Tutorial 27: Workflows

[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE]

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.

What is your question?

The most appropriate analysis pipeline for your data depends on the question you are trying to answer.

What is the objective you have with your data?

• Contrast two experimental conditions across trials, for one single subject
• Contrast two experimental conditions across multiple subjects
• Contrast two groups of subjects for one given experimental condition

What are the dimensions you want to explore?

• MEG/EEG recordings
• Cortical sources
• Time-frequency dimensions

What level of precisions you want to get?

• Averages
• Difference of averages
• Identify statistically significant differences

[TODO: WHEN TO USE WHAT]

Important physical limitations and implications

Recommendations for averaging/constrasting different types of data.

MEG sensor data

• MEG channels are not aligned across subjects (or sessions) because the physical position of channels varies with respect to the head.
  As a result, do not contrast/average MEG channel data across subjects or sessions.

• However, even though this is not recommended for formal analysis, it can be extremely useful for data exploration. Most of channel patterns are spatially smooth and averaging across subjects will probably highlight interesting effects, and suggest time points and sensors with experimental effects. Examples include auditory/language signals (auditory cortices align reasonably well), attention effects (parietal/occipital alpha is fairly consistent across subjects) and most other perceptional/cognitive processes.
• Note for maxfilter users: A good practice is to align all within-subject data to a reference fif file (align all sessions to a reference session). This will allow direct channel comparisons within-subject. Aligning data across subjects is not recommended since it can introduce large data distortions (though sometimes it may work well).
• This does not apply to EEG because it uses standard channel configurations (e.g. 10-20).

Cortical maps

• Cortical maps have ambiguous signs across subjects: reconstructed sources depend heavily on the orientation of true cortical sources. Given the folding patterns of individual cortical anatomies vary considerably, cortical maps have subject-specific amplitude and sign ambiguities (e.g. positive vs. negative sources). This is true even if a standard anatomy is used for reconstruction.
• As a result, to average/contrast cortical maps:

  • Across subjects: Rectify the cortical maps (absolute values)

  • Within subject: Do not rectify the cortical maps

Regions of interest (scouts)

• Even within-subject cortical maps have sign ambiguities. MEG has limited spatial resolution and sources in opposing sulcal/gyral areas are reconstructed with inverted signs (constrained orientations only). Averaging activity in cortical regions of interest (scouts) would thus lead to signal cancelation. To avoid this brainstorm uses algorithms to manipulate the sign of individual sources before averaging within a cortical region. Unfortunately, this introduces an amplitude and sign ambiguity in the time course when summarizing scout activity.

• As a result, perform any interesting within-subject average/contrast before computing an average scout time series.

Design considerations

• Use within-subject designs whenever possible (i.e. collect two conditions A and B for each subject). Such designs are not only statistically optimal, but also ameliorate the between-subject sign ambiguities as contrasts can be constructed within each subject.
• Contrast/average data within subject before comparing data between subjects.

Summary of the analysis

Workflow within-subject (for single trial analysis)

1. Compute source map for each trial (constrained/unconstrained, no normalization)
2. Estimate differences between two conditions A/B for which you have multiple trials

Workflow within-subject (for group analysis)

1. Compute sensor average per acquisition session => Session-level average for each condition

2. Compute source map for each session average (constrained or unconstrained, no normalization)

3. Average source maps across sessions => Subject-level average for each condition

4. Low-pass filter < 40Hz for evoked responses (optional)

5. Normalize the subject min-norm averages: Z-score vs. baseline

6. Absolute value or norm for display

Workflow group analysis

1. Project the individual source maps on a template (no absolute value)

2. Constrained sources: Smooth spatially the sources (no absolute value)

3. Compute grand averages or other group-level statistics (signed or absolute)

Within-subject 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 sessions, 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.

Regions of interest (scouts)

• Average/constrast cortical maps before summarizing scout activity.
• Then consider as constrained or unconstrained source maps.

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.

Between-subject statistics [TODO]

Subject averages

You need first to process the data separately for each subject:

1. 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).

2. 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).

3. 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.

4. 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: (not recommended in MEG)

  • (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 (sign issue?).

  • (|A|-|B|=0): Non-parametric tests, two-tailed, FDR-corrected.

  • (|A-B| = 0): ???

• 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.

• Regions of interest (scouts):

  • Comparison of scout time series between subjects is tricky because there is no way to avoid sign ambiguity for different subjects. Thus there are no clear recommendations. Rectifying before comparing scout time series between subjects can be a good idea or not depending on different cases. Having a good understanding of the data (multiple inspections across channels/sources/subjects) can offer hints whether rectifying the scout time series is a good idea. Using unconstrained cortical maps to create the scout time series can ameliorate ambiguity concerns.

• For help with relative/absolute options, read the previous tutorial: Difference.

Averages

• In order to compute grand averages (across subjects), you should rectify your source maps before averaging. Averaging the absolute values of the subject-level averages will help avoiding possible cancellation effects due to anatomical differences between subjects.

• If you have two conditions A and B to contrast, first compute the difference within-subject (A-B), then average the rectified differences: average_subjects(|Ai-Bi|).

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.
• [Group analysis] Rectify source maps?
  • Recommended in Dimitrios' guidelines, which is incoherent with the rest of the page.