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<div class=Section1>
<p class=MsoPlainText>Dear FT-colleagues,<o:p></o:p></p>
<p class=MsoPlainText><o:p> </o:p></p>
<p class=MsoPlainText><o:p> </o:p></p>
<p class=MsoPlainText><span lang=EN-US>> The fundamental underlying problem
is that you go from a scalar field<o:p></o:p></span></p>
<p class=MsoPlainText>> and massively paralellel univariate statistics to a
vector field and,<o:p></o:p></p>
<p class=MsoPlainText>> hence, truly multivariate statistics (e.g. because
the local gradient<o:p></o:p></p>
<p class=MsoPlainText>> vectors will rotate across the course of an ERP/ERF
for example).<o:p></o:p></p>
<p class=MsoPlainText>> Coming up with a p-value there sounds difficult to
me. I always<o:p></o:p></p>
<p class=MsoPlainText>> wondered how people with planar gradiometer devices
solve that issue<o:p></o:p></p>
<p class=MsoPlainText>> when doing time-domain stuff? Maybe there are
solutions out there<o:p></o:p></p>
<p class=MsoPlainText>> already?<o:p></o:p></p>
<p class=MsoPlainText><o:p> </o:p></p>
<p class=MsoPlainText><span lang=EN-US>Following up on the proposal by
Jan-Mathijs, this is how you can do it:<o:p></o:p></span></p>
<p class=MsoPlainText style='margin-left:36.0pt;text-indent:-18.0pt;mso-list:
l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-family:Symbol'><span
style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Use the following test
statistic at the level of the (_dV,_dH) channel pairs: calculate the difference
between the experimental conditions of the lengths of the trial-averaged vector-valued
(_dV,_dH)-signals (J-M calls this “to Pythagoras”). You do this for all
channels and all time points<o:p></o:p></span></p>
<p class=MsoPlainText style='margin-left:36.0pt;text-indent:-18.0pt;mso-list:
l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-family:Symbol'><span
style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Find the channel- and time-point-specific
permutation distributions of these statistics<o:p></o:p></span></p>
<p class=MsoPlainText style='margin-left:36.0pt;text-indent:-18.0pt;mso-list:
l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-family:Symbol'><span
style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Use these channel- and
time-point-specific permutation distributions to find sensible thresholds for
the statistics<o:p></o:p></span></p>
<p class=MsoPlainText style='margin-left:36.0pt;text-indent:-18.0pt;mso-list:
l0 level1 lfo1'><![if !supportLists]><span lang=EN-US style='font-family:Symbol'><span
style='mso-list:Ignore'>·<span style='font:7.0pt "Times New Roman"'>
</span></span></span><![endif]><span lang=EN-US>Do cluster-based permutation
testing using these thresholded test statistics that are subsequently combined
in a maximum cluster statistic to control the false alarm<o:p></o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US>If you write the statfun for the
channel-specific test statistic, then the rest can be performed using existing
Fieldtrip code.<o:p></o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US>Best,<o:p></o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US>Eric Maris<o:p></o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoNormal><o:p> </o:p></p>
<p class=MsoNormal><span lang=EN-GB style='font-family:"Verdana","sans-serif"'>dr.
Eric Maris<br>
Donders Institute for Brain, Cognition and Behavior<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-GB style='font-family:"Verdana","sans-serif"'>Center
for Cognition and F.C. Donders Center for Cognitive Neuroimaging<o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-GB style='font-family:"Verdana","sans-serif"'>Radboud
University</span><span lang=EN-US style='font-family:"Verdana","sans-serif"'><br>
</span><span lang=EN-GB style='font-family:"Verdana","sans-serif"'>P.O. Box
9104</span><span lang=EN-US style='font-family:"Verdana","sans-serif"'><br>
</span><span lang=EN-GB style='font-family:"Verdana","sans-serif"'>6500 HE
Nijmegen<br>
The Netherlands<br>
T:+31 24 3612651<br>
Mobile: 06 39584581<o:p></o:p></span></p>
<p class=MsoNormal><span lang=DE style='font-family:"Verdana","sans-serif"'>F:+31
24 3616066<br>
E: e.</span><span style='font-family:"Verdana","sans-serif"'><a
