1 experimental design, contrasts & inference - eeg & meg joseph brooks (icn) maria joao...
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Experimental Design, Contrasts & Inference - EEG & MEG
Joseph Brooks (ICN)Maria Joao (FIL)
Methods for Dummies 2007Wellcome Department For Neuroimaging
13/02/2008
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Topics
• Exp. design and ERPs• SPM for EEG-MEG• 2D interpolation• 1st level analysis• 2nd level analysis• Time as another dimension• Time-frequency analysis• Conclusion
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Popular approaches to M/EEG Data
Event-Related Potentials (ERP) & Event-Related Fields (ERF)
ERP/F Quantification ApproachesPeaks, latency, area-under-curve
Spectral Analysis (a.k.a. time-frequency)
Connectivity
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What is the ERP/ERF?
-Def: the average (across trials/subjects) potential/field at the scalp relative to some specific event in time
Stimulus/EventOnset
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What is the ERP/ERF?
-Def: the average (across trials/subjects) potential at the scalp relative to some specific event in time
Averaging
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What is the ERP/ERF?
-Def: the average (across trials/subjects) potential at the scalp relative to some specific event in time
Reflects reliable changes in potential that are strongly time-locked to stimulus onset (i.e. are synchronous over trials)
Non-time-locked activity is lost to averaging
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Interpreting ERP/ERF Waveforms
sensor
ERP/ERF waveforms are often interpreted in terms of their constituent components
Component (def) - Scalp-recorded electrical activity that is generated by a given patch of cortex engaged in a specific computational operation
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Latent Components
Any given electrode/sensor records a series of temporally overlapping latent components
Latent Components Observed Waveform
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Latent Components
A given waveform could have arisen from many combinations of latent components
Latent Components Observed Waveform
OR
OR
Many others…
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Important Observation #1
The morphology of a component is not necessarily obvious from the observed waveform when
components overlap
Latent Components Observed Waveform
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Important Observation #2
Peaks ≠ ComponentsLocal maxima and minima in a waveform are not necessarily the best indicators of a component
Latent Components
Observed Waveform
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Important Observation #3Amplitude and latency of components are not
independent
A change of amplitude in one component can change amplitude and timing of many peaks
Latent Components Observed Waveform
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Feeling hopeless?
Given these observations how can one make valid inferences about latent components from observed
waveforms?
Experimental design to the rescue!
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Design Strategies
Focus on one component and design experiment to stop other components from varying, especially
temporally overlapping components
Focus on easily isolated components that are well-known
Focus on large components. Large components are less sensitive to variations in others
Test hypotheses that are component-independent
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ERP/ERF Quantification
To Peak or Not to Peak?
Peak amplitude & latency are common measures
BUT THEY ARE POOR MEASURES
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ERP/ERF Quantification
Amplitude and Latency are NOT independent
Apparent amplitude difference is actually a difference in latency variance
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ERP/ERF Quantification
Solution: Use non-peak measures such as Area-Under-the-Curve
Area under curves is same in the two average waveforms
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SPM Approach to M/EEG
Raw M/EEG data
Raw M/EEG data
Single trialsEpochingArtefactsFiltering
Averaging, etc.
Single trialsEpochingArtefactsFiltering
Averaging, etc.
PreprocessingPreprocessing
2D - scalp2D - scalp
ProjectionProjection
3D-sourcespace
3D-sourcespace
mass-univariateanalysis
mass-univariateanalysis
SPM{t}SPM{F}
Control of FWE
SPM{t}SPM{F}
Control of FWE
SPM5-statsSPM5-stats
Kiebel, S. 2005
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PreprocessingPreprocessing ProjectionProjection SPM5-statsSPM5-stats
The transformation of discreet channels into a continuous 2D interpolated image of M/EEG signals
Sensor Space Scalp Space
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PreprocessingPreprocessing ProjectionProjection SPM5-statsSPM5-stats
The transformation of discreet channels into a continuous 2D interpolated image of M/EEG signals
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PreprocessingPreprocessing ProjectionProjection
mass-univariateanalysis
mass-univariateanalysis
SPM{t}SPM{F}
Control of FWE
SPM{t}SPM{F}
Control of FWE
SPM5-statsSPM5-stats
Kiebel, S. 2005
With data in 2D (+time) map form we can now apply similar statistical procedures
as used in FMRI
Create SPMS of significant effects
Use random field theory to control error
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Experimental Design, Contrasts & Inference - EEG & MEG
Joe Brooks (ICN)Maria Joao (FIL)
Methods for Dummies 2007Wellcome Department For Neuroimaging
13/02/2008
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Topics
• Experimental design and ERPs• SPM for EEG-MEG• Projection to voxel space• 1st level analysis• 2nd level analysis• Space-Time SPMs• Time-frequency analysis• Conclusion
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Voxel Space(revisited)
2D scalp projection
(interpolation in sensor space)
3D source reconstruction
(brain space)
2/3D images over peri-stimulus time bins
[Next week!]
Data ready to be analysed
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M/EEG modelling and statisticsEpoched time-series data
Data is analysed using the General Linear model at each voxel and Random Field Theory to adjust the p-values for multiple comparisons.
