[FieldTrip] ft_sourcestatistics output

[Schoffelen, J.M. (Jan Mathijs)]

Hi Sara,

Thanks for reaching out. I am sorry that you are confused by the textual output of the function.

The statement ‘using connectivity of voxels in 3-D volume’ refers to the fact that you requested a cluster-based heuristic for multiple comparison correcion. Thus, the code needs to define how individual space-time-frequency points are connected, hence the term connectivity in this context.

The statement ‘using … for the single-sample statistics’: the single sample here refers to the fact that the test-statistic-of-interest, here an unpaired T-statistic, is computed for each individual (space-time-frequency) sample of the data.

If you have a good suggestion as to how to amend this text, please feel free to make a Pull Request on github with your suggestions included.

Best wishes,

Jan-Mathijs




On 1 Jun 2020, at 21:04, Hussain, Sara (NIH/NINDS) [F] <sara.hussain at nih.gov<mailto:sara.hussain at nih.gov>> wrote:

Hi FieldTrippers-

I have a somewhat simple question. I am running some statistics on source-localized frequency domain data across two groups of subjects. I would like to use the independent samples t-test statistic for cluster-based permutation testing. However, the output in the command window when I run my code seems to disagree with what I am specifying. I’ve followed the tutorials closely on this to ensure I am doing the analysis appropriately.

I am running the following:

cfg=[];
cfg.parameter='pow';
cfg.method='montecarlo';
cfg.statistic='indepsamplesT';
cfg.correctm='cluster';
cfg.clusteralpha=0.05;
cfg.clusterstatistic='maxsum';
cfg.tail=0;
cfg.clustertail=0;
cfg.alpha=0.025;
cfg.numrandomization=1000;
cfg.design=[repmat(1,1,numel(cond_A)) repmat(2,1,numel(cond_B))]; %1=cond A, 2=cond B
stat=ft_sourcestatistics(cfg,cond_A{:},cond_B{:});

The command window prints the following. Bolded are the items that seem misleading. I have not specified any connectivity analysis. Also, the bit talking about single-sample statistics leads me to believe that this is running a single sample t-test where a set of observations is being contrasted against some expected mean value:

using "ft_statistics_montecarlo" for the statistical testing
using connectivity of voxels in 3-D volume
using "ft_statfun_indepsamplesT" for the single-sample statistics
constructing randomized design
total number of measurements     = 33
total number of variables        = 1
number of independent variables  = 1
number of unit variables         = 0
number of within-cell variables  = 0
number of control variables      = 0
using a permutation resampling approach
computing a parametric threshold for clustering

Any tips anyone has would be very much appreciated!

Sara
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