[FieldTrip] Striations in stats?

[Peter Goodin]

Hi Fieldtrip list (and apologies for two posts in 4 days),

I've run source analysis using DICS and the method outlined on the extended beamformer tutorial (http://www.fieldtriptoolbox.org/tutorial/beamformingextended) and am happy with the outcome, except after running permutation stats there's striations the the output (see attached). Any ideas on where this is coming from and how it could possibly be fixed (if at all) would be really appreciated. Just in case I've attached the code used to run the source analysis and stats. 

Thanks, 

Peter

Source analysis on individuals (using common filter):

%Source analysis of conditions and baselines
cfg=[];
cfg.grad=grad;
cfg.frequency=foi; %foi is taken from a peak at ~18Hz with 9Hz smoothing
cfg.method='dics';
cfg.grid=leadfield;
cfg.grid.filter=common; %Common filter computed from adding 4 conditions together.
cfg.headmodel=hdm;
cfg.dics.lambda=reg; %reg is 0 (due to having ~500 trials per condition). 
cfg.dics.keepfilter='no';
cfg.dics.fixedori='yes';
cfg.dics.realfilter='yes';

source_bsl=ft_sourceanalysis(cfg, bl_freqs);
source_cond=ft_sourceanalysis(cfg, av_freqs);

source_diff=source_cond;
source_diff.avg.pow = (source_cond.avg.pow ./ source_bsl.avg.pow) -1;
        
%Replace subject source grid locations with MNI positions. 
source_diff.pos = template.sourcemodel.pos;
source_diff.dim = template.sourcemodel.dim;
source_cond.pos = template.sourcemodel.pos;
source_cond.dim = template.sourcemodel.dim;
source_bsl.pos = template.sourcemodel.pos;
source_bsl.dim = template.sourcemodel.dim;

cfg.parameter='pow';
cfg.interpmethod='spline';
source_diff=ft_sourceinterpolate(cfg, source_diff, mnimri);
source_cond=ft_sourceinterpolate(cfg, source_cond, mnimri);
source_bsl=ft_sourceinterpolate(cfg, source_bsl, mnimri);



*****Average source (source_diff) calculated using ft_sourcegrandaverage*****


Stats (13 subs, repeated measures t-test):

subj=size(cond,2);
design=zeros(2,2*subj);
for i=1:subj
     design(1,i)=i;
end
for i=1:subj
    design(1,subj+i)=i;
end
design(2,1:subj)=1;
design(2,subj+1:2*subj)=2;

cfg=[];
cfg.parameter='pow';
cfg.method='montecarlo';
cfg.statistic='ft_statfun_depsamplesT';
cfg.correctm='cluster';
cfg.numrandomization=rand; %rand = 2000
cfg.clustertail=0;
cfg.alpha=0.025;

cfg.design=design;
cfg.uvar=1;
cfg.ivar=2;

%stat=ft_sourcestatistics(cfg, cond_ga{:},bsl_ga{:});
stat=ft_sourcestatistics(cfg, cond{:},bsl{:});

Plotting (average source masked by stats <.05):

cfg=[];
cfg.method='slice';
cfg.funparameter='pow';
cfg.anaparameter='anatomy';
cfg.maskparameter='mask';

source_diff.mask=stat.prob <.05;
ft_sourceplot(cfg, source_diff);

_______________________
Dr Peter Goodin, 

Brain and Psychological Sciences Research Centre (BPsyC)
Swinburne University, 
Hawthorn, Vic, 3122
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