Typically one wants to analyse multiple subjects’ data acquired under multiple conditions
2-Level ModelTim
eIntensity
Tim
e
Single voxel time series
Model specification
Parameter
estimation
Hypothesis
Statistic
SPM
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1st Level AnalysisEpoched time-series data
At the 1st level, we select periods or time points in peri-stimulous time that we would like to analyse. Choice made a priori.
Example: if we were interested in the N170 component, one could average the data between 150 and 190 milliseconds.
Time is treated as an experimental factor and we form weighted-sums over peri-stimulus time to provide input to the 2nd level
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•Similar to fMRI analysis. The aim of the 1st level is to compute contrast images that provide the input to the second level.
•Difference: here we are not modelling the data at 1st level, but simply forming weighted sums of data over time
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1st Level AnalysisEpoched time-series data
Example: EEG data / 8 subjects / 2 conditions
1. Choose Specify 1st-level
2. Select 2D images
For each subject
3. Specify EEG file
4. Specify Time Interval
5. Click Compute
SPM output:
2 contrast images
average_con_0001.img
Timing information
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2nd Level AnalysisEpoched time-series data
Given the contrast images from the 1st level (weighted sums), we can now test for differences between conditions or between subjects.
1Tc =
2X
2
+ 2
second levelsecond level
-1 1
2nd level contrast 2nd level model = used in fMRI
SPM output:
Voxel map, where each voxel contains one statistical value
The associated p-value is adjusted for multiple comparisons
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2nd Level AnalysisEpoched time-series data
Example: EEG data / 8 subjects / 2 conditions
1. Specify 2nd-level
2. Specify Design
SPM output:
Design Matrix
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2nd Level AnalysisEpoched time-series data
Example: EEG data / 8 subjects / 2 conditions
3. Click Estimate
4. Click Results
5. Define Contrasts
Output: Ignore brain outline:
“Regions” within the 2D map in
which the difference between the two conditions
is significant
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Space-Time SPMs (Sensor Maps over Time)Time as another dimension of a Random Field
Advantages:
• If we had no a priori knowledge where and when the difference between two conditions would emerge. Weighted sums of data, over time, not appropriate in this case
• Especially useful for time-frequency power analysis
Both approaches available: choice depends on the data
We can treat time as another dimension and construct
3D images (2D space + 1D peri-stimulus time)
We can test for activations in space and time
Disadvantages:
• not possible to make inferences about the temporal extent of evoked responses
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Space-Time SPMs (Sensor Maps over Time)How this is done in SMP5
Example: EEG data / 1 subject / 2 conditions (344 trials)
1. Choose 2D-to-3D image on the SPM5 menu and epoched data: e_eeg.mat
2. Choose options
32x32x161 images for each trial /
condition
3. Statistical Analysis
(test across trials)
4. Estimate + Results
5. Create contrasts
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Space-Time SPMs (Sensor Maps over Time)How this is done in SMP5
Example: EEG data / 1 subject / 2 conditions (344 trials)
Ignore brain outline!!!
More than 1 subject:
• Same procedure with averaged ERP data for each subject
• Specify contrasts and take them to the 2nd level analysis
Overlay with EEG image:
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Time-Frequency analysisTransform data into time-frequency domain
Not phase-locked to the stimulus onset – not revealed with classical averaging methods
[Tallon-Baudry et. al. 1999]
Useful for evoked responses and induced responses:
SPM uses the Morlet Wavelet Transform
Wavelets: mathematical functions that can break a signal into different frequency components.
The transform is a convolution
The Power and Phase Angle can be computed from the wavelet coefficients:
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Time-Frequency analysisHow this is done in SPM5:
1. Choose time-frequency on the SPM5 menu and epoched data: e_meg.mat
2. Choose options
t1_e_eeg.mat and t2_e_eeg.mat power at each frequency, time and channel
(t1*); phase angles (t2*)
3. Average
4. Display
mt1_e_eeg.mat and mt2_e_eeg.mat
Example: MEG data / 1 subject / 2 conditions (86 trials)
5. 2D Time-Frequency SPMs
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Summary
(2D interpolation or 3D source reconstruction)
1st Level Analysis
(create weighted sums of the data over time)
(contrast images = input to the 2nd level)
2nd Level Analysis(test for differences between conditions or groups)
(similar to fMRI analysis)
Time-Space SPMs(time as a dimension of the measured response variable)
Time-Frequency Analysis(induced responses)
Projection to voxel space
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References
• S. J. Kiebel: 10 November 2005. ppt-slides on ERP analysis at http://www.fil.ion.ucl.ac.uk/spm/course/spm5_tutorials/SPM5Tutorials.htm
• S.J. Kiebel and K.J. Friston. Statistical Parametric Mapping for Event-Related Potentials I: Generic Considerations. NeuroImage, 22(2):492-502, 2004.
• S.J. Kiebel and K.J. Friston. Statistical Parametric Mapping for Event-Related Potentials II: A Hierarchical Temporal Model. NeuroImage, 22(2):503-520, 2004.
• Todd, C. Handy (ed.). 2005. Event-Related Potentials: A Methods Handbook. MIT
• Luck, S. J. (2005). An Introduction to the Event-Related Potential Technique. MIT Press.
