[FieldTrip] Connectivity comparison across conditions

[Azeez Adebimpe]

See my opinion within the line

On Mon, Oct 5, 2015 at 12:17 PM, Jose Rebola <jrebola at gmail.com> wrote:

> Hi!
>
>
>
> I am trying to assess if there is a difference in connectivity across two
> conditions in my data. Condition 1 has 240 trials and condition 2 has 252
> trials.
>
> I have chosen to use the *phase-locking-value* as a measure of
> connectivity.
>
> *My first question is:*
>
>
>
> Which formula should I use to evaluate the difference between conditions?
>
> Would   plv1- plv2 be appropriate, or do I have to do some kind of
> transformation or normalization? If I choose to use other connectivity
> measures, will it be much different?
>
>  I have seen for example in the paper of Jan-Mathijs Schoffelen in 2011
> that he uses the formula in the following image for the assessment of
> differences between the coherence values x1 and x2.
>
> I should note that I would like to evaluate differences at the
> intra-subject level.
>
>
> [image: Inline image 1]
>
In simpler form, the  PLV is implemented in fieldtrip based on this
paper  Lachaux
1999 <http://www.ma.utexas.edu/users/davis/reu/ch3/cwt/lachaux.pdf>.  your
formular may be right but seem more complex to me. On comparison of two
conditions, you dont need to subtract, you can do statistics in fieldtrip
or look for the file  name *statcond*  in fieldtrip  or eeglab, very easy
to understand and follow.

>
>
> *My second question is:*
>
>
>
> In order to do the non-parametric testing, my null hypothesis is that
> there are no differences between plv1 and plv2 ( I guess). So I can
> randomize trial labels, evaluate plv1 and plv2 again, do this 1000 times
> and count the number of times that this difference is bigger than my
> original difference, right?
>

I didnt get your questions here clearly. Meanwhile, randomization  is
random sequence of random permutation i.e resampling of original data in
1000 times (in this case -plv).

>
>
> *My third and fourth questions concern clustering:*
>
> In order to do clustering, I should first establish a threshold value for
> the metric under evaluation. This may be easy to set if I was using
> t-values, but if I am evaluating differences in means, what should be an
> appropriate value to use?
>
>
>
> Regarding spatial clustering, I now have two levels of “neighbour”
> electrodes, right? At the seed level, and at the destination level. How can
> these be clustered? I mean, if I consider for example the electrode-pairing
> T7-P3, both T9-P3 and T7-P5 will be neighbours…
>
>
>
Ofcourse, you need threshold to threshold the graph, otherwise you can
computed weighted clustering  coefficient. Thresholding depend on you and
your data, there are many ways to threshold. You can threshold within
certain range or based on distribution of your data. It is better to read
more literature on this aspect.

> *Lastly,*
>
> Are these issues already implemented in Fieldtrip or do I have to build my
> own MATLAB code for the randomization, thresholding and clustering?
>
>
>
Everything implemented  in fieldtrip and sometime you need your own
initiative. the best things you can do now is  to go through tutorials in
fieldtrip first.

> Thank you so much,
>
>
>
>  José Rebola
>
> I hope it helps.

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