[FieldTrip] Effect of cfg.minnbchan
Marlies Vissers
marliesvissers at gmail.com
Tue Aug 4 16:38:57 CEST 2015
Dear Jörn,
Thanks for your reply and suggestion! My neighbourhood-definitions look
fine according to ft_neighbourplot.
However, I am not sure whether the connectivity matrix in my output
(stat.cfg.connectivity) is correct. For example, I inspected which channels
are defined as neighbours of C5 in the connectivity matrix. Here, I assumed
the channel order in this matrix resembles the channel order defined by the
function ‘ft_read_sens’, as stored in the field elec.label.
Using this particular organization of electrodes, the pattern of spatially
neighbouring channels matches the strange spatial distribution of one of my
resulting clusters.
E.g., C5 seems to neighbour FC4 and Cz (among others). This pattern matches
my strange result, where a cluster consisting of FC4, Cz, and C5, survived
cluster correction with minnbchan set to 2.
I am currently trying to establish whether my cfg.connectivity matrix is
indeed the problem, and where this problem arises. One thing I suspect
might be the cause is that the call to ‘channelconnectivity’ by
‘ft_statistics_montecarlo’ to construct the spatial neighbour structure,
goes wrong. When I try to apply ‘channelconnectivity’ on my cfg structure
directly (but not when running ft_freqstatistics), I get the following
error *‘Undefined function 'channelconnectivity' for input arguments of
type 'struct'’*.
If channelconnectivity was not properly run, it could be that the electrode
ordering as defined by ft_read_sens is not adjusted to the order used by
cfg.neighbours before construction of the connectivity matrix. However,
when I just run ft_freqstatistics, I do not get an error that suggests any
problems with ‘channelconnectivity’, so I am not entirely sure whether this
error also occurs when running the analysis as a whole.
Could you (or anyone else) give me any advice on how to make sure that the
definition of neighbours in the connectivity matrix is constructed using
the properly reordered vector with channel indices (the variable sel2 in
channelconnectivity if I’m correct)?
Thanks in advance!
Best,
Marlies
On 3 August 2015 at 09:37, Jörn M. Horschig <jorn at artinis.com> wrote:
> 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
>
> Artinis Medical Systems <http://www.artinis.com/> | +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 | 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.powspctrm,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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