Thanks for your point of view also.

Best
Torben


Den 05/06/2008 kl. 14.46 skrev Steve Smith:

> I think you could argue that quantitative value is stronger than  
> that. If you view the ICA processing as a black box, and consider  
> that you have fed your data into it, and got out a spatial map and a  
> timecouse, and then you do a t-test between the timecourse and some  
> model that you already had, then the p-value is perfectly valid. At  
> the most you might Bonferroni-correct for the number of ICA  
> components you had, or if you had a good reason for choosing the  
> particular component (e.g. it's the only one that looks spatially  
> remotely like the default mode network) then you wouldn't  
> necessarily even need to do that.
>
> Cheers.
>
>
> On 5 Jun 2008, at 13:31, Torben Ellegaard Lund wrote:
>
>> Hi Christian
>>
>> Thanks for some illustrative Gedankenexperiments. Would you agree,  
>> that since the effect of the PCA reduction on the noise space is  
>> the effect is not easily predictable, the F and p-values should  
>> mainly be used as useful tool for sorting components?
>>
>> Best
>> Torben
>>
>>
>>
>> Den 05/06/2008 kl. 06.43 skrev Christian F. Beckmann:
>>
>>> Hi Torben,
>>>
>>> The data reduction in the PCA is not accounted for in the dof  
>>> calculation - that would not make much sense as the reduction is  
>>> data driven and the F-test is calculated across time, not PCA  
>>> loadings space. As a thought experiment, imagine reducing the  
>>> subspace to 1 (i.e. only retaining the largest Eigenvector,  
>>> whatever that is in your data) and then finding that a single  
>>> regressor fits really well. Would you be inclined to say that dof2  
>>> should be 1, so no matter what you're not surprised at all?  
>>> Conversely, if you find a nice fit between an IC and a regressor  
>>> for d=30, say, you could re-run with d=900, find the same  
>>> component and then see an increase in F that is very  
>>> significant.... that's clearly not right. You're right in thinking  
>>> that the PCA somewhat could be understood as a filter, but because  
>>> it's data driven the effect on the noise space is not easily  
>>> predictable. One could in theory calculate the F in the loadings  
>>> space, after projecting the GLM design into the same PCA subspace  
>>> but this would not get around the issue that e.g. if you retain  
>>> only one EIgenvector, even a F-value of 190 does not get you to  
>>> p<0.05 with 2 and 1 dofs...
>>> cheers
>>> Christian
>>>
>>> On 4 Jun 2008, at 15:28, Torben Ellegaard Lund wrote:
>>>
>>>> Dear List (Christian in particular)
>>>>
>>>> Im am trying to sort the components from a MELODIC analysis based  
>>>> on a design.mat file. The original dataset consisted of 1000  
>>>> volumes each containing 33 slices. MELODIC estimated the number  
>>>> of components after PCA reduction to be 677, so in interest of  
>>>> time I stopped the program and manually specified the number of  
>>>> components to 50. As far as I understand the algorithm, my data  
>>>> are now projected into this 50 dimensional subspace. The F-test  
>>>> on full model fit nicely show that the 2 regressors in my  
>>>> design.mat file do indeed have a significant contribution to some  
>>>> of the components, and that is basically what I would like to  
>>>> demonstrate. I assume the (uncorrected for # comp) refers to the  
>>>> the p-values not being corrected for the number of tests made.  
>>>> This is OK since I can multiply the p-values with the number of  
>>>> tested components to get Bonferroni corrected p-values. What  
>>>> worries me a bit is the way the degrees of freedom is calculated:  
>>>> dof1=2 is OK but dof2=997 seems to indicate that the reduction of  
>>>> the data from a 1000 dimensional space to a 50 dimensional space  
>>>> has not been taken into account, isn't this a problem? I am  
>>>> preparing for a talk on RSN at the HBM so I would really  
>>>> appreciate your comments on this.
>>>>
>>>> Best
>>>> Torben
>>>>
>>>>
>>>>
>>>>
>>>> Torben Ellegaard Lund
>>>> Assistant Professor, PhD
>>>> The Danish National Research Foundation's Center of Functionally  
>>>> Integrative Neuroscience (CFIN)
>>>> Aarhus University
>>>> Aarhus University Hospital
>>>> Building 30
>>>> Noerrebrogade
>>>> 8000 Aarhus C
>>>> Denmark
>>>> Phone: +4589494380
>>>> Fax: +4589494400
>>>> http://www.cfin.au.dk
>>>> [log in to unmask]
>>>>
>>>>
>>>> <Billede 4.png>
>>>>
>>>>
>>>>
>>>>
>>>>
>>>>
>>
>
>
> ---------------------------------------------------------------------------
> Stephen M. Smith, Professor of Biomedical Engineering
> Associate Director,  Oxford University FMRIB Centre
>
> FMRIB, JR Hospital, Headington, Oxford  OX3 9DU, UK
> +44 (0) 1865 222726  (fax 222717)
> [log in to unmask]    http://www.fmrib.ox.ac.uk/~steve
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