[FieldTrip] ft_freqstatistics to find significantly different time samples

[Thomas Lusignan]

Dear List,

I am Thomas, a Master's student in Paul Cisek's lab in Montréal. I work on
monkey local field potentials and spike data recorded during a
decision-making task. This means as of now I have only one subject, but
hundreds of sessions.

I figured out how to plot time-frequency representations of each condition
(i.e. trial types). However, I would like to statistically compare
conditions together to see if each time-frequency point is significantly
different between conditions. I've already used ft_preprocessing and
ft_freqanalysis on the data to get power spectra (with cfg.keeptrials =
'yes').

*I'm looking to use ft_freqstatistics to do the following:*

*1-* For one channel, process each time-point at every frequency of
interest, taking the power at that moment for each trial (e.g.
data.powspctrm(1:53, 1,1,1), then data.powspctrm(1:53, 1,1,2), etc.).

*2-* Use permutations to perform a t-test to see if the average of the
points in one condition is different from the average of the second
condition.

*3-* Repeat for each time-point and frequency, correcting for multiple
comparisons.

*4.* Plot the resulting heatmap and mask.


So, dear List,* here are my questions:*

*A: *Is what I want to do possible with ft_freqstatistics, or some other
function in FieldTrip?
*B: *If it is, what am I doing wrong? I'm guessing it has to do with the
design matrix, but I don't know how to fix it.
*C: *If it works, can I compare distributions of different sizes (i.e.: a
condition with 53 trials vs a condition with 85 trials)? I *could* randomly
select only a certain number of trials so that sample sizes are equal, but
would rather not.



*Following is how the input data is structured, the cfg and the result of
the call.*

My *data *looks like this, for each condition :
data:
        label: {1×15 cell}            % 15 channels with data
       dimord: 'rpt_chan_freq_time'
         freq: [1×57 double]          % from 4 to 60 Hz
         time: [1×1000 double]        % one second of data
    powspctrm: [53×15×57×1000 double] % 53 trials. Not constant between
conditions.
          cfg: [1×1 struct]

The *cfg *I use for my call to ft_freqstatistics is the following:

             channel: 'Ch:1 PMd idES:377'
             latency: 'all'
           frequency: 'all'
         avgoverchan: 'no'
         avgovertime: 'no'
         avgoverfreq: 'no'
          resampling: 'permutation'
           parameter: 'powspctrm'
              design: [2×106 double]
              method: 'montecarlo'
    numrandomization: 1000
           statistic: 'depsamplesT'
               alpha: 0.025
                ivar: 1
            correctm: 'bonferroni'

The *design *looks like this, where the first row represents condition type
and the second, trials:

     1   1   1   ...    1     1     1     2     2     2   ...    2     2
  2
     1   2   3   ...   51    52    53     1     2     3   ...   51    52
 53

I'm pretty sure this is not the right design for my needs, as permutations
only switch between "corresponding" trials (e.g. index 1 with 54, 2 with 55
and so on).


When I call ft_freqstatistics, the result looks like this:

the call to "ft_selectdata" took 0 seconds
using "ft_statistics_montecarlo" for the statistical testing
using "ft_statfun_depsamplesT" for the single-sample statistics
constructing randomized design
total number of measurements     = 106
total number of variables        = 2
number of independent variables  = 1
number of unit variables         = 1
number of within-cell variables  = 0
number of control variables      = 0
using a permutation resampling approach
repeated measurement in variable 2 over 53 levels
number of repeated measurements in each level is 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2
computing statistic
estimated time per randomization is 0.00 seconds
computing statistic 1000 from 1000

performing Bonferroni correction for multiple comparisons
the returned probabilities are uncorrected, the thresholded mask is
corrected
the call to "ft_freqstatistics" took 4 seconds

stat =

  struct with fields:

       prob: [1×57×0 double]
    cirange: [1×57×0 double]
       mask: [1×57×0 logical]
       stat: [1×57×0 double]
        ref: [1×57×0 double]
     dimord: 'chan_freq_time'
       freq: [1×57 double]
      label: {'Ch:1 PMd idES:377'}
       time: [1×0 double]
        cfg: [1×1 struct]

Since stat is basically an structure of empty matrices, I figure
something's wrong.




Thank you all very much,

Regards,

Thomas Lusignan



P.S.: My neurophysiological data is stored in a custom SQL database, which
means that I have no .plx files to import (and therefore no headers, which
makes using FieldTrip a little less straightforward). My data structures
therefore come from a function I built which gets the data and organizes it
correctly (or so I think, but I might be wrong).
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