Contrasts & Inference - EEG & MEG Himn Sabir 1

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

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

M/EEG modelling and statistics 4 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

1 st Level Analysis 5 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

1 st Level Analysis 6 Epoched time-series data Example: EEG data / 8 subjects / 2 conditions 1.Choose Specify 1st-level 2.Select 2D images 3.Specify M/EEG matfile 4.Specify Time Interval For each subject 5. Click Compute Timing information SPM output: 2 contrast images average_con_0001.img

2 nd Level Analysis 7 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

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

2 nd Level Analysis 9 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

Space-Time SPMs (Sensor Maps over Time) 10 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 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

Space-Time SPMs (Sensor Maps over Time) 11 How this is done in SMP8 Example: EEG data / 1 subject / 2 conditions (344 trials) 2. Choose options 32x32x161 images for each trial / condition 3.Statistical Analysis (test across trials) 4. Estimate + Results 5. Create contrasts 1.Choose 2D-to-3D image on the SPM8 menu and epoched data: e_eeg.mat

Space-Time SPMs (Sensor Maps over Time) 12 How this is done in SMP8 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:

Time-Frequency analysis 13 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:

Time-Frequency analysis 14 How this is done in SPM8: 1.Choose time-frequency on the SPM8 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

Summary 15 (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

References S. J. Kiebel: 10 November ppt-slides on ERP analysis at htm htm 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. 16

Thank You! 17 For difficult questions: (James Kilner)