quiestions about planar transformation

[Mark Drakesmith]

Hi

Thanks very much for your comments. I'll stick to using the axial
gradients for stats for now. I was just concerned about the issue with
dipoles in the same location but different orientations potentially
cancelling each other out when analysising the axial gradients.

  I suppose, if looking at within-subjects trials, if a set of dipoles
are  in the same location but showing large variability in orientation,
they should not be considered anymore significant than dipoles that have
different spatial locations. But between subjects, there is alot of
varaiblity in cortex topology which may lead to functionally-equivilent
dipoles haveing vastly different orientations.

I'm not sure how much this is worth worrying about. Do you ahve any
thoguhts on this?

Thanks again

Mark

> Dear Mark, dear Jan-Mathijs,
>
> I fully agree with JM on not doing statistics (or even averaging) on the planar (absolute) gradient values when working in the time domain. What I resorted to is doing all statistics on fields and presenting that. I addition I supply the planar gradient of the final raw (field) effect underlying the statistics - solely on a desriptive level.
>
> The fundamental underlying problem is that you go from a scalar field and massively paralellel univariate statistics to a vector field and, hence, truly multivariate statistics (e.g. because the local gradient vectors will rotate across the course of an ERP/ERF for example). Coming up with a p-value there sounds difficult to me. I always wondered how people with planar gradiometer devices solve that issue when doing time-domain stuff? Maybe there are solutions out there already?
>
> Michael
>
> -----Ursprüngliche Nachricht-----
> Von: jan-mathijs schoffelen<jan.schoffelen at DONDERS.RU.NL>
> Gesendet: Mar 5, 2010 11:12:03 AM
> An: FIELDTRIP at NIC.SURFNET.NL
> Betreff: Re: [FIELDTRIP] quiestions about planar transformation
>
>
>> Dear Mark,
>>
>> There may be some confusion here, relating to whether we are talking
>> about frequency domain data (=power, always a positive value), or
>> whether we are talking about time domain data (=amplitude, can be
>> positive and negative).
>>
>> Single trial combination of planar gradient transformed axial gradient
>> (or magnetometer) data is not the best thing to do for time domain
>> stuff.
>> Reason: combination takes place by applying Pythagoras' rule to the
>> _dV and _dH pairs, and when the individual _dH and _dV components are
>> noisy, the noise is also squared, added, and squar-rooted, which leads
>> to an 'amplification' of noise. Not good. It works after trial-
>> averaging because you squeeze out the noise first and Pythagoras the
>> reduced-noise signals.
>>
>> For frequency domain data this is not an issue.
>> Reason: combination usually takes place by just adding the _dV and _dH
>> pairs (power is a squared value already). This is just a linear step
>> (just like averaging) and the order of performing the linear steps
>> does not matter for the result. So single trial combination prior to
>> averaging or vice versa should not make a difference here.
>>
>> In general I would advise against trying to do statistics on the
>> combined planar gradient and use it for visualization purposes only.
>> Alternatively, one could come up with a non-parametric statistical
>> test (permutation), in which I could think of a way of extracting a p-
>> value from a single _dH/_dV pair combined. But this would be a
>> different story...
>>
>> Best,
>>
>> Jan-Mathijs
>>
>>
>>
>>> Hi all
>>>
>>> I am currently a little confused as to how to properly use megplanar
>>> and combineplanar for my 4D 148 axial gadiometer data. I would
>>> ideally like to use planar gradients as it will make subsequent ERF
>>> and frequency analysis easier to interpret. My question is when
>>> should I combine the v and h components?
>>>
>>> Looking  at some previous posts it is suggested that you should
>>> combine the gradients on an individual basis, but when I do this i
>>> get an almost flat amplitude across all sensors which doesn't look
>>> right (see attached image, 'bad_planar'). When i use combineplanar
>>> after averaging, the data the data looks ok (good_planar). I get the
>>> same result doing both megplanar and combineplanar on the averaged
>>> data.
>>>
>>> I just want to double check that the 'bad' planar fields i am
>>> getting are correct and not due to a bug in field trip. Intuitively,
>>> I would expect combining the components on the individual level
>>> world be more accurate as it would prevent fields from opposeingly
>>> orientated dipoles cancelling out when averaged.
>>>
>>> If it is the case that you should use combineplanar after trial
>>> averaging, how do you go about combining after statistical tests?
>>> i.e. when you have a p-value or a test statistic for the v and h
>>> components for each sensor, how would you combine these together?
>>>
>>> Any help or advice would be appreciated.
>>>
>>> Many thanks
>>>
>>> Mark
>>>
>> Dr. J.M. (Jan-Mathijs) Schoffelen
>> Donders Institute for Brain, Cognition and Behaviour,
>> Centre for Cognitive Neuroimaging,
>> Radboud University Nijmegen, The Netherlands
>> J.Schoffelen at donders.ru.nl
>> Telephone: 0031-24-3668063
>>
>> ----------------------------------
>> The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/neuroimaging/fieldtrip.
>>
> ----------------------------------
> The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/neuroimaging/fieldtrip.


--

Mark Drakesmith
PhD Student

Neuroscience and Aphasia Research Unit (NARU)
University of Manchester

----------------------------------
The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/neuroimaging/fieldtrip.