[FieldTrip] Problems with permutation based cluster analysis (ft_freqstatistics)
Alea Skwara
acskwara at ucdavis.edu
Fri Oct 20 20:59:49 CEST 2017
Hi J.M.,
Thanks so much for your reply. The tip to not trust the topographic maps
too much helped in my sleuthing. I realized that adjusting the minnbchan
parameter actually *does* result in the topographic changes I thought I
should see if I was understanding the algorithm correctly (and that I just
wasn't seeing these changes in the map that I was using as a quick-check,
but that they were present in the stats themselves).
Problem solved! Thank you!
Alea
On Wed, Oct 18, 2017 at 11:19 PM, Schoffelen, J.M. (Jan Mathijs) <
jan.schoffelen at donders.ru.nl> wrote:
> Hi Alea,
>
> I think that it all looks OK. I guess that the P5-PO1-O1 cluster is
> spatially disjoint from the bulk of the suprathreshold data points. Don’t
> be mislead by the topographical image, where both the contour lines, and
> the 2D placement of the electrodes might reflect a picture that is
> different from what you would expect.
>
> Note, that providing cirange as extra information is not the most
> informative. Better would have been to show posclusterslabelmat. This data
> field provides for each sample, whether and to which ‘cluster’ it belongs.
>
> Best wishes,
> Jan-Mathijs
>
>
> J.M.Schoffelen, MD PhD
> Senior Researcher, VIDI-fellow - PI, language in interaction
> Telephone: +31-24-3614793 <+31%2024%20361%204793>
> Physical location: room 00.028
> Donders Centre for Cognitive Neuroimaging, Nijmegen, The Netherlands
>
>
>
> On 18 Oct 2017, at 23:05, Alea Skwara <acskwara at ucdavis.edu> wrote:
>
> Hi All,
>
> I have been conducting a permutation based cluster analysis using the
> method = 'montecarlo' correctm= 'cluster' options in ft_freqstatistics to
> examine longitudinal changes in spectral power. Everything runs just fine
> and it identifies clusters successfully.
>
> However, when I examine the results of the analysis, some of the stats
> strike me as strange. Electrodes that have the highest F values (I am
> comparing across 3 timepoints) do not test as significant, while electrodes
> with lower F values do. At first I thought this was because I had a single
> "hot" electrode that was not part of a larger cluster, but looking more
> closely at the stats, it appears that this is not the case.
>
> I have tried double-checking my neighbor definition method and electrode
> locations to assure that it is not an issue with neighbors being
> incorrectly registered. I have also tried adjusting the minimum cluster
> size using the minnbchan option to ensure it was not an issue of a cluster
> not reaching the minimum extent. Neither of these options seem to have any
> impact on the result.
>
> At this point I'm at a loss. Is there something I'm misunderstanding about
> the algorithm?
>
> Code below and plot below, channel-by-channel stats attached.
>
> Thanks!
> Alea
>
> One funky thing to note about this code is that I am inputting average
> values into the analysis (averaged across time as participants have
> slightly differing lengths of data, and within bands as I am running this
> analysis on IAF-based bands, thus "7" is a dummy code for "mean beta band
> power", not 7 Hz)
>
>
> %% THREE CONDITIONS
> %Set up the analysis cfg for 3 conditions (pre- to mid- to post-)
> cfg = [];
> cfg.channel = {'all'};
> cfg.latency = 'all';
> cfg.frequency = [7 7]; %frequency band indicator
> cfg.avgoverfreq = 'no'; %working on bands not frequencies, so nothing
> to average
> cfg.method = 'montecarlo';
> cfg.statistic = 'depsamplesFmultivariate'; %three time points
> cfg.correctm = 'cluster';
> cfg.clusteralpha = 0.1; %change to .1 because we're computing over
> averaged bands and time (losing power)
> cfg.clusterstatistic = 'maxsum';
> cfg.minnbchan = 2;
> cfg.tail = 1; %this has to be 1 for depsamplesF, 0 otherwise
> cfg.clustertail = 1; %this has to be 1 for depsamplesF, 0 otherwise
> cfg.alpha = 0.05; %this has to be 0.05 for depsamplesF, 0.025
> otherwise
> cfg.numrandomization = 10000; %to get a min p-value of 0.0001 to account
> for 500 tests and correction by Bonferroni method.
> %specify which electrodes can form clusters
> cfg_neighb.method = 'distance';
> cfg.neighbours = ft_prepare_neighbours(cfg_neighb, r1t1r_struct);
> %cfg.neighbours = cfgManish.neighbours;
> %cfg = getNeighbours(cfg); % using fixed neighbours
> cfg.layout = layout';
>
>
> % Set up the design matrixfor 3 conditions
> subj = 27; %will differ based on condition
>
> design = zeros(2,3*subj);
> for isub = 1:1:subj
> design(1,isub) = isub;
> design(1,subj+isub) = isub;
> design(1,2*subj+isub) = isub;
> end
> design(2,1:subj) = 1;
> design(2,subj+1:2*subj) = 2;
> design(2,2*subj+1:3*subj) = 3;
>
> cfg.design = design;
> cfg.uvar = 1;
> cfg.ivar = 2;
>
>
> % Run the analysis
> [r1r_stat_beta] = ft_freqstatistics(cfg, r1t1r_struct, r1t2r_struct,
> r1t3r_struct);
>
>
> %Plot the results
> cfg1 = [];
> cfg1.alpha = 0.1;
> cfg1.parameter = 'stat';
> %cfg1.zlim = [0 18.1];
> cfg1.layout = layout';
> cfg1.marker = 'o';
> cfg1.subplotsize = [1 1];
> cfg1.saveaspng = 'r1r_betacluster';
>
> ft_clusterplot(cfg1, r1r_stat_beta);
>
>
> ***Note non-significant hot spot over left parietal***
>
>
>
> <r1r_betacluster_fig1.png>
>
>
> Alea C. Skwara
> Graduate Student | Saron Lab
> http://mindbrain.ucdavis.edu/labs/Saron
>
> Center for Mind and Brain | University of California, Davis
> 267 Cousteau Place | Davis CA 95616
> Cell: (828) 273-8595
>
>
>
>
>
>
>
>
> <clusterstats_beta.xlsx>_______________________________________________
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>
>
>
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--
Alea C. Skwara
Graduate Student | Saron Lab
http://mindbrain.ucdavis.edu/labs/Saron
Center for Mind and Brain | University of California, Davis
267 Cousteau Place | Davis CA 95616
Cell: (828) 273-8595
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