[FieldTrip] regressconfound and frequency domain

Fri Feb 21 16:14:04 CET 2014

I don't *think* I'm asking about how to do second-order analysis, unless I
have missed something important. I did read the link/FAQ when I was working
on this last week, and that seems different than the question I am asking.

What you described in your reply to me just now, calling
ft_timelockstatistics at the subject level to calculate descriptives, still
effectively reduces each subject to one "datapoint" -- the difference
between conditions of the selected t-descriptive for that subject. I
believe I understand how to do that, and how to then do nonparametric
shuffle testing at the between-subjects level for the signfiicance of that
within-subjects descriptive/difference.

In your earlier reply to Raghavan, it sounded like you were suggesting
pooling the trials themselves between subjects (that is, all subjects'
trials go into one big "bucket"), with the trial-to-trial variance having
been removed via ft_regressconfound, *then* calling the ft_statistics
functions on this single large "bucket" of trials. Have I misunderstood
your suggestion?

Alik

Alik Widge
alik.widge at gmail.com
(206) 866-5435

On Fri, Feb 21, 2014 at 9:53 AM, Stolk, A. (Arjen)
<a.stolk at fcdonders.ru.nl>wrote:

> Dear Alik,
>
> If I am correct, you're asking how to create subject-level t-descriptives,
> mentioned in the previous post? A quick answer would be to perform an
> indepedent t-test ( cfg.statistic = indepsamplesT; ) using
> ft_timelockstatistics (although you're not really 'testing' here, but only
> interested in that t-descriptive), in which trials are the unit of
> observations ( cfg.design = [ones(1, ntrls_conditionA) 2*ones(1,
> ntrls_conditionB)]; ). Note that since an indepedent t-test tests for
> differences of the means of two conditions, it won't fail on unequal number
> of observations across the conditions (unlike a dependent/paired t-test).
>
> Hope this gets you kickstarted for the moment. I found a previous
> mail-conversation that goes more into detail, providing more overview of
> the steps involved:
> http://mailman.science.ru.nl/pipermail/fieldtrip/2011-November/004539.html
> More useful documentation:
>
> http://fieldtrip.fcdonders.nl/faq/what_is_the_idea_behind_statistical_inference_at_the_second-level
>
> Yours,
> Arjen
>
> ------------------------------
>
> *Van: *"Alik Widge" <alik.widge at gmail.com>
> *Aan: *"FieldTrip discussion list" <fieldtrip at science.ru.nl>
> *Verzonden: *Vrijdag 21 februari 2014 11:38:10
>
> *Onderwerp: *Re: [FieldTrip] regressconfound and frequency domain
>
> Arjen, what you just described is more or less what I struggled to do last
> week and ultimately gave up as I was unable to figure out how to get FT to
> do it despite much meditation over tutorials and source files. Can you
> elaborate a bit more on what you are saying below -- not the
> ft_regressconfound bit, but the bit about how to get
> ft_statistics_montecarlo and its wrappers to do a trials-level analysis and
> permutation at the whole-group level? Especially, what does one put in
> cfg.design and how does one call the function? Everything I could find in
> the tutorials described the case of doing means at the subject level and
> then permutation of means at the group level, which as you point out is
> underpowered for subtle effects.
>
> My particular situation was timelock-analyzed trials (with
> keeptrials='yes'), but I  could not find a way to set up cfg.design that
> did not throw error messages. The thing that really seemed to bother it was
> that there were different numbers of trials in the 2-3 conditions of
> interest, since some had to be removed for excessive artifact.
>
> Thanks for any help,
> Alik
>
> Alik Widge
> alik.widge at gmail.com
> (206) 866-5435
>
>
>
> On Fri, Feb 21, 2014 at 3:23 AM, Stolk, A. (Arjen) <
> a.stolk at fcdonders.ru.nl> wrote:
>
>> 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
>>
>>
>> ------------------------------
>>
>> *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 <http://mailman.science.ru.nl/mailman/listinfo/fieldtrip>>
>> *>* Aan: fieldtrip at science.ru.nl <http://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
>> *>* 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 <http://mailman.science.ru.nl/mailman/listinfo/fieldtrip> >
>> *>* To: FieldTrip discussion list < fieldtrip at science.ru.nl <http://mailman.science.ru.nl/mailman/listinfo/fieldtrip> >
>> *>* Subject: Re: [FieldTrip] regressconfound and frequency domain
>> *>* Message-ID:
>> *>* < 2108167665.5423215.1392847118322.JavaMail.root at sculptor.zimbra.ru.nl <http://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
>> *>* >
>> *>* 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 <http://mailman.science.ru.nl/mailman/listinfo/fieldtrip> >
>> *>* > Aan: fieldtrip at science.ru.nl <http://mailman.science.ru.nl/mailman/listinfo/fieldtrip>
>> *>* > 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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>
>
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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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