[FieldTrip] regressconfound and frequency domain

Fri Feb 21 09:23:49 CET 2014

Dear Raghavan, To compute a t-descriptive on subject level freq data, you'll need to use ft_freqstatistics. Have a look here for instance: http://fieldtrip.fcdonders.nl/tutorial/cluster_permutation_freq At the subject level, you do not need (non-parametric) cluster permutation testing (Maris & Oostenveld), as you're taking each subject's t-descriptives to the group level. At the group level, you can then test the hypothesis that there's a difference between tasks/conditions (H1) vs. no difference (H0). In order to do so, you'll need to create a dummy variable at the group level, that has the same number of 'subjects', but with zeros in all fields (in your case this will be a .stat field). At the group level, you thus call ft_freqstatistics again. This approach has the advantage that you're more sensitive (as compared to taking each subject's mean to the group level) to effects that are small but consistent over trials in each subject. Arjen ----- Oorspronkelijk bericht -----
> Van: "Raghavan Gopalakrishnan" <gopalar.ccf at gmail.com>
> Aan: fieldtrip at science.ru.nl
> Verzonden: Donderdag 20 februari 2014 23:33:18
> Onderwerp: Re: [FieldTrip] regressconfound and frequency domain
> Thanks Arjen,
> Should I use ft_freqdescriptives to compute t descriptives for
> individual subjects, and then take that to group level instead of
> mean? If not, what are the other alternatives?
> Thanks,
> Raghavan
> Hi Raghavan, ft_regressconfound run on timelock data seems to return
> output with avg field. However, ft_regressconfound run on frequency
> data, does not return average. I see the avg field being removed. Is
> there a reason? >> Not intentionally, but not an issue either. You
> could still use ft_freqdescriptives to compute the average for you,
> but see my comment below. Question - Since ft_regressconfound outputs
> power spectrum of individual trials - 4D matrix (instead of average),
> can I simply re-average the power spectrum over trials to see the
> average power for that subject. Also, I need to run grand average
> (over subjects) before running statistics. I hope these steps does not
> distort the data. Please advise. >> Remember that the mean over trials
> is not affected by your clean-up of trial-by-trial variance due to
> head movement. Taking each subject's mean (unaffected) to the group
> level is an approach that will not benefit from your clean-up. In
> order to benefit from reduced trial-by-trial variance, you'll need a
> measure that depends on it, e.g. t-descriptive, neural
> activity-behavior correlation (for taking to the group level). Hope
> this helps, Arjen ----- Oorspronkelijk bericht -----
> > Van: "Raghavan Gopalakrishnan" < gopalar.ccf at gmail.com > > Aan:
> > fieldtrip at science.ru.nl > Verzonden: Donderdag 20 februari 2014
> > 22:12:28 > Onderwerp: Re: [FieldTrip] regressconfound and frequency
> > domain > Arjen, > Thanks, I reduced down the time resolution so
> > computation can go > faster. Now, m y matrix looks like this >
> > hpicomptimefreq = > label: {204x1 cell} > dimord:
> > 'rpt_chan_freq_time' > freq: [1x56 double] > time: [1x375 double] >
> > powspctrm: [4-D double] > cumtapcnt: [59x56 double] > cfg: [1x1
> > struct] > trialinfo: [59x1 double] > beta: [4-D double] >
> > ft_regressconfound run on timelock data seems to return output with
> > > avg field. However, ft_regressconfound run on frequency data, does
> > not > return average. I see the avg field being removed. Is there a
> > reason? > Question - Since ft_regressconfound outputs power spectrum
> > of > individual trials - 4D matrix (instead of average), can I
> > simply > re-average the power spectrum over trials to see the
> > average power for > that subject. Also, I need to run grand average
> > (over subjects) before > running statistics. I hope these steps does
> > not distort the data. > Please advise. > Thanks, > Raghavan > Date:
> > Wed, 19 Feb 2014 22:58:38 +0100 (CET) > From: "Stolk, A. (Arjen)" <
> > a.stolk at fcdonders.ru.nl > > To: FieldTrip discussion list <
> > fieldtrip at science.ru.nl > > Subject: Re: [FieldTrip]
> > regressconfound and frequency domain > Message-ID: > <
> > 2108167665.5423215.1392847118322.JavaMail.root at
> > sculptor.zimbra.ru.nl > > > Content-Type: text/plain;
> > charset="utf-8" > Dear Raghavan, Good to hear it's working out for
> > you. A short answer > would be 'no'. Reducing the size of your data
> > matrix is likely going > to speed up computations. Your time
> > resolution seems pretty high (1500 > frequency estimations per
> > single trial); do you need that many? Yours, > Arjen -----
> > Oorspronkelijk bericht ----- > > Van: "Raghavan Gopalakrishnan" <
> > gopalar.ccf at gmail.com > > > Aan: fieldtrip at science.ru.nl > >
> > Verzonden: Woensdag 19 februari 2014 22:01:00 > > Onderwerp:
> > [FieldTrip] regressconfound and frequency domain > > Arjen, > >
> > Thanks for answering all my previous questions. I was successfully >
> > > able to incorporate head movements to my erf data. As I understand
> > I > > have to do this separately for the time frequency data after
> > keeping > > individual trials. I am interested in both beta and
> > gamma bands > > [15:1:70]. my time frequency looks like this using
> > wavelets, > > timefreq = > > label: {204x1 cell} > > dimord:
> > 'rpt_chan_freq_time' > > freq: [1x56 double] > > time: [1x1500
> > double] > > powspctrm: [4-D double] > > cumtapcnt: [55x56 double] >
> > > grad: [1x1 struct] > > elec: [1x1 struct] > > cfg: [1x1 struct] >
> > > trialinfo: [55x1 double] > > After regressconfound > >
> > hpicomptimefreq = > > label: {204x1 cell} > > dimord:
> > 'rpt_chan_freq_time' > > freq: [1x56 double] > > time: [1x1500
> > double] > > powspctrm: [4-D double] > > cumtapcnt: [55x56 double] >
> > > cfg: [1x1 struct] > > trialinfo: [55x1 double] > > beta: [4-D
> > double] > > Regressconfound took about 1 hr and 30 mins, since its a
> > huge matrix > > [55x204x56x1500]. I have 25 such blocks of data for
> > 20 subjects. It > > will take an enoumous amount of time to process
> > the data through > > regressconfound. Is there a workaround to make
> > the processing faster > > or am I missing something. Any help would
> > be of great help. > > Thanks, > > Raghavan
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-- Donders Institute for Brain, Cognition and Behaviour Centre for Cognitive Neuroimaging Radboud University Nijmegen Email: a.stolk at donders.ru.nl Phone: +31(0)243 68294 Web: www.arjenstolk.nl 
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