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 1 st level analysis 2 nd 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 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

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

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

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

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

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

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

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

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 1 st level analysis 2 nd level analysis Space-Time SPMs Time-frequency analysis Conclusion

24 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

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. Typically one wants to analyse multiple subjects’ data acquired under multiple conditions 2-Level Model Time Intensity Time Single voxel time series Model specification Parameter estimation Hypothesis Statistic SPM

26 1 st Level Analysis Epoched time-series data At the 1 st 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 2 nd level 0 1 Similar to fMRI analysis. The aim of the 1 st level is to compute contrast images that provide the input to the second level. Difference: here we are not modelling the data at 1 st level, but simply forming weighted sums of data over time

27 1 st Level Analysis Epoched 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

28 2 nd Level Analysis Epoched time-series data Given the contrast images from the 1 st level (weighted sums), we can now test for differences between conditions or between subjects. = + second level nd level contrast 2 nd 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

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

30 2 nd Level Analysis Epoched 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

31 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

32 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

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

34 Time-Frequency analysis Transform 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:

35 Time-Frequency analysis How 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

36 Summary (2D interpolation or 3D source reconstruction) 1 st Level Analysis (create weighted sums of the data over time) (contrast images = input to the 2 nd level) 2 nd 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

37 References S. J. Kiebel: 10 November ppt-slides on ERP analysis at S.J. Kiebel and K.J. Friston. Statistical Parametric Mapping for Event- Related Potentials I: Generic Considerations. NeuroImage, 22(2): , S.J. Kiebel and K.J. Friston. Statistical Parametric Mapping for Event- Related Potentials II: A Hierarchical Temporal Model. NeuroImage, 22(2): , Todd, C. Handy (ed.) 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: (James Kilner)