href="mailto:e.maris@donders.ru.nl" title="blocked::mailto:maris@nici.kun.nl"><span
lang=DE style='color:blue'>maris@donders.ru.nl</span></a></span><span
lang=EN-US style='font-family:"Verdana","sans-serif"'><o:p></o:p></span></p>
<p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText><span lang=EN-US><o:p> </o:p></span></p>
<p class=MsoPlainText>> <o:p></o:p></p>
<p class=MsoPlainText>> Michael<o:p></o:p></p>
<p class=MsoPlainText>> <o:p></o:p></p>
<p class=MsoPlainText>> -----Ursprüngliche Nachricht-----<o:p></o:p></p>
<p class=MsoPlainText>> Von: jan-mathijs schoffelen
<jan.schoffelen@DONDERS.RU.NL><o:p></o:p></p>
<p class=MsoPlainText>> Gesendet: Mar 5, 2010 11:12:03 AM<o:p></o:p></p>
<p class=MsoPlainText>> An: FIELDTRIP@NIC.SURFNET.NL<o:p></o:p></p>
<p class=MsoPlainText>> Betreff: Re: [FIELDTRIP] quiestions about planar
transformation<o:p></o:p></p>
<p class=MsoPlainText>> <o:p></o:p></p>
<p class=MsoPlainText>> >Dear Mark,<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >There may be some confusion here, relating to
whether we are talking<o:p></o:p></p>
<p class=MsoPlainText>> >about frequency domain data (=power, always a
positive value), or<o:p></o:p></p>
<p class=MsoPlainText>> >whether we are talking about time domain data
(=amplitude, can be<o:p></o:p></p>
<p class=MsoPlainText>> >positive and negative).<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >Single trial combination of planar gradient
transformed axial gradient<o:p></o:p></p>
<p class=MsoPlainText>> >(or magnetometer) data is not the best thing to
do for time domain<o:p></o:p></p>
<p class=MsoPlainText>> >stuff.<o:p></o:p></p>
<p class=MsoPlainText>> >Reason: combination takes place by applying
Pythagoras' rule to the<o:p></o:p></p>
<p class=MsoPlainText>> _dV<o:p></o:p></p>
<p class=MsoPlainText>> >and _dH pairs, and when the individual _dH and
_dV components are<o:p></o:p></p>
<p class=MsoPlainText>> >noisy, the noise is also squared, added, and
squar-rooted, which leads<o:p></o:p></p>
<p class=MsoPlainText>> >to an 'amplification' of noise. Not good. It
works after trial-<o:p></o:p></p>
<p class=MsoPlainText>> >averaging because you squeeze out the noise
first and Pythagoras the<o:p></o:p></p>
<p class=MsoPlainText>> >reduced-noise signals.<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >For frequency domain data this is not an issue.<o:p></o:p></p>
<p class=MsoPlainText>> >Reason: combination usually takes place by just
adding the _dV and _dH<o:p></o:p></p>
<p class=MsoPlainText>> >pairs (power is a squared value already). This
is just a linear step<o:p></o:p></p>
<p class=MsoPlainText>> >(just like averaging) and the order of
performing the linear steps<o:p></o:p></p>
<p class=MsoPlainText>> does<o:p></o:p></p>
<p class=MsoPlainText>> >not matter for the result. So single trial
combination prior to<o:p></o:p></p>
<p class=MsoPlainText>> >averaging or vice versa should not make a
difference here.<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >In general I would advise against trying to do
statistics on the<o:p></o:p></p>
<p class=MsoPlainText>> >combined planar gradient and use it for
visualization purposes only.<o:p></o:p></p>
<p class=MsoPlainText>> >Alternatively, one could come up with a
non-parametric statistical<o:p></o:p></p>
<p class=MsoPlainText>> test<o:p></o:p></p>
<p class=MsoPlainText>> >(permutation), in which I could think of a way
of extracting a p-<o:p></o:p></p>
<p class=MsoPlainText>> value<o:p></o:p></p>
<p class=MsoPlainText>> >from a single _dH/_dV pair combined. But this
would be a different<o:p></o:p></p>
<p class=MsoPlainText>> >story...<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >Best,<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >Jan-Mathijs<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >> Hi all<o:p></o:p></p>
<p class=MsoPlainText>> >><o:p></o:p></p>
<p class=MsoPlainText>> >> I am currently a little confused as to how
to properly use megplanar<o:p></o:p></p>
<p class=MsoPlainText>> >> and combineplanar for my 4D 148 axial
gadiometer data. I would<o:p></o:p></p>
<p class=MsoPlainText>> >> ideally like to use planar gradients as it
will make subsequent ERF<o:p></o:p></p>
<p class=MsoPlainText>> >> and frequency analysis easier to interpret.
