[FieldTrip] cluster-based permutation test in the time dimension

Thu Dec 2 11:53:43 CET 2021

Hi Songhee,

I am not sure I understand the problem you describe, but I can see two issues:

1.	If you average vertices and create virtual channels (i.e. the average time series within a ROI), you cannot look for clusters at vertex level anymore. You can, however, use the COG of the ROIs to create an “elec” structure with channel positions and treat your data as it would be in sensor space with your ROIs as the “sensors”. I am developing the Automatic Analysis (aa) <https://github.com/automaticanalysis/automaticanalysis>  pipeline, which has all of these steps implemented. For code snippets, see aamod_meeg_preparesourcemodel.m <https://github.com/tiborauer/automaticanalysis/blob/master/aa_modules/aamod_meeg_preparesourcemodel.m>  (lines 112-149) and aamod_meeg_sourceatlasing <https://github.com/tiborauer/automaticanalysis/blob/master/aa_modules/aamod_meeg_sourceatlasing.m> .
2.	Neighbourhood in time is implicit, as Jan-Mathijs describes. However, you can filter your clusters after the stats and select only those with a temporal extent >= N, if that is what you ask for.

Kind regards,

Tibor 

Auer, Tibor M.D. Ph.D.

Research Fellow

School of Psychology, Faculty of Health and Medical Sciences

University of Surrey, Guildford GU2 7XH

 <mailto:T.Auer at surrey.ac.uk> T.Auer at surrey.ac.uk

 <https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Ftwitter.com%2FTiborAuer&data=02%7C01%7Ct.auer%40surrey.ac.uk%7Cdb32da458c424eedef2908d7d4bd1421%7C6b902693107440aa9e21d89446a2ebb5%7C0%7C0%7C637211780868086968&sdata=zrT5%2FnGGsar14C3WartuU99tzsfLu30Peh9fuaqrAUg%3D&reserved=0> @TiborAuer

From: fieldtrip <fieldtrip-bounces at science.ru.nl> On Behalf Of Songhee Kim via fieldtrip
Sent: Saturday, November 27, 2021 12:37 AM
To: FieldTrip discussion list <fieldtrip at science.ru.nl>
Cc: Songhee Kim <songhee.kim at nyu.edu>
Subject: Re: [FieldTrip] cluster-based permutation test in the time dimension

Thanks Jan-Mathijs. I have the following questions: 

1. So minnbchan is only for the data in the sensor space, correct? 

2. If so, is the 'cluster' method available at all for data in the source space as a means of multiple comparisons correction? 

Basically I was wondering if I can run a cluster-based permutation t-test on the source-level data. For example, I might want to average brain activities across all the vertices within an ROI, get a timeseries, and then do a permutation t-test on it such that a cluster is formed if a t-statistic is above a certain threshold at least for, say, 5 contiguous time samples. (This is a temporal clustering test, and one might want to do a spatio-temporal clustering test where a cluster is formed if an above-threshold statistic is found for N adjacent time samples and M adjacent vertices, etc). 

I hope I explained my question more clearly. Thanks! 

Best,

Songhee

On Wed, Nov 24, 2021 at 7:16 AM Schoffelen, J.M. (Jan Mathijs) via fieldtrip <fieldtrip at science.ru.nl <mailto:fieldtrip at science.ru.nl> > wrote:

Hi Songhee, 

I don’t know whether I fully understand your question, but I think that such option does not exist. The option minnbchan only exists (and is functional) for channel level data, in order to define the minimum number of suprathreshold neighbouring channels. Across the time and frequency etc. dimension the spatial neighbourhood is implicit in the consecutive time/freq points, so each sample by definition only has 2 neighbours (one on either side).

Note that minnbchan will not work for ft_sourcestatistics, as far as I know.

Best wishes,

Jan-Mathijs

On 11 Nov 2021, at 01:17, Songhee Kim via fieldtrip <fieldtrip at science.ru.nl <mailto:fieldtrip at science.ru.nl> > wrote:

Hi folks,

I wonder if a cluster-based permutation test with respect to the temporal dimension is implemented in Fieldtrip. I can see that there is an option to set a minimal number of channels to form a cluster (i.e., minnbchan) when running ft_sourcestatistics. Is there a similar option for the temporal dimension? Any help would be appreciated! 

Best,

Songhee  

-- 

Songhee Kim, PhD

Postdoctoral Fellow

Department of Neurology

Medical College of Wisconsin

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-- 

Songhee Kim, PhD

Postdoctoral Fellow

Department of Neurology

Medical College of Wisconsin

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