[FieldTrip] the right normalsation?

[Tom Marshall]

Hi Jörn

> My favourite is log(Post#1/Post#2) or taking the z-score.

Could you expand on 'taking the z-score'? This could be interpreted in 
different ways, which might control for different sources of variance.

To throw a bit more fuel on this fire... the problem with log ratios - 
ie log(post#1/post#2) - is that they ignore the prestimulus period 
altogether. That always seemed a bit funny to me. Presumably the brain 
response to your event of interest is somehow dependent on the brain 
state at the time the event happens ;)

Of course one can independently test the hypothesis that the 
prestimulus periods differ, but how to be confident that they do not?

On Thursday, July 04, 2013 10:39:00 AM, "Jörn M. Horschig" wrote:
> Dear Ye,
>
> normalizing has different purposes. On the one hand, as Arjen pointed
> out, it is necessary to normalize in source space to get rid of the
> depth bias (well, alternatively you could normalize the leadfields).
> On the other hand, it makes averaging over subjects reasonable -
> otherwise differences in e.g. scalp conducitivity (for EEG) or
> headshape and size (for MEG) might lead to biasing an average in
> favour of some subjects. It is just the same reason as using a
> baseline when analysing ERPs/ERFs.
> Furthermore, there is no correct way of normalizing. As I said, you
> could also normalize the leadfield rather than normalizing by
> conditions. I think the most important thing to remember is that
> a) you need some contrast or
> b) you need to normalize the leadfield
>
> For a) it would be sufficient to use Post#1/Post#2, but I would rather
> not contrast two conditions normalized by two baselines. This gets
> really hard in interpretation (e.g. is the observed effect caused by a
> difference in baseline or a different in stimulus processing?). My
> favourite is log(Post#1/Post#2) or taking the z-score. Taking the
> logarithm has the advantage that extreme values get squashed nearer
> together, thereby reducing the influence of outliers. Z-scoring
> achieves a similar thing by explicitly normalizing by the variance.
> I would suggest for you to create some synthetic signals or values and
> play around with different ways of normalizing to get a feeling for
> what you are doing and what influence this has. I test different cases
> (e.g. 3: difference in prestim but no difference in poststim, no
> difference in prestim but difference in poststim and difference in
> prestim and poststim) and apply different normalizations/contrasts.
>
> Good luck :)
> Best,
> Jörn
>
>
> On 7/3/2013 9:35 PM, Frank Mei wrote:
>> Hello FieldtripList,
>>
>> I am trying to differentiate brain areas responsible for two
>> different conditions using the method show in the
>> tutorial(http://fieldtrip.fcdonders.nl/tutorial/beamformer), and so
>> far I have tried to subtract condition# 1 minus the #2, and divided
>> by the average of the baseline period (pre-stimuli presentation),
>> i.e., (Post#1-Post#2)./(Average of Pre#1 and Pre#2). I think this
>> division is for normalisation purposes. Is this the right
>> normalsation? What normalization do you suggest to use? Is it
>> necessary to normalise?
>>
>> thanks
>> Ye
>>
>>
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>
>
> --
> Jörn M. Horschig
> PhD Student
> Donders Institute for Brain, Cognition and Behaviour
> Centre for Cognitive Neuroimaging
> Radboud University Nijmegen
> Neuronal Oscillations Group
> FieldTrip Development Team
>
> P.O. Box 9101
> NL-6500 HB Nijmegen
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>
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>
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--
Tom Marshall, MSc.
Neuronal Oscillations Group, Donders Centre for Cognitive Neuroimaging
tel: +31(0)243668487
email: t.marshall at fcdonders.ru.nl
postal: PO Box 9101, 6500HB, Nijmegen, The Netherlands
visiting: Kapittelweg 29, 6525EN, Nijmegen, The Netherlands