Hi Simon,
I agree with you: lowering the threshold is not a good idea. I will
check SVC. However, my analysis is whole brain.
Yes, permutation (as opposed to bootstrap) is the obvious cure to these
problems. The two are not equivalent, however. My reading of Efron &
Tibshirani's 1993 book, p. 224 is that the bootstrap approach allows
better targeted null hypotheses, hence my preference.
Insisting on the bootstrap: my thought is that in principle the mean
centering should not affect the ResMS image, hence the idea of plugging
in what estimated with non-mean-centered data into the analysis of
mean-centered data.
Thank you for your suggestions,
Bruno
On 14/05/2012 5:20 PM, Eickhoff, Simon wrote:
> Dear Bruno
>
>
> The problem is, when you use non-sphericity correction in your actual analysis, you will not be able to produce data under the null-hypothesis given the F-threshold. You *could* lower that to, e.g., p<0.05 or even p<0.5 to force SPM to consider also those voxels in the REML estimation where your model doesn't fir, given that it actually fits nowhere. But it's hard to predict, what will happen then.
>
> As an alternative, why don't you just use SVC as implemented in SPM? This would be the much more straightforward approach to inference in a particular ROI. In particular, since you will be quite vulnerable with your null-distribution given that there is no principaled way to specify the variance under the null-distribution a priori. As an alternative, you could use permutation-based statistics (SnPM or the randomise tool in FSL)
>
>
> Best
> Simon
>
>
> ________________________________________
> Von: SPM (Statistical Parametric Mapping) [[log in to unmask]]" im Auftrag von"Bruno L. Giordano [[log in to unmask]]
> Gesendet: Montag, 14. Mai 2012 18:13
> An: [log in to unmask]
> Betreff: Re: [SPM] AW: [SPM] Estimating a second-level model under the null hypothesis
>
> Hello Donald and Simon,
>
> the reason for analyzing such mean-centered data is that I need to
> estimate the maximum value of my statistics within the analysis mask
> under the null hypothesis of no condition effect. This involves a
> resampling scheme (I am referring to the bootstrap-F principle): the
> FWE-corrected p-value of the statistic computed with unaltered data can
> be computed from the observed distribution of the maximum statistics
> across analyses of resampled mean-centered data (percentile method).
>
> Donald: I tried adding a constant, it did not help, and disabling
> sphericity corrections and assuming independence is not a good idea.
>
> I am thinking that the correct approach here would be to consider the
> ResMS and variance estimates from the unaltered data, and plug in those
> when analyzing the mean-corrected data. It is not obvious if this would
> be enough (e.g., should I consider eventual whitening? I am not 100%
> familiar with the mechanics).
>
> Thank you for your thoughts,
>
> Bruno
>
>
>
>
> On 14/05/2012 4:46 PM, MCLAREN, Donald wrote:
>> What you want to do is to disable the REML estimation (e.g
>> non-sphericity correction) OR setup the model to assume that everything
>> is independent (this sets the covariance to 0). Of course, the latter
>> option is bad for of obvious reasons.
>>
>> An alternative might be to add 1 to all the input images. The issue then
>> becomes how to subtract out the 1 from the betas to make the contrasts
>> test things agaisnt 0. If you are comparing conditions, the added 1 to
>> all images should have no effect on the statistics. This should find the
>> significant voxels for REML estimation as well.
>>
>> Best Regards, Donald McLaren
>> =================
>> D.G. McLaren, Ph.D.
>> Postdoctoral Research Fellow, GRECC, Bedford VA
>> Research Fellow, Department of Neurology, Massachusetts General Hospital
>> and
>> Harvard Medical School
>> Website: http://www.martinos.org/~mclaren
>> <http://www.martinos.org/%7Emclaren>
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>> On Mon, May 14, 2012 at 11:40 AM, Eickhoff, Simon
>> <[log in to unmask]<mailto:[log in to unmask]>> wrote:
>>
>> Dear Bruno
>>
>>
>> The problem you encounter may relate to the fact, that only voxels
>> passing an F-threshold of p<0.001 (spm_spm ~ line 450), i.e., those
>> voxels where your model fits reasonably well, enter the
>> non-sphericity estimation (cf. ~ line 700).
>>
>> If the analysis crashed with an "no inmask voxels", that should be
>> the reason (your noise-model didn't fit anywhere)
>>
>>
>> If the analysis ran through but you got "no significant voxels" in
>> the inference... this is exactly what happend: Nothing was
>> significant, which is not surprising given you only entered noise.
>>
>> It's not clear to me, however, why you would want to fit that model
>> anyways, given you know the mean and variance of your data since you
>> actually generated it from a particular generative model.
>>
>>
>> Best
>> Simon
>>
>>
>>
>>
>> ________________________________________
>> Von: SPM (Statistical Parametric Mapping) [[log in to unmask]
>> <mailto:[log in to unmask]>]" im Auftrag von"Bruno L.
>> Giordano [[log in to unmask]<mailto:[log in to unmask]>]
>> Gesendet: Montag, 14. Mai 2012 17:00
>> An: [log in to unmask]<mailto:[log in to unmask]>
>> Betreff: [SPM] Estimating a second-level model under the null hypothesis
>>
>> Dear list,
>>
>> I am trying to fit a null model second level model where the
>> across-subjects mean of each condition is set to 0. I need the parameter
>> estimates for resampling under H0 of no between-condition difference.
>> SPM protests that I have no significant voxel. I would need the
>> estimates/stats anyways.
>>
>> What could I do?
>>
>> Thank you,
>>
>> Bruno
>>
>>
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