[FieldTrip] Connectivity analysis after applying Welch's method

[Dragos Stanciu]

Dear FieldTrippers,

I'm Dragos Stanciu and I'm working on my MSc in Neuroinformatics
dissertation at the University of Edinburgh. My project involves analysis
of resting-state functional connectivity using graph theory in Alzheimer's
disease based on MEG data.

Each of my subjects has a number of 10s epochs (trials) associated with
him/her. I was able to compute the coherence and weighted phase lag index
measures (with *ft_freqanalysis *and *ft_connectivityanalysis) *by treating
my 10s epochs as trials, but now I would like to reduce the amount of noise
in the estimation of the frequency spectrum by employing Welch's method.

For this, I split each 10s epoch in 2s segments (minitrials) with 50%
overlap:

>   [sep_epoch_data] = ft_redefinetrial(cfg_cut, single_epoch_data)*. *


I then apply *ft_preprocessing *on the minitrials:

>  [processed_single_epoch] = ft_preprocessing(cfg, sep_epoch_data);


I then do frequency analysis on the preprocessed segmented data:

> [single_epoch_freq] = ft_freqanalysis(cfg_freq, processed_single_epoch);

where

> display(cfg_freq)
>         method: 'mtmfft'
>          taper: 'hanning'
>         foilim: [0.5000 4]
>         output: 'powandcsd'
>        channel: {148x1 cell}    % 148 channels labelled from A1 to A148
>      keeptrial: 'no'     % don't keep the minitrials, as we want to
> average them
>     keeptapers: 'no'

Please note that I average the minitrials (*keeptrial = 'no'*) as I want to
get an average of the frequencies.

The resulting *single_epoch_freq* structure looks like:

> display(single_epoch_freq)
> label: {148x1 cell}
>        dimord: 'chan_freq'
>          freq: [0.5001 1.0002 1.5004 2.0005 2.5006 3.0007 3.5008 4.0009]
>     powspctrm: [148x8 double]
>      labelcmb: {10878x2 cell}     % channel combinations (148*147/2)
>     crsspctrm: [10878x8 double]
>           cfg: [1x1 struct]


The last step is to append the averaged frequency structures of each 10s
epoch together and perform connectivity analysis on the main 10s epochs. I
do the concatenation like so:
freq_avgs_powspctrm = [freq_avgs_powspctrm;
permute(single_epoch_freq.powspctrm, [3,1,2])];

freq_avgs_crsspctrm = [freq_avgs_crsspctrm;
permute(single_epoch_freq.crsspctrm, [3,1,2])];

The idea behind *permute(..., [3, 1, 2])* is that I want the first
dimension to represent trials, the second dimension channel combinations
and the third dimension frequencies, as this is needed for the input of
*ft_connectivity_wpli *(Repetitions x Channelcombination (x Frequency)).

I then call stat_conn = ft_connectivityanalysis(cfg_conn, freq_avgs);
where:

> display(cfg_conn)
>      method: 'wpli_debiased'
>     channel: {148x1 cell}

and

> display(freq_avgs)
>      powspctrm: [4x148x8 double]      % as I have 4 ten second epochs
>     crsspctrm: [4x10878x8 double]     % as I have 4 ten second epochs
>         label: {148x1 cell}
>        dimord: 'chan_freq'
>          freq: [0.5001 1.0002 1.5004 2.0005 2.5006 3.0007 3.5008 4.0009]
>      labelcmb: {10878x2 cell}
>           cfg: [1x1 struct]


The error that I get when running *ft_connectivityanalysis* is:

> Error using cat
> CAT arguments dimensions are not consistent.
> Error in ft_checkdata>fixcsd (line 1170)
>     data.crsspctrm = cat(catdim, data.powspctrm, data.crsspctrm);


When debugging, *catdim* is equal to 1. The error occurs because the 2nd
dimension of data.powspctrm and data.crsspctrm are not equal (former is
148, latter is 10878). Do you have any suggestions on getting around this
problem? Should I construct *freq_avgs *(data input to
ft_connectivityanalysis) differently? I'm also open to different approaches
to working out Welch's method in FieldTrip.

Please download this archive that contains my test script and 4 example 10s
epochs of a subject:
https://www.dropbox.com/s/js7pztai02f5p27/Welch_fieldtrip.zip The code
should make things clearer (or the opposite).

Observations: I thought about using *ft_freqanalysis_mtmwelch*, but
apparently it's deprecated.

Thank you all in advance for your feedback.

Kind regards,
Dragos Stanciu
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