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 event-related analysis. It is not directly related with the auditory dataset, but provides guidelines you should consider for any MEG/EEG experiment.
We do not provide standard analysis pipelines for resting or steady state recordings yet, but we will add a few examples soon in the section Other analysis scenarios of the tutorials page.

What is your question?

The most appropriate analysis pipeline for your data depends on the question you are trying to answer. Before defining what are the main steps of your analysis, you should be able to state clearly the question you want to answer with your recordings.

What dimension?

• MEG/EEG recordings
• Cortical sources
  • Individual anatomy or template
  • Constrained (one value per vertex) or unconstrained (three values per grid point)
  • Full cortex or regions of interests
• Frequency or time-frequency maps

What kind of experiment?

• Within subject: Contrast two experimental conditions across trials, for one single subject.

  • Files A: Single trials for condition A.
  • Files B: Single trials for condition B.

• Between subjects: Contrast two experimental conditions across multiple subjects.

  • Files A: Subject-level averages for condition A (all the subjects).
  • Files B: Subject-level averages for condition B (all the subjects).

• Between groups: Contrast two groups of subjects for one given experimental condition.

  • Files A: Subject-level averages for group #1.
  • Files B: Subject-level averages for group #2.

What level of precision?

• Difference of averages
• Statistically significant differences between conditions or groups

What statistical test?

• A = B

  • Tests the null hypothesis H0:(A=B) against the alternative hypothesis H1:(A≠B)

  • Significance level obtained with two-sided tests.

  • Correct effect size: We identify correctly where and when the conditions are different.

  • Ambiguous sign: We cannot say which condition is stronger.

• |A - B| = 0

  • Tests the null hypothesis H0:(|A-B|=0) against the alternative hypothesis H1:(|A-B|>0)

  • Significance level obtained with one-sided tests.

  • Correct effect size: We identify correctly where and when the conditions are different.

  • No sign: We cannot say which condition is stronger.

• |A| = |B|

  • Tests the null hypothesis H0:(|A|=|B|) against the alternative hypothesis H1:(|A|≠|B|)

  • Significance level obtained with two-sided tests.

  • Incorrect effect size: Doesn't detect correctly the effects when A and B have opposite signs.

  • Correct sign: We can identify correctly which condition has a stronger response.

• |x| represents the modulus of the values:
  • Absolute value for scalar values (recordings, constrained sources, time-frequency maps)
  • Norm of the three orientations for unconstrained sources.

Common pre-processing pipeline

Most event-related studies can start with the pipeline we've introduced in these tutorials.

1. Import the anatomy of the subject (or use a template for all the subjects).
2. Access the recordings:
   • Link the continuous recordings to the Brainstorm database.
   • Prepare the channel file: co-register sensors and MRI, edit type and name of channels.
   • Edit the event markers: fix the delays of the triggers, mark additional events.
3. Pre-process the signals:
   • Evaluate the quality of the recordings with a power spectral density plot (PSD).
   • Apply frequency filters (low-pass, high-pass, notch).
   • Identify bad channels and bad segments.
   • Correct for artifacts with SSP or ICA.
4. Import the recordings in the database: epochs around some markers of interest.

EEG recordings

Average

• Average the epochs across sessions and subjects: OK.
• Electrodes are in the same standard positions for all the subjects (e.g. 10-20).
• Never use an absolute value for averaging or contrasting sensor-level data.
• Group averages: Use the same number of trials for all the subjects.

Statistics: Within subject

• A = B

  • Parametric or non-parametric t-test, independent, two-tailed, FDR-corrected.

  • Use as many trials as possible for A and B: No need to have an equal number of trials.

Statistics: Within subject

• A = B

  • First-level statistic: Average

    • For each subject, compute the sensor average for conditions A and B.
    • Use the same number of trials for all the the averages.

  • Second-level statistic: t-test

    • Parametric or non-parametric t-test, paired, two-tailed, FDR-corrected.

Statistics: Between groups

• A = B

  • First-level statistic: Average

    • For each subject, compute the sensor average for conditions A and B.
    • Use the same number of trials for all the the averages.

  • Second-level statistic: t-test

    • Parametric or non-parametric t-test, independent, two-tailed, FDR-corrected.

MEG recordings

Average

• Average the epochs within each session: OK.
• Averaging across sessions: Not advised because the head of the subject may move between runs.
• Averaging across subjects: Strongly discouraged because the shape of the heads vary but the sensors are fixed. One sensor does not correspond to the same brain region for different subjects.
• Tolerance for data exploration: Averaging across runs and subjects can be useful for identifying time points and sensors with interesting effects but should be avoided for formal analysis.
• Note for Elekta/MaxFilter users: You can align all sessions to a reference session, this will allow direct channel comparisons within-subject. Not recommended across subjects.
• Never use an absolute value for averaging or contrasting sensor-level data.
• Group averages: Use the same number of trials for all the sessions.

