[FieldTrip] 3f0c
Dr. Stephan Frisch
stephan.frisch at uni-ulm.de
Wed Mar 29 10:02:31 CEST 2023
Guten Tag,
Hi,
Ich bin vom 23.03.2023 bis 02.04.2023 nicht im Büro und kann die Emails nur sehr unregelmäßig lesen und beantworten. Die Emails werden auch nicht weitergeleitet.
I am out of the office from 03/23/2023 to 04/022023 and can only read and answer emails very sporadically. The emails are also not being forwarded.
Viele Grüße / Best regards
Stephan Frisch
Am 28.03.2023 um 16:49 schrieb Dr. Stephan Frisch via fieldtrip <fieldtrip at science.ru.nl>:
> Guten Tag,
> Hi,
>
> Ich bin vom 23.03.2023 bis 02.04.2023 nicht im Büro und kann die Emails nur sehr unregelmäßig lesen und beantworten. Die Emails werden auch nicht weitergeleitet.
>
> I am out of the office from 03/23/2023 to 04/022023 and can only read and answer emails very sporadically. The emails are also not being forwarded.
>
> Viele Grüße / Best regards
> Stephan Frisch
>
>
> Am 28.03.2023 um 15:56 schrieb Maris, E.G.G. (Eric) via fieldtrip <fieldtrip at science.ru.nl>:
>
> Hi Philip,
>
> Here is some advice:
>
> 1. If you have a well-founded expectation for the direction of the effect (A>B, I>II, AI-BI>AII-BII) perform a one-tailed test. There is no sense in going back to a two-tailed test.
> 2. Your clusteralpha=0.01 is more stringent than what most users would choose. I always go for 0.05 and never touch it (not playing around to find a value that gives me a “significant” result). (These high thresholds 0.01 and smaller come from Gaussian Random Field theory, and are not required for permutation inference.)
> 3. Did you analyze your data at the single participant level (using trials as your replications/degrees-of-freedom)? To be hopeful for a significant result over participants, you need at least a few participants that show the effect at the single participant level and all over approximately the same area/frequency band/time interval. In addition, the other participants may not contradict this pattern by showing an opposite effect over the same area/frequency band/time interval.
>
> Good luck,
> Eric Maris
>
>
>
> Van: philip Joadavi <p.joadavi at gmail.com>
> Datum: dinsdag 28 maart 2023 om 14:43
> Aan: "Maris, E.G.G. (Eric)" <e.maris at donders.ru.nl>, FieldTrip discussion list <fieldtrip at science.ru.nl>
> Onderwerp: Fwd: 2x2 ANOVA at source level using cluster based permutation
>
>
> Dear Dr.Maris/ Schoffelen /Oostenveld
>
> I posted a question on the mailing list about 2x2 ANOVA at the source level (please see below my email).
> It's been 2 weeks and I have not found if there is anything wrong with my code. I would really really appreciate it if you could have a look at my code and let me know if I have done all the steps correctly.
>
> Thanks a lot,
> Philip
>
>
>
> ---------- Forwarded message ---------
> From: philip Joadavi <p.joadavi at gmail.com>
> Date: Wed, Mar 15, 2023 at 5:14 PM
> Subject: 2x2 ANOVA at source level using cluster based permutation
> To: FieldTrip discussion list <fieldtrip at science.ru.nl>
>
> Dear all,
>
> I would like to run ANOVA to test the main and interaction effects on my beamforming results. (I used DISC beamformer)
> I've already looked at the FAQ on the FieldTrip website and also the questions in the mailing list.
>
> To check if I have done the steps correctly, I have also done the 2x2 ANOVA using SPM and I got significant results, but without FWE/FDR correction for multiple comparisons at the voxel level.
>
> When I apply the steps described in the FieldTrip and also the mailing list (for cluster-based permutation) I could not get any significant clusters. This is surprising for me because I get always nicer results from permutation tests than I do not get from SPM.
>
> I think I'm doing something wrong and I would appreciate any help.
>
> So, as far as I understood, I can not use F statistics for 2x2 ANOVA, and therefore I used dependent sample T statistics for within-subject design as explained by Eric in the mailing list.
>
> here is my code :
> So, I have 2 conditions A and B, each with 2 levels (I & II).
> For the averages, I used ft_ grandaverage for 2 conditions/levels, and I got a matrix of pow = [number of subjects x number of voxels] = [12x 411477]
>
> Here is the code:
>
> my data is arranged such that the columns are the conditions (A/B) and the rows are the levels (I/II).
>
> % Main effect of levels (I/II)
> I = ft_sourcegrandaverage([],GA{1,1:2});
> II = ft_sourcegrandaverage([],GA{2,1:2});
>
> % Main effect of conditions (A/B)
> A = ft_sourcegrandaverage([],GA{1:2,1});
> B = ft_sourcegrandaverage([],GA{1:2,2});
>
> % Interaction effect
> cfg = [];
> cfg.operation = 'subtract';
> cfg.parameter = 'pow';
>
> INA = ft_math(cfg, GA{1,1}, GA{2,1}); % I - II (A)
> INB = ft_math(cfg, GA{1,2}, GA{2,2}); % I - II (B)
>
>
> %% prepare the hypothesis testing
> cfg = [];
> cfg.dim = GA{1}.dim;
> cfg.method = 'montecarlo';
> cfg.correctm = 'cluster';
> cfg.spmversion = 'spm12';
> cfg.parameter = 'pow';
> cfg.numrandomization = 'all';
> cfg.correcttail = 'alpha';
>
> cfg.statistic = 'depsamplesT';
> cfg.alpha = 0.05/2; % two tails
> cfg.clusteralpha = 0.01;
> cfg.tail = 0; % I also tried 1 for one tail
> cfg.clustertail = cfg.tail;
> cfg.uvar = 1; % subject
> cfg.ivar = 2; % conditions
> cfg.design = [repmat(1:numel(subjects),1,2); repelem([1 2],numel(subjects))];
>
>
> stat{1} = ft_sourcestatistics(cfg,I,II); % main effect (I/II)
> stat{2} = ft_sourcestatistics(cfg,A,B); % main effect (A/B)
> stat{3} = ft_sourcestatistics(cfg,INA,INB); % Interaction
>
> Side note: I also tried the one-tail option, but the results were not significant eighter.
>
>
>
> I would appreciate any help!
> Best,
> Philip
>
>
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