[FieldTrip] Questions on Functional Connectivity results using debiased wPLI
SERGIO OSORIO GALEANO
srosorio at uc.cl
Thu Sep 23 03:08:17 CEST 2021
Dear Jan,
Here's a follow up question on this thread.
I understand now that 'mtmfft' provides an estimate of the spectrum using
the whole trial. I also understand that if I want to estimate the wPLI for
a specific time window within my trial, I should either 1) use a
time-resolved method when calling *ft_freqanalysis *(e.g. wavelet) or 2)
call *ft_selectdata *prior to using 'mtmfft' in ft_freqanalysis.
Because it is less computationally demanding, I tried the second option.
However, I've noticed that *ft_freqanalysis *throws an error if I try to
use *ft_selectdata* with a time window shorter than one second. After
inspecting the output structure of *ft_selectdata**, *it seems as
though the function performs an average on the second dimension for the
first trial only. When I use a latency over a second, this does not happen
and all trials have the same dimensions. My question is: am I doing
anything wrong or have I again made a wrong assumption while implementing
this pipeline? Is there any reason for this strange output? See code
snippet below. Thanks a lot in advance!
cfg = [ ];
data = ft_preprocessing(cfg,rwdata);
% resample data
cfg = [];
cfg.resamplefs = 250;
data = ft_resampledata(cfg, data);
data =
struct with fields:
fsample: 250
trial: {1×117 cell}
time: {1×117 cell}
label: {100×1 cell}
cfg: [1×1 struct]
% select the portion of the data I want to compute the wPLI for
cfg = [ ];
cfg.latency = .15:.25; <<<<<<<<<<<<<< if
I use 0:1 or 1:1.5, *ft_freqanaylisis* runs without any problem, but if I
use any other latency within 1 second it doesn't work.
data = ft_selectdata(cfg,data);
% here's the output structure
data =
struct with fields:
fsample: 250
trial: {1×117 cell}
time: {1×117 cell}
label: {100×1 cell}
cfg: [1×1 struct]
data.trial
ans =
1×117 cell array
Columns 1 through 9
{100×1 double} {100×1251 double} {100×1251 double} % averaged
first trial in the 2nd dimension instead of selecting the time interval of
interest
data.time
ans =
1×117 cell array
Columns 1 through 10
{[0.1480]} {1×1251 double} {1×1251 double}
% Now call the ft_freqanalysis function
cfg = [ ];
cfg.output = 'fourier';
cfg.method = 'mtmfft';
cfg.taper = 'dpss';
cfg.foi = linspace(1,31,60);
cfg.tapsmofrq = 4;
tf_data = ft_freqanalysis(cfg,data);
Error using dpss>parseinputs (line 308)
Time-bandwidth product NW must be a positive number.
Error in dpss (line 62)
[method,k,Ni,traceFlag,N,NW] = parseinputs(N,NW,varargin{:});
Error in ft_specest_mtmfft>double_dpss (line 362)
tap = dpss(double(a), double(b), varargin{:});
Error in ft_specest_mtmfft (line 151)
tap = double_dpss(ndatsample,ndatsample*(tapsmofrq./fsample))';
Error in ft_freqanalysis (line 563)
[spectrum,ntaper,foi] = ft_specest_mtmfft(dat, time, 'taper',
cfg.taper, options{:}, 'feedback', fbopt);
Error in Connectivity_wPLI (line 68)
tf_data = ft_freqanalysis(cfg,data);
All best,
*Sergio. *
El mié, 15 sept 2021 a las 15:09, SERGIO OSORIO GALEANO (<srosorio at uc.cl>)
escribió:
> Yes, you made a mistake in the (assumptions underlying) the
> implementation: the cfg.toi that you supply before ft_freqanalysis is not
> going to have any effect if you use ‘mtmfft’ as a method, since this method
> just provides a single estimate of the spectrum using the whole ’trial’ for
> the estimation.
>
>
> Oh, that makes perfect sense. Cheers Jan! Really appreciate it.
>
> *Sergio.*
>
>
>
> El mié, 15 sept 2021 a las 14:29, Schoffelen, J.M. (Jan Mathijs) via
> fieldtrip (<fieldtrip at science.ru.nl>) escribió:
>
>> Hi Sergio,
>> >
>> > 1) Regardless of whether I use my full time window of interest (0-1.5
>> s) or whether I estimate wPLI for two separate shorter time windows (0-.7 s
>> and .7-1.5 s), the results are always identical to one another. Is this
>> normal behaviour in wPLI analyses or is there something potentially wrong
>> with how I've implemented it?
>>
>> Yes, you made a mistake in the (assumptions underlying) the
>> implementation: the cfg.toi that you supply before ft_freqanalysis is not
>> going to have any effect if you use ‘mtmfft’ as a method, since this method
>> just provides a single estimate of the spectrum using the whole ’trial’ for
>> the estimation.
>>
>> > 2) I notice that wPLI values are way lower for low-frequencies (4-7hz)
>> than for higher-frequencies (8-12hz and 15-30hz). While this could be a
>> real effect associated with my task, the fact that this result is
>> consistent across different conditions makes me wonder whether I've done
>> something wrong in the ft_freqanalysis configuration.
>>
>> I think that this is just a property of the data.
>>
>> Best wishes,
>> Jan-Mathijs
>>
>>
>> >
>> > Thanks a lot in advance for any help anyone can provide!
>> >
>> > -----------
>> >
>> > cfg = [ ];
>> > data = ft_preprocessing(cfg,rwdata);
>> >
>> > %resample data
>> > cfg = [ ];
>> > cfg.resamplefs = 250;
>> > data = ft_resampledata(cfg, data);
>> >
>> > data =
>> > struct with fields:
>> >
>> > fsample: 250
>> > trial: {1×117 cell}
>> > time: {1×117 cell}
>> > label: {100×1 cell}
>> > cfg: [1×1 struct]
>> >
>> > % next, call the ft_freqanalysis function in our source data
>> > cfg = [ ];
>> > cfg.output = 'fourier';
>> > cfg.method = 'mtmfft';
>> > cfg.taper = 'dpss';
>> > cfg.foi = linspace(4,30,30);
>> > cfg.tapsmofrq = 4;
>> > cfg.toi = 0:.02:1.5; % try 0:.02:.7 and .7:.02:1.5
>> > tf_data = ft_freqanalysis(cfg,data);
>> >
>> > tf_data =
>> > struct with fields:
>> >
>> > label: {100×1 cell}
>> > dimord: 'rpttap_chan_freq'
>> > freq: [1×30 double]
>> > fourierspctrm: [4563×100×30 double]
>> > cumsumcnt: [117×1 double]
>> > cumtapcnt: [117×1 double]
>> > cfg: [1×1 struct]
>> >
>> > % estimate wPLI values
>> > cfg = [ ];
>> > cfg.method = 'wpli_debiased';
>> > connect_data = ft_connectivityanalysis(cfg,tf_data);
>> >
>> > connect_data =
>> > struct with fields:
>> >
>> > label: {100×1 cell}
>> > dimord: 'chan_chan_freq'
>> > wpli_debiasedspctrm: [100×100×30 double]
>> > freq: [1×30 double]
>> > cfg: [1×1 struct]
>> >
>> > Best,
>> >
>> > Sergio.
>> >
>> > _______________________________________________
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>> >
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