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| 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? |
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 your want to answer with your data. 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: All subjects, average for condition A. * Files B: All subjects, average for condition B. * Use '''paired tests''' (= dependent tests), or average of differences. * '''Between groups''': Contrast two groups of subjects for one given experimental condition.<<BR>> * Files A: Averages for group of subjects #1. * Files B: Averages for group of subjects #2. * Use '''independent tests''', or difference of averages. What dimensions do you want to explore? |
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| * Cortical sources (template): Full cortex * Cortical sources (template): Regions of interest * Cortical sources (individual anatomy): Full cortex * Cortical sources (individual anatomy): Regions of interest * Time-frequency dimensions What type of results do 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. <<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'''. ==== 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. |
* Cortical sources: * Individual anatomy or template * Constrained (one value per vertex) or unconstrained (three values per grid point) * Full cortex or regions of interests * Time-frequency What level of precisions you want to get? * Averages / difference of averages * Statistically significant differences |
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| 1. Pre-processing of the signals: | 1. Pre-process the signals: |
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| 1. Importing of the == Summary of the analysis == ==== Workflow within-subject (for single trial analysis) ==== 1. Compute source map for each trial (constrained/unconstrained, no normalization) 1. 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 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. '''Low-pass filter''' < 40Hz for evoked responses (optional) 1. '''Normalize '''the subject min-norm averages: Z-score vs. baseline 1. '''Absolute value''' or norm for display ==== Workflow group analysis ==== 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) == 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: [[Tutorials/Difference|Difference]]. ==== Sensor recordings ==== * Not advised for MEG with multiple sessions, correct for EEG. * '''A vs B''': |
1. 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). === Within subject statistics === * '''A ='''''' B''' |
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| * Parametric or non-parametric tests, independent, two-tailed, FDR-corrected. * Correct effect size (we identify correctly where and when the conditions are different). * Ambiguous sign (we cannot say which condition is stronger). === Between subjects statistics === * '''(A-B=0)''': Parametric or non-parametric tests, 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. * 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. ==== Within subject statistics ==== * '''A = ''''''B''' * Never use an absolute value for testing recordings. * Parametric or non-parametric tests, independent, two-tailed, FDR-corrected. * Correct effect size (we identify correctly where and when the conditions are different). * Ambiguous sign (we cannot say which condition is stronger). ==== Between subjects statistics ==== * Not recommended with MEG recordings: do your analysis in source space. == Constrained cortical sources == === Within-subject average === 1. '''Sensor average''': Compute one sensor-level average''' '''per acquisition session and condition. <<BR>>Use the '''same number of trials''' for all the averages. 1. '''Sources''': Estimate sources for each average (constrained or unconstrained, no normalization). 1. '''Source average''': Average the source-level session averages to get one subject average. 1. '''Low-pass filter''' below 40Hz for evoked responses (optional). 1. '''Normalize '''the subject min-norm averages: Z-score vs. baseline (no absolute value).<<BR>>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. 1. '''Do not rectify the cortical maps''', but display them in absolute values. === Between-subjects average === 1. '''Subject average'''s: Compute the within-subject averages for all the subjects, as described above. 1. '''Rectify''' the cortical maps (apply an absolute value). <<BR>>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. 1. '''Project '''the individual source maps on a template (only when using the individual brains). <<BR>> For more details, see tutorial: [[Tutorials/CoregisterSubjects|Group analysis: Subject coregistration]]. 1. '''Smooth '''spatially the sources.<<BR>>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. 1. '''Group average''': Compute grand averages of all the subjects. === Between-subject difference of average === 1. '''Subject averages''': Compute the subject averages for conditions A and B, as described above. 1. '''Subject difference''': Compute the difference between conditions for each subject (A-B). 1. '''Rectify''' the difference of source maps (apply an absolute value). 1. '''Project '''the individual difference on a template. 1. '''Smooth '''spatially the sources. 1. '''Group average''': Compute grand averages of all the subjects: average_subjects(|Ai-Bi|). === Within-subject statistics === 1. '''Sources''': Compute source maps for each trial (constrained or unconstrained, no normalization) 1. '''Statistics''': Compare all the trials of condition A vs all the trials of condition B.<<BR>>Use as many trials as possible for A and B: No need to have an equal number of trials. 1. '''A = B''' |
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| * 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|). |
* Correct effect size: We identify correctly where and when the conditions are different. * Ambiguous sign: We cannot say which condition has the stronger response. 1. '''|A| = |B|''' |
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| * Incorrect effect size, meaningful sign. ==== Unconstrained source maps ==== |
* 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. === Between-subject statistics === 1. '''Sources''': Compute source maps for each trial (constrained or unconstrained, no normalization) 1. '''|A - B| = 0''' * '''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 * 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 * Correct effect size, no sign (cannot detect which condition has the strongest response). * '''[TODO]''' This test is not coded yet. 1. '''A = B''' * Parametric or non-parametric tests, two-tailed, FDR-corrected ('''sign issue?'''). 1. '''|A| = |B|''' * Non-parametric tests, two-tailed, FDR-corrected. === 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 == ==== Within-subject statistics ==== |
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| ==== Regions of interest (scouts) ==== | '''Between-subject statistics''' (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. == 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'''. ==== Within-subject statistics ==== |
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| ==== Time-frequency maps ==== | '''Between-subject statistics '''(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. == Time-frequency maps == ==== Within-subject statistics ==== |
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| == 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.<<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''': (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: [[Tutorials/Difference|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|). |
==== Between-subject statistics ==== * '''(|A|-|B|=0)''': Non-parametric tests, two-tailed, FDR-corrected. |
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.
