= Tutorial 27: Group analysis =
'''[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.

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== 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. <<BR>>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'''.

== Summary of the analysis ==
'''Workflow single subject    (for single trial analysis) '''

 1. Compute min-norm map for each trial (constrained/unconstrained, no normalization)
 1. Estimate differences between two conditions A/B for which we have multiple trials

''' Workflow single subject   (for group analysis) '''

 1. Compute sensor '''average '''per acquisition session    => Session-level average for each condition
 1. Compute '''source map''' for each session average (constrained or unconstrained, no normalization)
 1. '''Average '''source  maps across sessions   => Subject-level average for each condition
 1. Optional: '''Low-pass filter''' < 40Hz for evoked responses
 1. '''Normalize '''the subject min-norm averages: Z-score vs. baseline
 1. '''Absolute value''' or norm for display

''' Workflow group analysis '''

 1. Compute subject-level '''averages '''min-norm maps, with same number of trials
 1. '''Normalize '''the subject min-norm averages: Z-score vs. baseline  (no absolute value)
 1. '''Project '''the individual source maps on a template                           (no absolute value)
 1. Constrained sources: '''Smooth '''spatially the sources                       (no absolute value)
 1. Compute grand averages or other group-level statistics                 (signed or absolute)

== Subject-level ==
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: [[Tutorials/Difference|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.

'''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:

 1. Compute the ''' subject-level averages''', using the '''same number of trials''' for each subject.<<BR>> Sources: Average the non-normalized minimum norm maps (current density maps, no Z-score).

 1. 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).

 1. Sources computed on individual brains: '''Project '''the individual source maps on a template (see the [[Tutorials/CoregisterSubjects|coregistration tutorial]]). Not needed if the sources were estimated directly on the template anatomy. <<BR>>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.

 1. 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: [[Tutorials/Difference|Difference]].

<<TAG(Advanced)>>

== 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: [[http://neuroimage.usc.edu/brainstorm/Tutorials/SourceEstimation#Z-score|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.

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