[FieldTrip] regressconfound and frequency domain
Raghavan Gopalakrishnan
gopalar.ccf at gmail.com
Mon Feb 24 23:01:26 CET 2014
Hi Arjen, Alik
Thanks for stimulating this discussion about second level statistics.
I tried as suggested, but it would be great if one of you can detail the steps involved so I can make sure what am doing is indeed correct.
It would be great if some more detail is provided on the field trip web page about this.
Thanks,
Raghavan
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.
>> Ok. That's indeed the procedure I tried to outline.
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?
>> I might have not been clear there then. I was meant to say that one
could use ft_regressconfound on trials of two (or more) conditions combined
of a single subject, prior to calculating the single subject t-descriptive
(using ft_xxxstatistics). This allows one to compute a 'cleaner' t-score,
or at least provide a good case that head movement did not differentially
influence activity levels in the separate conditions. It was not related to
pooling trials between subjects.
2014-02-21 16:14 GMT+01:00 Alik Widge <alik.widge at gmail.com>:
> 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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