[pymvpa] question about detrend and zscore

[Matthias Ekman]

Hi,

mh - may be I am wrong here, but from my point of view, high-pass
filtering AND detrending is not "necessary". What happens if you
zscore/detrend your data without step (4). My guess would be, that this
somehow strange effect of negative correlation will disappear.

Bests,
Matthias

John Clithero schrieb:
> Hi,
> 
> Looks like my email with the voxel image is waiting for moderator
> approval, so here is my updated response without the picture:
> 
> On Tue, Oct 6, 2009 at 4:39 PM, Yaroslav Halchenko
> <debian at onerussian.com> wrote:
>> On Tue, 06 Oct 2009, John Clithero wrote:
>>> The 'wb_file' is func data with the relevant timepoints... it has had
>>> some preprocessing done in FSL (motion correction, slice timing, brain
>>> extraction). The ROI mask is a whole brain mask. It seems (I hope)
>>> that the NiftiDataset is being put together correctly.
>> wild guess - may be while doing FSL preprocessing you've done some
>> intensity normalization?
> Here (sorry if I didn't list them) are the preprocessing options I used in FSL
> (1) brain extraction
> (2) motion correction
> (3) slice-timing correction
> (4) high-pass filtering
> Added this just now:
> As far as I know, FSL does the following (from website and their
> course lectures):
> "Scale each 4D dataset by a single value to get the overall 4D mean
> (dotted line) to be the same"
> The intensitiy normalization was off during preprocessing (as FSL recommends).
> Is this what you were asking about?
> 
>>
>>> This perfect negative correlation occurs even if I feed in arbitrary
>>> labels to the NiftiDataset, so there must be some sort of error in how
>>> I'm using detrend and/or score?? I am guessing this is my erros since
>>> the raw feature data looks fine to me.
>>> This perfect negative correlation also occurs after just implementing
>>> "zscore" or "detrend", although obviously the values are different.
>> so, they aren't present in just loaded dataset but if you do zscore or
>> detrend -- they come?
> 
> If I break data in half (A trials vs B trials, or random) before using
> zscore or detrend, I generally see strong positive correlation (some
> voxels are just more active than others across the experiment...this
> makes sense).  But, once I use zscore or detrend....then goes to
> perfect negative correlation (when looking at meanA vs meanB).
> Each voxel after zscore, as it was said early, will have a mean of
> zero across all trials...this is true for all the voxels.
> 
>> could you just plot 1 voxel (which later carries perfect correlation)
>> before/after detrend?
>>
> This is waiting for moderator approval, I guess.
> Thanks for the help.
> 
> Cheers,
> John
> 
> 
>> --
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>>
>>
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