[FieldTrip] PAC analysis with ft_crossfrequencyanalysis

Thu Apr 14 19:42:30 CEST 2022

Hi Elisa,

Thanks for the additional information.

In response to your reply:

Last but not least, is there a physiological or computational explanation for the impossibility of computing PAC on different electrodes?

I don’t understand the question underlying this question: given some data, and a function, one could most of the time produce a result. In that sense it is always possible. Whether or not that result is interpretable, that’s a different question, but it probably does not depend on whether it was computed on electrode O1, or on electrode P6

The code of ft_crossfrequencyanalysis states that computation for different channels is not supported:

Lines 85-86:

% prior to 19 Jan 2017 this function had input options cfg.chanlow and cfg.chanhigh,
% but nevertheless did not support between-channel CFC computations

 I was wondering the reason behind this since I have found some toolboxes for PAC that allow computing the MI with a different selection of channels (e.g. occipital chans for phase and frontal chans for amplitude).

OK, I did not understand your question earlier. So what you mean with ‘on different electrodes’ is to use one channel for the low frequency phase, and another for the high frequency envelope. I missed that one. As far as I know there have been some contributions to this function (since 2017) which enable the computation of cross-channel PAC.

Q.2 When computing PAC with Fieldtrip, I get the same value for the entire frequency band of the phase (i.e. same number from 4 to 8 Hz). This is despite the resulting MI matrix being N.channel*N.freqphase*N.freqAmplitude.
Since I would like to explore which theta frequency phase is mostly coupled with the higher frequency power, I would like to know why I get the same values and how to fix this as I could not find any configuration about averaging frequencies (e.g. cfg.averageoverfreq = 'yes'/'no').

This I don’t understand. I cannot comment on this without knowing more about the exact parameters used for the generation of the input data into the ft_crossfrequencyanalysis function.

I asked this question because, despite the output of ft_crossfrequencyanalysis being chanxfreqlowxfreqhigh (180x24x51), the value is the same for all the frequencies of the phase (4-8 Hz). There is no parameter in the function to configure this option and it is indeed limiting any explorative analysis. Is there a way to get a PAC estimate for every frequency of the phase?

For me it just seems to work. That is: I never use this function myself, but I just generated some random data, and got some results with different values for 4/5/6/7/8 Hz.
I cannot comment on this any further, because I don’t know the specifics of your data.

Q3. The last question about the statistics was because, with other PAC toolboxes, a statistic against a surrogate comodulogram with no coupling is computed and a pvalue is generated to assess whether the PAC index is reliable or not.

‘Reliable’ is not a statistical statement. As I said earlier, statistics are done in order to test the data against a certain null hypothesis. In the case of an event-related design (but also in the case of ongoing oscillations where the basic periodicity is not a nice sinusoid), there may be all kinds of interesting across frequency non-linear interaction patterns, so rejection of a null hypothesis of CFC=0 will be trivial. Things might be a bit better if you compute across channel PAC, but as long as you are using sensor level EEG data with a lot of spatial smearing across the scalp, I wouldn’t hold my breath.

It seems that Fieldtrip does not have a function for this purpose. Also, the standard permutation statistics would require changing the structure of the data to match it with the inputs required by spectral or time statistical analysis functions. Thus, I was wondering how to compute proper statistical testing on PAC data with Fieldtrip.

It depends. If your unit-of-observation is a group comparison across two sufficiently well matchted conditions, then a surrogate strategy would not be needed, would it? If you want to compute a null distribution within a subject, trial shuffling is probably an option, but this requires some matlab voodoo on your end.

Best wishes,
Jan-Mathijs

In conclusion, if I understood your response correctly, Fieldtrip's function for computing PAC with Tort's Modulation index is not appropriate for tasks with event-related modulation of oscillatory activity. Thus, PAC analysis should only be run on time windows where no event-related activity takes place or on resting-state data. Is that correct?

Thank you for taking the time to read my questions and responses.

Best wishes and happy Easter,

Elisa

________________________________
From: fieldtrip <fieldtrip-bounces at science.ru.nl<mailto:fieldtrip-bounces at science.ru.nl>> on behalf of Schoffelen, J.M. (Jan Mathijs) via fieldtrip <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Sent: Wednesday, April 13, 2022 07:59
To: FieldTrip discussion list <fieldtrip at science.ru.nl<mailto:fieldtrip at science.ru.nl>>
Cc: Schoffelen, J.M. (Jan Mathijs) <janmathijs.schoffelen at donders.ru.nl<mailto:janmathijs.schoffelen at donders.ru.nl>>
Subject: Re: [FieldTrip] PAC analysis with ft_crossfrequencyanalysis

Hi Elisa,

Your questions  are not decorated with any insights about code/data, so it’s a bit guessing as to how to compile an answer to your questions that might help you further. I’ll try anyhow:

  1.   What is the minimum epoch length required to compute PAC (is 1s enough to compute the phase of 4 Hz)?

The sliding time window (which is not necessarily the same as the epoch length) should be sufficiently long to get a decent estimate of the phase at 4Hz. Thus, a cfg.t_ftimwin for the low frequency oscillation, to be used in ft_freqanalysis to compute the freqlo should be sufficient.

  1.
  2.  Is this computation suitable for tasks with a strong event-related synchronization? I read that ERPs should be subtracted from this analysis to avoid confounding effects but here I have reaching movements.

No, and subtraction of an ERP is not going to be very useful in my opinion. See: https://www.fieldtriptoolbox.org/faq/evoked_vs_induced/<https://urldefense.com/v3/__https://www.fieldtriptoolbox.org/faq/evoked_vs_induced/__;!!HJOPV4FYYWzcc1jazlU!9SocnEHY5D4b6IoSCImxrNII9ePfLxiWSO-Bl4bagz1C_AbEfYdSngiijmxGe6qz686hKu_fPxCTB8yHpEPajhSg_5JG08Xl$>

  1.  When computing PAC with Fieldtrip, I get the same value for the entire frequency band of the phase (i.e. same number from 4 to 8 Hz). This is despite the resulting MI matrix being N.channel*N.freqphase*N.freqAmplitude.
  2.  Since I would like to explore which theta frequency phase is mostly coupled with the higher frequency power, I would like to know why I get the same values and how to fix this as I could not find any configuration about averaging frequencies (e.g. cfg.averageoverfreq = 'yes'/'no').

This I don’t understand. I cannot comment on this without knowing more about the exact parameters used for the generation of the input data into the ft_crossfrequencyanalysis function.

  1.  If I manage to compute a meaningful MI, how do I statistically test whether it is significant?

‘Significant’ is always related to a null hypothesis. What’s your null hypothesis?

  1.  Last but not least, is there a physiological or computational explanation for the impossibility of computing PAC on different electrodes?

I don’t understand the question underlying this question: given some data, and a function, one could most of the time produce a result. In that sense it is always possible. Whether or not that result is interpretable, that’s a different question, but it probably does not depend on whether it was computed on electrode O1, or on electrode P6

Best wishes,
Jan-Mathijs

Thank you very much for taking the time to read my questions. I really hope to get some answers from you.

Best wishes.

Elisa

Elisa Tatti, Ph.D.
Post Doctoral Research Fellow
CUNY, School of Medicine
160 Convent ave
10031
New York City, NY

Tel. +1 3472043952

 pronouns: she/her/hers

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