Experimental Design, Contrasts & Inference - EEG & MEG

1. Experimental Design, Contrasts & Inference - EEG & MEG Joseph Brooks (ICN) Maria Joao (FIL) Methods for Dummies 2007 Wellcome Department For Neuroimaging 13/02/2008

2. 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

3. Popular approaches to M/EEG Data • Event-Related Potentials (ERP) & • Event-Related Fields (ERF) • ERP/F Quantification Approaches • Peaks, latency, area-under-curve • Spectral Analysis (a.k.a. time-frequency) • Connectivity

4. What is the ERP/ERF? • Def: the average (across trials/subjects) potential/field at the scalp relative to some specific event in time Stimulus/Event Onset

5. What is the ERP/ERF? • Def: the average (across trials/subjects) potential at the scalp relative to some specific event in time Averaging

6. 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

7. + + + + + + - - - - - - Interpreting ERP/ERF Waveforms • 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 sensor

8. Latent Components Any given electrode/sensor records a series of temporally overlapping latent components Latent Components Observed Waveform

9. Latent Components A given waveform could have arisen from many combinations of latent components Latent Components Observed Waveform OR OR Many others…

10. Important Observation #1 The morphology of a component is not necessarily obvious from the observed waveform when components overlap Latent Components Observed Waveform

11. Important Observation #2 Peaks ≠ Components Local maxima and minima in a waveform are not necessarily the best indicators of a component Latent Components Observed Waveform

12. Important Observation #3 Amplitude and latency of components are not independent A change of amplitude in one component can change amplitude and timing of many peaks Observed Waveform Latent Components

13. Feeling hopeless? Given these observations how can one make valid inferences about latent components from observed waveforms? Experimental design to the rescue!

14. 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

15. ERP/ERF Quantification To Peak or Not to Peak? Peak amplitude & latency are common measures BUT THEY ARE POOR MEASURES

16. ERP/ERF Quantification Amplitude and Latency are NOT independent Apparent amplitude difference is actually a difference in latency variance

17. ERP/ERF Quantification Solution: Use non-peak measures such as Area-Under-the-Curve Area under curves is same in the two average waveforms

18. Projection SPM5-stats SPM{t} SPM{F} Control of FWE 2D - scalp mass-univariate analysis 3D-source space Kiebel, S. 2005 SPM Approach to M/EEG Preprocessing Raw M/EEG data Single trials Epoching Artefacts Filtering Averaging, etc.

19. Projection Preprocessing SPM5-stats The transformation of discreet channels into a continuous 2D interpolated image of M/EEG signals Sensor Space Scalp Space

20. Projection Preprocessing SPM5-stats The transformation of discreet channels into a continuous 2D interpolated image of M/EEG signals

21. Preprocessing Projection SPM5-stats SPM{t} SPM{F} Control of FWE 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 mass-univariate analysis Kiebel, S. 2005

22. Experimental Design, Contrasts & Inference - EEG & MEG Joe Brooks (ICN) Maria Joao (FIL) Methods for Dummies 2007 Wellcome Department For Neuroimaging 13/02/2008

23. 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

24. Voxel Space (revisited) 2/3D images over peri-stimulus time bins 2D scalp projection (interpolation in sensor space) 3D source reconstruction (brain space) [Next week!] Data ready to be analysed

25. M/EEG modelling and statistics Epoched 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. Model specification Parameter estimation Time Time Hypothesis Statistic Single voxel time series Intensity Typically one wants to analyse multiple subjects’ data acquired under multiple conditions 2-Level Model SPM

26. 1st Level Analysis Epoched time-series data • 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 At the 1st level, we select periods or time points in peri-stimulous time that we would like to analyse. Choice made a priori. Time is treated as an experimental factor and we form weighted-sums over peri-stimulus time to provide input to the 2nd level Example: if we were interested in the N170 component, one could average the data between 150 and 190 milliseconds. 1 0

27. 1st Level Analysis Epoched time-series data Example:EEG data / 8 subjects / 2 conditions For each subject • Choose Specify 1st-level SPM output: 2 contrast images 2. Select 2D images average_con_0001.img 3. Specify EEG file 4. Specify Time Interval Timing information 5. Click Compute

28. 2nd Level Analysis Epoched time-series data Given the contrast images from the 1st level (weighted sums), we can now test for differences between conditions or between subjects. 2nd level model = used in fMRI 2nd level contrast -1 1 SPM output: Voxel map, where each voxel contains one statistical value = + The associated p-value is adjusted for multiple comparisons second level

29. 2nd Level Analysis Epoched time-series data Example:EEG data / 8 subjects / 2 conditions 1. Specify 2nd-level SPM output: Design Matrix 2. Specify Design

30. 2nd Level Analysis Epoched time-series data Example:EEG data / 8 subjects / 2 conditions Ignore brain outline: 3. Click Estimate Output: 4. Click Results 5. Define Contrasts “Regions” within the 2D map in which the difference between the two conditions is significant

31. Space-Time SPMs (Sensor Maps over Time) Time as another dimension of a Random Field 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 Both approaches available: choice depends on the data • 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 • Disadvantages: • not possible to make inferences about the temporal extent of evoked responses

32. Space-Time SPMs (Sensor Maps over Time) How this is done in SMP5 Example:EEG data / 1 subject / 2 conditions (344 trials) • Choose 2D-to-3D image on the SPM5 menu and epoched data: e_eeg.mat • Statistical Analysis (test across trials) 2. Choose options 32x32x161 images for each trial / condition 4. Estimate + Results 5. Create contrasts

33. 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!!! Overlay with EEG image: • More than 1 subject: • Same procedure with averaged ERP data for each subject • Specify contrasts and take them to the 2nd level analysis

34. Time-Frequency analysis Transform data into time-frequency domain Useful for evoked responses and induced responses: Not phase-locked to the stimulus onset – not revealed with classical averaging methods SPM uses the Morlet Wavelet Transform Wavelets: mathematical functions that can break a signal into different frequency components. [Tallon-Baudry et. al. 1999] The transform is a convolution The Power and Phase Angle can be computed from the wavelet coefficients:

35. Time-Frequency analysis How this is done in SPM5: Example:MEG data / 1 subject / 2 conditions (86 trials) • 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 mt1_e_eeg.mat and mt2_e_eeg.mat 4. Display 5. 2D Time-Frequency SPMs

36. Summary Projection to voxel space (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)

37. 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.

38. Thank You! For difficult questions:j.kilner@fil.ion.ucl.ac.uk(James Kilner)