Statistics: Within subject

• A = B

  • Parametric or non-parametric t-test, independent, two-tailed, FDR-corrected.

  • Use as many trials as possible for A and B: No need to have an equal number of trials.

Statistics: Between subjects

• Not recommended with MEG recordings: do your analysis in source space.

Statistics: Between-groups

• Not recommended with MEG recordings: do your analysis in source space.

Constrained cortical sources

Average: Within subject

1. Sensor average: Compute one sensor-level average per acquisition session and condition.
   Use the same number of trials for all the averages.

2. Sources: Estimate sources for each average (constrained or unconstrained, no normalization).

3. Source average: Average the source-level session averages to get one subject average.

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

5. Normalize the subject min-norm averages: Z-score vs. baseline (no absolute value).
   Justification: The amplitude range of current densities may vary between subjects because of anatomical or experimental differences. This normalization helps bringing the different subjects to the same range of values.

6. Do not rectify the cortical maps, but display them in absolute values.

Average: Between subjects

1. Subject averages: Compute the within-subject averages for all the subjects, as described above.

2. Rectify the cortical maps (apply an absolute value).
   Justification: 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. This is true even if a standard anatomy is used for reconstruction.

3. Project the individual source maps on a template (only when using the individual brains).
   For more details, see tutorial: Group analysis: Subject coregistration.

4. Smooth spatially the sources.
   Justification: The effects observed with constrained cortical maps may be artificially very focal, not overlapping very well between subjects. Smoothing the cortical maps may help the activated regions overlap between subjects.

5. Group average: Compute grand averages of all the subjects.

Average: Between groups

• Same as average between subjects.

Difference of averages: Between subjects

1. Subject averages: Compute the subject averages for conditions A and B, as described above.

2. Subject difference: Compute the difference between conditions for each subject (A-B).

3. Rectify the difference of source maps (apply an absolute value).

4. Project the individual difference on a template.

5. Smooth spatially the sources.

6. Group average: Compute grand averages of all the subjects: average_subjects(|Ai-Bi|).

Difference of averages: Between groups

1. Subject averages: Compute the subject averages, as described above (rectified values).

2. Grand averages: Compute the group-level averages for groups #1 and #2 as described in "Average: Between subjects"

3. Difference: Compute the difference between group-level averages: avg(|A1|)-avg(|A2|)

4. Limitations: Because we rectify the source maps before computing the difference, we lose the ability to detect the differences between equal values of opposite signs. And we cannot keep the sign because we are averaging across subjects. Therefore, many effects are not detected correctly.

Statistics: Within subject

1. Sources: Compute source maps for each trial (constrained or unconstrained, no normalization)

2. Statistics: Compare all the trials of condition A vs all the trials of condition B.
   Use as many trials as possible for A and B: No need to have an equal number of trials.

3. A = B

   • Parametric or non-parametric t-test, independent, two-tailed, FDR-corrected.

   • Indicates when and where there is a significant effect (but not in which direction).

4. |A| = |B|

   • Non-parametric tests only, independent, two-tailed, FDR-corrected.

   • Indicates which condition corresponds to a stronger brain response (for a known effect).

Statistics: Between subjects

• Parametric: |A - B| = 0

  • Sources: Compute source maps for each trial (constrained or unconstrained, no normalization)

  • First-level statistic: Compute a t-statistic for the source maps of all the trials A vs B.

    • Process2: "Test > Parametric test: Independent": t-test with equal variance, no test.

    • Use as many trials as possible for A and B: No need to have an equal number of trials.
    • With a relatively high number of trials, the t-values follow approximately a Z-distribution.

  • Second-level statistic: Compute a one-sampled power test based on the subject t-statistic.

    • Process1: "Test > Parametric test against zero": One-sampled Chi-square test

    • This tests for |A-B|=0 using a power test: X = sum(|ti|^2) ~ Chi-square distribution

  • [TODO] This test is not coded yet.

• Non-parametric: |A - B| = 0

• |A| = |B|

  • Non-parametric tests, two-tailed, FDR-corrected.

Statistics: Between groups

• [TODO]

Design considerations

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

Unconstrained cortical sources

Statistics: Within subject

• 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.

Statistics: Between subjects

• (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.

Statistics: Between groups

[TODO]

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.

Statistics: Within subject

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

Statistics: Between subjects

• 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.

Time-frequency maps

Statistics: Within subject

• 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.

Statistics: Between subjects

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

Statistics: Between groups

[TODO]

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 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.

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