[FieldTrip] Questions relating to MEG nonparametric testing and uneven trial numbers

[Eric Maris]

Dear May,





2 subject groups: Controls (C) and Patients (P).
For a particular experimental condition (A), on average, subjects within 
each group have the following number of trials:
condA_Ntrials_C  ~= 100
condA_Ntrials_P  ~=  80

I plan to perform a 2-step statistics-test to test for differences between C 
and P groups on a particular experimental condition.
Using a 1st level within subject t-test (or active-vs-baseline test) on 
baseline vs active period for each group, deriving subject-specific t-stats 
which are to be subsequently used in a 2nd level test between subject group 
contrast, using nonparametric test.

Question 1) Does it matter if the average trials per subject in each group 
is different? Should I try to equalize the trial numbers by e.g. random 
removal of some trials before computing any statistics? Is this advisable?



Performing a second level t-test (in your case, between different groups of 
subjects)  on first level t-tests is very unusual. In fact, this would imply 
that you second level t-test is about an hypothesis that pertains to first 
level t-tests. Instead, one always performs a second level t-test on 
first-level averages (typically baseline-normalized to deal with individual 
differences in distance to the helmet or baseline power). Differences in 
number of trials per conditions is never a problem as long as your 
first-level measure (the baseline-normalized mean) is unbiased.


Question 2) Minimum trials required for source-level significance?
The supplementary info. accompanying the 2007 paper illustrated some minimum 
trial numbers required for obtaining a significant effect at the 
sensor-level analysis, given some threshold (e.g. cluster>250 sensor-time 
pairs).
Has anyone systematically shown what is the minimum number of trials 
required to obtain significant clusters/fdr stats at the source-level 
analysis?
If not, is there a sensible way to find out? (presumably involving some form 
of bootstrapping and simulation?)



There is no sensible way to find out this minimum number of trials, because 
the true effect size is unknown.


This relates to another issue I have with regards to 'within-subjects' 
comparison for 2 experimental conditions A and B where there are average 
trial number differences both within and between subject groups:
condA_Ntrials_C  ~= 100         condB_Ntrials_C  ~= 60   (min. =35)
condA_Ntrials_P  ~=  80          condB_Ntrials_P  ~=  50  (min. =35)

A similar concern of uneven trials arises if say I wish to perform a within 
subjects comparison between the experimental conditions.



I think I’ve dealt with this question.


In general, Q: Would the intended 2-step statistical test (as described 
above) be appropriate, OR would it best to control for 'equal' number of 
trials for all subjects and conditions of interest?

I think I’ve answered this.



Best,



Eric Maris





I would really appreciate it if someone could kindly comment or offer advise 
where appropriate.

Thank you very much in advance for your time.

Yours sincerely,
May



-- 



Heng-Ru May Tan

Institute of Neuroscience and Psychology (INP) ▫ Centre for Cognitive 
Neuroimaging (CCNi) ▫ University of Glasgow

58 Hillhead Street, Glasgow G12 8QB ▫ +44 (0)141-330-5090 ▫ 
Heng-RuMay.Tan at glasgow.ac.uk

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