson/personal
> -------------------------------------------------------
>
|
