[FieldTrip] ft_regressconfound

[Stolk, A. (Arjen)]

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
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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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