[FieldTrip] Fwd: A question about connectivity values

[masoud]

Thank you for the clarification, that was very helpful
Best regards

On Wed, Jan 4, 2023, 4:11 PM Schoffelen, J.M. (Jan Mathijs) via fieldtrip <
fieldtrip at science.ru.nl> wrote:

> Hi Masoud et al.,
>
> Apologies: I was inaccurate in my previous e-mail in explaining the GC
> coefficient. It’s the *negative* natural logarithm of the residual
> variance ratio, where the model with the larger number of parameters (i.e.
> the one which predicts a signal based on the past of the other signals as
> well) is in the numerator.
>
> BW,
> JM
>
>
> Begin forwarded message:
>
> *From: *Jan Mathijs Schoffelen <janmathijs.schoffelen at donders.ru.nl>
> *Subject: **Re: [FieldTrip] A question about connectivity values*
> *Date: *4 January 2023 at 13:25:18 CET
> *To: *FieldTrip discussion list <fieldtrip at science.ru.nl>
>
> Hi Masoud,
>
> Theoretically, the Granger causality coefficient is a natural logarithm of
> a ratio of residual variances, where this ratio is typically >1, because
> the model implicitly used for the numerator has more parameters, so
> typically a lower residual variance. In other words, GC > 0 but can become
> larger than 1.
>
> If the input data into the algorithm are numerically not well behaved, I
> can imagine that the output GC will be unexpected. In your case, you
> estimate a series of models consisting of 62x62x6 (i.e. >18.000) parameters
> (for each time window). If the number of samples in your data is
> insufficient and/or there’s linear dependence between channels, the
> estimated parameters, and by consequence the spectral quantities, and the
> GC will not make sense.
>
> Best wishes and good luck,
>
> Jan-Mathijs
>
>
>
>
>
>
>
> On 4 Jan 2023, at 09:26, masoud via fieldtrip <fieldtrip at science.ru.nl>
> wrote:
>
> Hello dear fieldtrip community
> I have been using fieldtrip for quite some time and now I am dealing with
> dynamic connectivity, the preprocessing of the data which I am using was
> done in fieldtrip as well as MVAR analysis, this is the code for
> calculating dynamic connectivity:
>    cfg.method = 'biosig';
>    cfg.t_ftimwin = 5;
>    cfg.toi = toi;
>    cfg.order = 6;
>    cfg.output = 'parameters';
>    mvardata = ft_mvaranalysis(cfg, data);
>    cfg = [];
>    cfg.method = 'mvar';
>    cfg.output = 'fourier';
>    freq = ft_freqanalysis(cfg, mvardata);
>    cfg = [];
>    cfg.method = 'granger';
>    stat = ft_connectivityanalysis(cfg, freq);
> so the output of connectivity (stat) is a 4-d matrix(channel, channel,
> frequency, windows) which for my data is (62, 62, 126, 12)
> the values of the channel x channel are mostly negative like -13.2650 ,
> -5.7060 or between -1 and 1.
> as my understanding the output should be between 0 and 1. So where am I
> doing it wrong? And if I change the method from 'granger' to 'pdc' or 'dtf'
> what should be the range of output?
> thank you so much in advance
> best regards
> Masoud
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