My question is when<o:p></o:p></p>
<p class=MsoPlainText>> >> should I combine the v and h components?<o:p></o:p></p>
<p class=MsoPlainText>> >><o:p></o:p></p>
<p class=MsoPlainText>> >> Looking at some previous posts it is
suggested that you should<o:p></o:p></p>
<p class=MsoPlainText>> >> combine the gradients on an individual
basis, but when I do this i<o:p></o:p></p>
<p class=MsoPlainText>> >> get an almost flat amplitude across all
sensors which doesn't look<o:p></o:p></p>
<p class=MsoPlainText>> >> right (see attached image, 'bad_planar'). When
i use combineplanar<o:p></o:p></p>
<p class=MsoPlainText>> >> after averaging, the data the data looks ok
(good_planar). I get the<o:p></o:p></p>
<p class=MsoPlainText>> >> same result doing both megplanar and
combineplanar on the averaged<o:p></o:p></p>
<p class=MsoPlainText>> >> data.<o:p></o:p></p>
<p class=MsoPlainText>> >><o:p></o:p></p>
<p class=MsoPlainText>> >> I just want to double check that the 'bad'
planar fields i am<o:p></o:p></p>
<p class=MsoPlainText>> getting<o:p></o:p></p>
<p class=MsoPlainText>> >> are correct and not due to a bug in field
trip. Intuitively, I would<o:p></o:p></p>
<p class=MsoPlainText>> >> expect combining the components on the
individual level world be<o:p></o:p></p>
<p class=MsoPlainText>> more<o:p></o:p></p>
<p class=MsoPlainText>> >> accurate as it would prevent fields from
opposeingly orientated<o:p></o:p></p>
<p class=MsoPlainText>> >> dipoles cancelling out when averaged.<o:p></o:p></p>
<p class=MsoPlainText>> >><o:p></o:p></p>
<p class=MsoPlainText>> >> If it is the case that you should use
combineplanar after trial<o:p></o:p></p>
<p class=MsoPlainText>> >> averaging, how do you go about combining
after statistical tests?<o:p></o:p></p>
<p class=MsoPlainText>> >> i.e. when you have a p-value or a test
statistic for the v and h<o:p></o:p></p>
<p class=MsoPlainText>> >> components for each sensor, how would you
combine these together?<o:p></o:p></p>
<p class=MsoPlainText>> >><o:p></o:p></p>
<p class=MsoPlainText>> >> Any help or advice would be appreciated.<o:p></o:p></p>
<p class=MsoPlainText>> >><o:p></o:p></p>
<p class=MsoPlainText>> >> Many thanks<o:p></o:p></p>
<p class=MsoPlainText>> >><o:p></o:p></p>
<p class=MsoPlainText>> >> Mark<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >Dr. J.M. (Jan-Mathijs) Schoffelen<o:p></o:p></p>
<p class=MsoPlainText>> >Donders Institute for Brain, Cognition and
Behaviour, Centre for<o:p></o:p></p>
<p class=MsoPlainText>> >Cognitive Neuroimaging, Radboud University
Nijmegen, The Netherlands<o:p></o:p></p>
<p class=MsoPlainText>> >J.Schoffelen@donders.ru.nl<o:p></o:p></p>
<p class=MsoPlainText>> >Telephone: 0031-24-3668063<o:p></o:p></p>
<p class=MsoPlainText>> ><o:p></o:p></p>
<p class=MsoPlainText>> >----------------------------------<o:p></o:p></p>
<p class=MsoPlainText>> >The aim of this list is to facilitate the
discussion between users of<o:p></o:p></p>
<p class=MsoPlainText>> the FieldTrip toolbox, to share experiences and to
discuss new ideas<o:p></o:p></p>
<p class=MsoPlainText>> for MEG and EEG analysis. See also<o:p></o:p></p>
<p class=MsoPlainText>> http://listserv.surfnet.nl/archives/fieldtrip.html
and<o:p></o:p></p>
<p class=MsoPlainText>> http://www.ru.nl/neuroimaging/fieldtrip.<o:p></o:p></p>
<p class=MsoPlainText>> <o:p></o:p></p>
<p class=MsoPlainText>> ----------------------------------<o:p></o:p></p>
<p class=MsoPlainText>> The aim of this list is to facilitate the discussion
between users of<o:p></o:p></p>
<p class=MsoPlainText>> the FieldTrip toolbox, to share experiences and to
discuss new ideas<o:p></o:p></p>
<p class=MsoPlainText>> for MEG and EEG analysis. See also<o:p></o:p></p>
<p class=MsoPlainText>> http://listserv.surfnet.nl/archives/fieldtrip.html
and<o:p></o:p></p>
<p class=MsoPlainText>> http://www.ru.nl/neuroimaging/fieldtrip.<o:p></o:p></p>
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<p>----------------------------------</p>
<p>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.</p>
<p> http://listserv.surfnet.nl/archives/fieldtrip.html</p>
<p> http://www.ru.nl/fcdonders/fieldtrip/</p>