[FieldTrip] cluster analysis on time courses from virtual channels

[Loes Koelewijn]

Hi Jan-Mathijs,

Many thanks - I wasn't aware of there being an F test version and this has
solved the problem now. Strangely, I had tried running the same dependent
samples T test with only 2 conditions just to see if that would work, and
for some reason, it wouldn't (I had adjusted the design matrix of course). I
have now gotten it all to work though, both F test with several conditions
and the T test with only 2, so I must have overlooked something previously.

Many thanks for your help!

Loes

On Wed, Mar 30, 2011 at 6:15 PM, jan-mathijs schoffelen <
jan.schoffelen at donders.ru.nl> wrote:

> Hi Loes,
>
> The statistical test you are trying to do, is a dependent samples T test.
> This can only be applied if you have a repeated measures design with 2
> conditions. You have 6 conditions, and this causes the error I presume.
> I guess what you need is probably a depsamplesF or so.
>
> Best wishes,
>
> Jan-Mathijs
>
> On Mar 30, 2011, at 8:21 AM, Loes Koelewijn wrote:
>
> Hi all,
>
> I have a question about group analysis statistics of my MEG study. I've
> made virtual sensors from group average images of an independent localiser
> run, created at several time windows, based on the group inversion. I then
> extracted dipole waveforms for all other conditions (6), for each subject
> (10), based on those virtual sensors (the study has a repeated measures
> design). I would now like to analyse group significance of any differences
> between these 6 time courses, over all time samples, corrected for multiple
> comparisons. I have tried to do this using FieldTrip's cluster based
> permutation test (Monte Carlo), independently for each virtual channel, by
> setting the neighbouring channels to 0. However, I get an error about the
> design matrix when running ft_timelockstatistics.
>
> Matlab's output is the following:
>
> selected 1 channels
> selected 251 time bins
> selected 1 frequency bins
> Using the gradiometer configuration from the dataset.
> there are on average 0.0 neighbours per channel
> using "statistics_montecarlo" for the statistical testing
> using "statfun_depsamplesT" for the single-sample statistics
> constructing randomized design
> total number of measurements     = 60
> 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 2 over 6 levels
> number of repeated measurements in each level is 10 10 10 10 10 10
> computing a parmetric threshold for clustering
> Error using ==> statfun_depsamplesT at 78
> Invalid specification of the design array.
> ??? Error using ==> statistics_montecarlo at 217
> could not determine the parametric critical value for clustering
>
> Error in ==> statistics_wrapper at 285
>     [stat, cfg] = statmethod(cfg, dat, cfg.design, 'issource',issource);
>
> Error in ==> ft_timelockstatistics at 117
> [stat, cfg] = statistics_wrapper(cfg, varargin{:});
>
> Could anybody help me in the direction of what is going wrong here? I
> wondered earlier if my data structures were incorrect, but these were
> created with ft_timelockgrandaverage, keeping the individual data. This is
> what I ran:
>
> [stat]=ft_timelockstatistics(cfg,cond1,cond2,cond3,cond4,cond5,cond6);
>
> cfg =
>
>              channel: 'Occipital_Inf_RR_X'
>              latency: [-0.2000 0.8000]
>               method: 'montecarlo'
>            statistic: 'depsamplesT'
>             correctm: 'cluster'
>         clusteralpha: 0.0500
>     clusterstatistic: 'maxsum'
>                 tail: 0
>          clustertail: 0
>                alpha: 0.0250
>     numrandomization: 500
>               design: [2x60 double]
>                 uvar: 2
>                 ivar: 1
>
> cfg.design is 2*60, with the first row (ivar) [1:10,1:10,1:10 etc], second
> row (uvar) [ones(1,10),ones(1,10)*2, etc].
> I initially had ivar and uvar reversed, but I got the same error.
>
> And this is the format of each data structure:
>
>          label: {'Occipital_Inf_RR_X'}
>        fsample: 250
>            avg: [1x826 double]
>            var: [1x826 double]
>           time: [1x826 double]
>     individual: [10x1x826 double]
>         dimord: 'subj_chan_time'
>            cfg: [1x1 struct]
>           grad: [1x1 struct]
>
> Apologies for the massive email, but I was hoping this is enough info for
> someone to have more of a clue than I do?
>
> Kind regards,
>
> Loes
>
> PS Do let me know if you think this approach is wrong in the first place.
> I was trying to avoid pre-setting time windows for image-based statistics,
> as we do not really have strong a priori expectations for times.
>
> --
> Loes Koelewijn
> PhD Candidate
> Macquarie Centre for Cognitive Science (MACCS)
> Macquarie University
> Sydney NSW 2109
> Australia
>
> Ph:   +61 2 9850 4135
> Fax: +61 2 9850 6059
> email: loes.koelewijn at mq.edu.au
>  _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>
>
> Dr. J.M. (Jan-Mathijs) Schoffelen
> Donders Institute for Brain, Cognition and Behaviour,
> Centre for Cognitive Neuroimaging,
> Radboud University Nijmegen, The Netherlands
> J.Schoffelen at donders.ru.nl
> Telephone: 0031-24-3614793
>
>
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>



-- 
Loes Koelewijn
PhD Candidate
Macquarie Centre for Cognitive Science (MACCS)
Macquarie University
Sydney NSW 2109
Australia

Ph:   +61 2 9850 4135
Fax: +61 2 9850 6059
email: loes.koelewijn at mq.edu.au
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