Tutorial 27: Group analysis
Authors: Francois Tadel, Elizabeth Bock, Dimitrios Pantazis, Richard Leahy, Sylvain Baillet
This page provides some general recommendations for your group analysis. It is not directly related with the auditory dataset, but provides guidelines that have to be considered for any MEG/EEG experiment.
Subject-level statistics
For one unique subject, test for significant differences between two experimental conditions:
Compare the single trials corresponding to each condition.
In most cases, you do not need to normalize the data.
Use independent tests.
For help with the implications of testing the relative or absolute values, see: Difference.
Sensor recordings:
- Not advised for MEG with multiple runs, correct for EEG.
A vs B:
- Never use an absolute value for testing recordings.
Parametric or non-parametric tests, independent, two-tailed, FDR-corrected.
- Correct effect size, ambiguous sign.
Constrained source maps (one value per vertex):
- Use the non-normalized minimum norm maps for all the trials (current density maps, no Z-score).
A vs B:
- Null hypothesis H0: (A=B).
Parametric or non-parametric tests, independent, two-tailed, FDR-corrected.
- Correct effect size, ambiguous sign.
|A| vs |B|:
- Null hypothesis H0: (|A|=|B|).
Non-parametric tests only, independent, two-tailed, FDR-corrected.
- Incorrect effect size, meaningful sign.
Unconstrained source maps (three values per vertex):
- Use the non-normalized minimum norm maps for all the trials (current density maps, no Z-score).
We need to test the norm of the three orientations instead of testing the orientations separately.
Norm(A) vs. Norm(B):
- Null hypothesis H0: (|A|=|B|).
Non-parametric tests only, independent, two-tailed, FDR-corrected.
- Incorrect effect size, meaningful sign.
Time-frequency maps:
- Test the non-normalized time-frequency maps for all the trials (no Z-score or ERS/ERD).
- The values tested are power or magnitudes, all positive, so (A=B) and (|A|=|B|) are equivalent.
|A| vs |B|:
- Null hypothesis H0: (|A|=|B|)
Non-parametric tests only, independent, two-tailed, FDR-corrected.
- Correct effect size, meaningful sign.
Group-level statistics [TODO]
Subject averages
You need first to process the data separately for each subject:
Compute the subject-level averages, using the same number of trials for each subject.
Sources: Average the non-normalized minimum norm maps (current density maps, no Z-score).Sources and time-frequency: Normalize the data to bring the different subjects to the same range of values (Z-score normalization with respect to a baseline - never apply an absolute value here).
Sources computed on individual brains: Project the individual source maps on a template (see the coregistration tutorial). Not needed if the sources were estimated directly on the template anatomy.
Note: We evaluated the alternative order (project the sources and then normalize): it doesn't seem to be making a significant difference. It's more practical then to normalize at the subject level before projecting the sources on the template, so that we have normalized maps to look at for each subject in the database.Constrained sources: Smooth spatially the sources, to make sure the brain responses are aligned. Problem: This is only possible after applying an absolute value, smoothing in relative values do not make sense, as the positive and negative signals and the two sides of a sulcus would cancel out. [TODO]
Group statistic
Two group analysis scenarios are possible:
One condition recorded for multiple subjects, comparison between two groups of subjects:
- Files A: Averages for group of subjects #1.
- Files B: Averages for group of subjects #2.
Use independent tests: Exactly the same options as for the single subject (described above)
Two conditions recorded for multiple subjects, comparison across all subjects:
- Files A: All subjects, average for condition A.
- Files B: All subjects, average for condition B.
Use paired tests (= dependent tests), special cases listed below.
Paired tests
Sensor recordings:
(A-B=0): Parametric or non-parametric tests, two-tailed, FDR-corrected.
Constrained source maps (one value per vertex):
(A-B=0): Parametric or non-parametric tests, two-tailed, FDR-corrected.
(|A|-|B|=0): Non-parametric tests, two-tailed, FDR-corrected.
Unconstrained source maps (three values per vertex):
(Norm(A-B)=0): Non-parametric tests, one-tailed (non-negative statistic), FDR-corrected.
(Norm(A)-Norm(B)=0): Non-parametric tests, two-tailed, FDR-corrected.
Time-frequency maps:
(|A|-|B|=0): Non-parametric tests, two-tailed, FDR-corrected.
For help with relative/absolute options, read the previous tutorial: Difference.
Workflow: Current problems [TODO]
The following inconsistencies are still present in the documentation. We are actively working on these issues and will update this tutorial as soon as we found solutions.
- [Group analysis] Unconstrained sources: How to compute a Z-score?
- Zscore(A): Normalizes each orientation separately, which doesn't make much sense.
- Zscore(Norm(A)): Gets rid of the signs, forbids the option of a signed test H0:(Norm(A-B)=0)
See also the tutorial: Source estimation
- We would need a way to normalize across the three orientations are the same time.
- [Group analysis] Constrained sources: How do we smooth?
- Group analysis benefits a lot from smoothing the source maps before computing statistics.
- However this requires to apply an absolute value first. How do we do?
- [Single subject] Unconstrained sources: How do compare two conditions with multiple trials?
- Norm(A)-Norm(B): Cannot detect correctly the differences
- (A-B): We test individually each orientation, which doesn't make much sense.
- We would need a test for the three orientations at once.