Contents
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 your want to answer with your data.
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: All subjects, average for condition A.
- Files B: All subjects, average for condition B.
Use paired tests (= dependent tests), or average of differences.
Between groups: Contrast two groups of subjects for one given experimental condition.
- Files A: Averages for group of subjects #1.
- Files B: Averages for group of subjects #2.
Use independent tests, or difference of averages.
What dimensions do you want to explore?
- 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
- Time-frequency
What level of precisions you want to get?
- Averages / difference of averages
- Statistically significant differences
Common pre-processing pipeline
All the event-related studies can start with the pipeline we've introduced in these beginners' tutorials.
- Import the anatomy of the subject (or use a template for all the subjects).
- 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.
- Pre-process the signals:
- Evaluate the quality of the recordings with a power spectrum density (PSD).
- Apply frequency filters (low-pass, high-pass, notch).
- Identify bad channels and bad segments.
- Correct for artifacts with SSP or ICA.
- 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).
Within subject statistics
A = B
- Never use an absolute value for testing recordings.
- Parametric or non-parametric tests, independent, two-tailed, FDR-corrected.
- Correct effect size (we identify correctly where and when the conditions are different).
- Ambiguous sign (we cannot say which condition is stronger).
Between subjects statistics
(A-B=0): Parametric or non-parametric tests, 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.
- 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.
Within subject statistics
A = B
- Never use an absolute value for testing recordings.
- Parametric or non-parametric tests, independent, two-tailed, FDR-corrected.
- Correct effect size (we identify correctly where and when the conditions are different).
- Ambiguous sign (we cannot say which condition is stronger).
Between subjects statistics
- Not recommended with MEG recordings: do your analysis in source space.
Constrained cortical sources
Within-subject average
Sensor average: Compute one sensor-level average per acquisition session and condition.
Use the same number of trials for all the averages.Sources: Estimate sources for each average (constrained or unconstrained, no normalization).
Source average: Average the source-level session averages to get one subject average.
Low-pass filter below 40Hz for evoked responses (optional).
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.Do not rectify the cortical maps, but display them in absolute values.
Between-subjects average
Subject averages: Compute the within-subject averages for all the subjects, as described above.
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.Project the individual source maps on a template (only when using the individual brains).
For more details, see tutorial: Group analysis: Subject coregistration.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.Group average: Compute grand averages of all the subjects.
Between-subject difference of average
Subject averages: Compute the subject averages for conditions A and B, as described above.
Subject difference: Compute the difference between conditions for each subject (A-B).
Rectify the difference of source maps (apply an absolute value).
Project the individual difference on a template.
Smooth spatially the sources.
Group average: Compute grand averages of all the subjects: average_subjects(|Ai-Bi|).
Within-subject statistics
Sources: Compute source maps for each trial (constrained or unconstrained, no normalization)
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.A = B
Parametric or non-parametric tests, independent, two-tailed, FDR-corrected.
- Correct effect size: We identify correctly where and when the conditions are different.
- Ambiguous sign: We cannot say which condition has the stronger response.
|A| = |B|
Non-parametric tests only, independent, two-tailed, FDR-corrected.
- 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.
Between-subject statistics
Sources: Compute source maps for each trial (constrained or unconstrained, no normalization)
|A - B| = 0
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
- 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
- Correct effect size, no sign (cannot detect which condition has the strongest response).
[TODO] This test is not coded yet.
A = B
Parametric or non-parametric tests, two-tailed, FDR-corrected (sign issue?).
|A| = |B|
- Non-parametric tests, two-tailed, FDR-corrected.
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
Within-subject statistics
- 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.
Between-subject statistics (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.
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.
Within-subject statistics
- Average/constrast cortical maps before summarizing scout activity.
- Then consider as constrained or unconstrained source maps.
Between-subject statistics (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.
Time-frequency maps
Within-subject statistics
- 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
(|A|-|B|=0): Non-parametric tests, two-tailed, FDR-corrected.
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: 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.