[FieldTrip] One-sample monte-carlo permutation statistics in Fieldtrip?

[Lars Costers]

Hi, 

Thanks for your suggestion Julian but I’m afraid that your approach is not applicable for my data/hypotheses...

I’m wondering whether I could generate zero data and use that in Fieldtrip's dependent t-test (so cfg.statistics = ‘depsamplesT’) together with my real data in my permutations. I think this will (in theory) be the same as doing a one-sample t-test and flipping the sign of subjects because the real data ("condition 1") and zero data ("condition 2") will be randomly shuffled within subjects. So for some subjects 'data - 0’ (equal to multiplying by 1) will be tested and for other subjects it will be ‘0 - data’ ( equal to multiplying by -1)). 

In Nichols & Holmes (2001, https://www.fil.ion.ucl.ac.uk/spm/doc/papers/NicholsHolmes.pdf <https://www.fil.ion.ucl.ac.uk/spm/doc/papers/NicholsHolmes.pdf>) they state the following for testing H0 = distribution centred to zero: 
We consider subject labels of ‘+1’ and ‘-1’ indicating an unflipped or flipped sign of the data. Under the null hypothesis, we have data symmetric about zero, and hence for a particular subject the sign of the observed data can be flipped without altering its distribution. With exchangeable subjects, we can flip the signs of any or all subjects’ data and the joint distribution of all of the data will remain unchanged. 

Therefore, I think this can be done, but I'm not 100% sure.. 

Does anybody have advice on this matter? 

Best,
Lars

> On 2 May 2019, at 12:31, Julian Keil <julian.keil at gmail.com> wrote:
> 
> Hi Lars,
> 
> we recently faced a similar problem (see here: https://www.nature.com/articles/s41598-019-42380-x). Our solution was to create „dummy“ data based on the actual data and use these in the comparison.
> In short, this relates to Eric Maris’ statement about the null hypothesis: In our case, the H0 was „the regression weights are independent of the stimulus category“, so we test our empirically found regression weights against random regression weights. Please note that we don’t have a formal proof that his is a valid approach.
> 
> Unfortunately, I don’t think this is directly applicable to your situation, as it sounds like you only have one category, but maybe there is a way for you to come up with dummy data? 
> Again, please note that there might be problems with our approach we didn’t consider.
> 
> Best,
> 
> Julian
> 
> 
>> Am 02.05.2019 um 12:05 schrieb Lars Costers <larscosters at gmail.com>:
>> 
>> Hi all,
>> 
>> I was wondering whether there are any one-sample permutation statistics to test for a difference from zero implemented in Fieldtrip, ideally to work with monte-carlo permutations? Didn’t find any standard of 'statfun’ options in the documentation.
>> 
>> My goal is to do maxstat correction on ERF MEG data over voxel space.
>> cfg.latency = [-0.2 0.8];
>> cfg.parameter = 'avg';
>> cfg.method = 'montecarlo';
>> cfg.numrandomization = 2000;
>> cfg.correctm = 'max'; % MaxStat correction
>> cfg.design    = ones(1,nsub);
>> cfg.ivar = 1;  % independent variable
>> cfg.statistic = ???;
>> [stat] = ft_timelockstatistics (cfg, timelock{:,1});
>> 
>>  I read the discussions about one-sample cluster-based permutation tests (e.g. https://mailman.science.ru.nl/pipermail/fieldtrip/2018-August/012314.html). However, for me it's not possible to permute the baseline and post stimulus condition because I have expectation reactions in the baseline. 
>> Anybody could suggest me how I could validly test whether my ERF is signficantly different from zero at every time-frame and electrode?  
>> 
>> Thanks, 
>> 
>> Lars
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>> https://doi.org/10.1371/journal.pcbi.1002202
> 
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