[FieldTrip] Effect of cfg.minnbchan

[Jörn M. Horschig]

Dear Marlies,

 

if I am not mistaken, the minnbchan option checks indeed if every channels
in a  cluster has at least the specified amount of channels in it and
removes it otherwise. The code is documented as follows:

    % For every (time,frequency)-element, it is calculated how many
significant

    % neighbours .this channel has If a significant channel has less than
minnbchan

    % significant neighbours, then this channel is removed from onoff.

 

If something went wrong is hard to tell – it also depends on how the
neighbor structure looks like. If you do not have C3 or C1, then C5 can
indeed be defined as a neighbor of Cz, and if you do not have FC2, then Cz
could be called a neighbor of FC4. You can check how neighbors are defined
by using ft_neighbourplot. That’s the first check I’d do.

 

Best,

Jörn

 

 

--

 

Jörn M. Horschig, PhD, Software Engineer

 <http://www.artinis.com/> Artinis Medical Systems  |  +31 481 350 980 

 

From: fieldtrip-bounces at science.ru.nl
[mailto:fieldtrip-bounces at science.ru.nl] On Behalf Of Marlies Vissers
Sent: Friday, July 31, 2015 3:27 PM
To: fieldtrip, donders <fieldtrip at donders.ru.nl>
Subject: [FieldTrip] Effect of cfg.minnbchan

 

Dear Fieldtrippers. 

 

I have a question about the effect of the tuning parameter cfg.minnbchan
when performing cluster based permutation testing, since the spatial
characteristics of my clusters differ from what I expected to find given the
settings I used.

 

I am running cross-subject correlations (n=31) between a behavioral variable
and a subj*channel*time*frequency matrix with power data (preprocessed using
EEGlab and decomposed outside of Fieldtrip), using ft_freqstatistics with
the function 'ft_statfun_intersubcorr'. Since I'd like to avoid finding
clusters with difficult-to-interpret shapes, I set cfg.minnbchan to 2.  

 

My concern however, is that the resulting clusters do not always exist of
neighbouring electrodes. For example, one of the resulting clusters is
located at channels C5, Cz & FC4 (EEG, 64 electrodes), which are connected
through the other dimensions (time & frequency). In one of Eric Maris'
previous replies to an older post about cfg.minnbchan, I read that the
parameter cfg.minnbchan only concerns spatial (not time/freq) neighbours.
Does this mean that any cluster without spatially neighbouring electrodes
should have been rejected when minnbchan is set to 2?

 

I would be very grateful if someone could let me know whether setting
cfg.minnbchan to 2 can -in principle- still yield clusters with
spatially-non-adjacent electrodes, or whether this result is likely due to
an error in my code. If the latter, I'd very much appreciate advice on
checks I could perform to detect the potential error.

 

The code I used to run this analysis is pasted below.

 

Thanks in advance!

 

Best,

Marlies

 

Marlies Vissers 

PhD student 

 

University of Amsterdam | Department Brain and Cognition | Cognition and
Plasticity Laboratory

Weesperplein 4 | 1018 XA Amsterdam | 020 - 525 67 24 |
<mailto:M.E.Vissers at uva.nl> M.E.Vissers at uva.nl

 

---

 

% Import the electrode labels from an EEGlab set 

elec = ft_read_sens('template_64.set','fileformat','eeglab_set');

 

% Create matrix with TF data

freqData.freq = frex;                                        % Frequenies of
TF matrix

freqData.time = tx(TF_idx(1):TF_idx(2))./1000;  % Time samples in TF matrix

freqData.dimord = 'subj_chan_freq_time';

freqData.label = elec.label;

freqData.powspctrm = tempTF;                       % tempTF is matrix with
subj*channels*freq*time

 

% Create matrix with behavioral data, make sure the sizes match

behav.freq = frex; 

behav.time =  tx(TF_idx(1):TF_idx(2))./1000; 

behav.dimord = 'subj_chan_freq_time';              % Trick FT: there is just
behavioral data in this matrix

behav.label = elec.label;

behav.powspctrm = repmat(accEffect,[1
size(freqData.powspctrm,2),size(freqData.powspctrm,3),size(freqData.powspctr
m,4)]); % accEffect is 1*subj array

 

% Set configuration for perm test

% Create cfg and insert parameters

cfg = [];

cfg.latency                = [0 1.250];

cfg.frequency            = [2 30];

cfg.channel               ='all';

cfg.method               = 'montecarlo';

cfg.statistic              = 'ft_statfun_intersubcorr';

cfg.type                   = ft_getopt(cfg, 'type', 'Spearman');

cfg.correctm            = 'cluster';

cfg.clusteralpha       = 0.05; 

cfg.clusterstatistic    = 'maxsum';

cfg.minnbchan         = 2;

cfg.tail                    = 0;

cfg.clustertail           = 0;

cfg.alpha                 = 0.025;

cfg.numrandomization = 1000; 

 

% prepare_neighbours determines what sensors may form clusters: load Biosemi
64 channel cap

cfg.template              =  [fieldtripdir thisSlash 'template' thisSlash
'neighbours' thisSlash 'biosemi64_neighb.mat'];

cfg.layout                  =  [fieldtripdir thisSlash 'template' thisSlash
'layout' thisSlash 'biosemi64.lay'];

cfg_neighb.method    =   'template';

cfg.neighbours          =   ft_prepare_neighbours(cfg_neighb,freqData);

 

% Create design mat

subj = 31;

design = zeros(2,2*subj);

for i = 1:subj

  design(2,i) = i;

end

for i = 1:subj

  design(2,subj+i) = i;

end

design(1,1:subj) = 1;

design(1,subj+1:2*subj) = 2;

cfg.design       = design;

cfg.ivar = 1; 

cfg.uvar = 2; 

 

% Run stats

[stat] = ft_freqstatistics(cfg,freqData,behav);

 

 

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