Freqstatistics Yields Zero Significant Clusters?

Charles Cook charles.cook at ULETH.CA
Thu Jun 4 18:04:38 CEST 2009


I've been having trouble still trying to perform cluster-based permutation
tests with Fieldtrip. What I'm attempting to do is compare male and female
participant's time-frequency data (between group or independent samples)
generated from BESA on a spatial memory task. We've been trying to
increasing the alpha levels to determine if we have any significant
clusters, and even moving it up to 0.9 still does not provide any
significance.  

Any suggestions would be much appreciated.

Cheers,

Charles
-------------------------------- 
 
% this is the list of BESA datafiles in the Female Location condition
filename_femloc = {
.
.
};
for i=1:11
  femloc{i} = besa2fieldtrip(filename_femloc{i});
end
 
% this is the list of BESA datafiles in the Male Location condition
filename_maleloc = {
.
. 
};
for i=11
maleloc{i} = besa2fieldtrip(filename_maleloc{i});
end
%}
 
% collect all single subject data in a convenient cell-array
for i=1:11
  femloc{i} = besa2fieldtrip(filename_femloc{i});
  maleloc{i} = besa2fieldtrip(filename_maleloc{i});
end
 

%Reading in the electrode locations for the Std.81 montage
elec = read_fcdc_elec('EGI-BESA_Standard_81.sfp');
 
% recompute the average, except do _not_ average but keepindividual
% this collects all identical time/frequency/channel samples over all
subjects into a single data structure
cfg = [];
cfg.keepindividual = 'yes';
maleloc_all = freqgrandaverage(cfg, maleloc{:});
femloc_all = freqgrandaverage(cfg, femloc{:});
 
 
% perform the statistical test using randomization and a clustering approach
% using the NEW freqstatistics function
cfg = [];
cfg.elec             = elec;
cfg.neighbourdist    = 4; 
cfg.statistic        = 'indepsamplesT';
cfg.minnbchan        = 0;
cfg.clusteralpha     = 0.05;
cfg.clustertail      = 0;
crg.makeclusters     = 'yes';
cfg.numrandomization = 100;
cfg.latency          = 'all';
cfg.frequency        = 'all';
cfg.avgovertime      = 'no';
cfg.avgoverfreq      = 'no';
cfg.avgoverchan      = 'no';
cfg.correctm         = 'bonferoni';
cfg.method           = 'montecarlo';
cfg.design           = [1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11  %
subject number is 1-11 males and 1-11 females
                        1 1 1 1 1 1 1 1 1 1  1  2 2 2 2 2 2 2 2 2 2  2]; %
condition number with 1 being males and 2 being females
                          
cfg.uvar = 1;                                   % "subject" is unit of
observation
cfg.ivar = 2;                                   % "condition" is the
independent variable
[stat] = freqstatistics(cfg, maleloc_all, femloc_all);
 
cfg = [];
[freq_maleloc] = freqdescriptives(cfg, maleloc_all);
[freq_femloc]  = freqdescriptives(cfg, femloc_all);
 
cfg = [];
cfg.zlim = [-6 6];
cfg.alpha = 0.025;
clusterplot(cfg, stat);

--------------------------------
Reading time-frequency representation using BESA toolbox
reading power on 81 channels
.
.
not computing grand average, but keeping individual power for 11 subjects
not computing grand average, but keeping individual power for 11 subjects
selected 81 channels
selected 31 time bins
selected 79 frequency bins
Warning: PACK can only be used from the MATLAB command line.
> In fieldtrip\private\prepare_timefreq_data at 310
  In fieldtrip\private\statistics_wrapper at 206
  In freqstatistics at 132
  In CMCWM2_std81 at 194
using "statistics_montecarlo" for the statistical testing
using "statfun_indepsamplesT" for the single-sample statistics
constructing randomized design
total number of measurements     = 22
total number of variables        = 2
number of independent variables  = 1
number of unit variables         = 1
number of within-cell variables = 0
number of control variables      = 0
using a permutation resampling approach
repeated measurement in variable 1 over 11 levels
number of repeated measurements in each level is 2 2 2 2 2 2 2 2 2 2 2 
computing statistic
estimated time per randomization is 1 seconds
computing statistic 1 from 100
.
.
computing statistic 100 from 100
performing Bonferoni correction for multiple comparisons
the returned probabilities are uncorrected, the thresholded mask is corrected
the input is freq data with 81 channels, 79 frequencybins and 31 timebins

computing the leave-one-out averages [---|                                 ]
computing the leave-one-out averages [-------/                             ]
computing the leave-one-out averages [-----------                          ]
computing the leave-one-out averages [-------------\                       ]
computing the leave-one-out averages [----------------|                    ]
computing the leave-one-out averages [--------------------/                ]
computing the leave-one-out averages [------------------------             ]
computing the leave-one-out averages [--------------------------\          ]
computing the leave-one-out averages [-----------------------------|       ]
computing the leave-one-out averages [---------------------------------/   ]
computing the leave-one-out averages [-------------------------------------]
the input is freq data with 81 channels, 79 frequencybins and 31 timebins

computing the leave-one-out averages [---|                                 ]
computing the leave-one-out averages [-------/                             ]
computing the leave-one-out averages [-----------                          ]
computing the leave-one-out averages [-------------\                       ]
computing the leave-one-out averages [----------------|                    ]
computing the leave-one-out averages [--------------------/                ]
computing the leave-one-out averages [------------------------             ]
computing the leave-one-out averages [--------------------------\          ]
computing the leave-one-out averages [-----------------------------|       ]
computing the leave-one-out averages [---------------------------------/   ]
computing the leave-one-out averages [-------------------------------------]
no significant clusters in data; nothing to plot
>> 

----------------------------------
The aim of this list is to facilitate the discussion between users of the FieldTrip  toolbox, to share experiences and to discuss new ideas for MEG and EEG analysis. See also http://listserv.surfnet.nl/archives/fieldtrip.html and http://www.ru.nl/neuroimaging/fieldtrip.



More information about the fieldtrip mailing list