[FieldTrip] ft_regressconfound

[Stephen Whitmarsh]

Dear Raghavan,

I've followed your conversation with Arjen with great interest. Although I
have worked with the headposition based on the location coils in the CTF
system, I am now working with an Elekta Neuromag Vectorview system. I would
be very interested to hear how you approach this issue using Maxfilter and
cHPI in the Neuromag setup. And not only me - I'm sure the Neuromag /
FieldTrip community would greatly appreciate your experience and approach.
Please let me know if you are willing to share your procedure.

Best wishes,
Stephen



On 10 February 2014 19:13, Stolk, A. (Arjen) <a.stolk at fcdonders.ru.nl>wrote:

> Dear Raghavan,
>
> > I have another question. I understand ft_regressconfound must be the
> > last
> > step. However, can time frequency calculation be done on data after
> > employing regressconfound? Or would you suggest doing trial by trial
> > time
> > frequency calculation, then compensate using regressconfound and then
> > compute timelock? Is there a reason not to do the former way?
>
> I would recommend doing the latter, provided that you meant to say
> 'compute statistics' instead of 'compute timelock'. When ft_regressconfound
> is applied to raw/ERF data, trial-by-trial adjustments are made to the
> data, according to explained variance by head movements (and thus
> differences in distances to the sensors). Subsequently performing time
> frequency analysis on this data may give a distorted view of signal
> frequency powers.
>
> Note that if a source reconstruction analysis is also one of your
> follow-up steps, it is recommended to do this on the 'original' data, and
> then use ft_regressconfound at the source level. See 'Practical issues' on
> this page for the rationale behind this:
>
>
> http://fieldtrip.fcdonders.nl/example/how_to_incorporate_head_movements_in_meg_analysis
>
> In sum, ft_regressconfound is best used as a last step prior
> ft_xxxstatistics.
>
> Yours,
> Arjen
>
>
> ----- Oorspronkelijk bericht -----
> > Van: "Raghavan Gopalakrishnan" <gopalar.ccf at gmail.com>
> > Aan: fieldtrip at science.ru.nl
> > Verzonden: Maandag 10 februari 2014 17:02:34
> > Onderwerp: Re: [FieldTrip] ft_regressconfound
> > Dear Arjen
> > Thanks for your prompt response. I understand your point regarding
> > using
> > ft_regressconfound with all 4 blocks together. I think that makes more
> > sense.
> > I have another question. I understand ft_regressconfound must be the
> > last
> > step. However, can time frequency calculation be done on data after
> > employing regressconfound? Or would you suggest doing trial by trial
> > time
> > frequency calculation, then compensate using regressconfound and then
> > compute timelock? Is there a reason not to do the former way?
> >
> > Thanks,
> > Raghavan
> >
> > Dear Raghavan, It is indeed recommended to use ft_regressconfound as a
> > last
> > step prior to statistical assessment. It will remove trial-by-trial
> > variance
> > in the neural data that can be attributed to trial-by-trial variance
> > in head
> > position. The latter is approximated with regressors containing
> > trial-by-trial information on head positions deviating from the
> > session/experiment mean. Because the head position timeseries is
> > mean-subtracted, the mean neural activity over trials is not affected
> > by
> > ft_regressconfound; only the variance over trials is, which should
> > result in
> > a cleaner representation of the data. In order to estimate the
> > contribution
> > of different head positions to the neural data, ft_regressconfound
> > relies on
> > general linear modeling. Applying ft_regressconfound to the four
> > blocks
> > separately (your option 1) will involve four different model
> > estimations and
> > their associated errors. Because these errors may differ per
> > estimation, the
> > quality of treatment of the neural data may also differ per block.
> > This will
> > not affect the mean neural activity in each block, but it may affect
> > the
> > grand mean over all four blocks as for trials in one block more
> > contribution
> > from head position may be regressed out than for another. Applying
> > ft_regressconfound on the data of the four blocks together (your
> > option 2),
> > will not affect the grand mean over the trials from all four blocks.
> > It will
> > reduce the influence of head movement on trial-by-trial variance in
> > neural
> > activity. This can be for better, or for worse: namely, if there are
> > consistent differences in head positions between two conditions
> > (captured in
> > those four blocks), it may bring the means of neural activity evoked
> > in
> > these two conditions closer to each other, reducing effect sizes. In
> > fact,
> > the employment of ft_regressconfound allows one to make a good case
> > that an
> > observed effect (i.e. differential neural activity between the two
> > conditions) cannot be attributed to differences in head positions when
> > recording those conditions (note that the same analysis could also be
> > performed with eye-movement related activity, or any other measure of
> > a
> > potential confound). Hope this helps, Arjen ----- Oorspronkelijk
> > bericht
> > -----
> > > Van: "Raghavan Gopalakrishnan" <gopalar.ccf at gmail.com>
> > > Aan: fieldtrip at science.ru.nl
> > > Verzonden: Vrijdag 7 februari 2014 17:03:40
> > > Onderwerp: [FieldTrip] ft_regressconfound
> > > Dear all,
> > > I am using regressconfound on Neuromag data. In CTF data, data for
> > > coil1, coil2 and coil3 are generated and then circumcenter function
> > > is
> > > called to compute 3 translational and 3 rotational dof.
> > > Unlike, CTF, Neuromag maxfilter allows to compute CHPI or QUAT
> > > channels that provide quarternion parameters q1 through q6, where
> > > q4,q5 and q6 are translations in x, y and z directions. I am using
> > > these q4,q5 and q6 to directly compute the rotational orientations
> > > (using the last part of the circumcenter.m script).
> > > It is said regressconfound must be used as a last step prior to
> > > stats.
> > > I have 4 blocks of data for each subject. Which option below should
> > > I
> > > follow?
> > > 1. Apply regress confound separately to four blocks? But, then I
> > > have
> > > to average these four blocks once again using ft_timelockanalysis,
> > > then grand average using ft_timelockgrandaverage before computing
> > > stats.
> > > 2. Or should I append the four blocks first, then perform
> > > regressconfound? In this case, I directly go to grandaverage and
> > > stats.
> > > Any suggestion is appreciated.
> > > Thanks,
> > > Raghavan
> >
> > _______________________________________________
> > fieldtrip mailing list
> > fieldtrip at donders.ru.nl
> > http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
> --
> 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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