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Contrasts & Inference - EEG & MEG

Contrasts & Inference - EEG & MEG. Himn Sabir. Topics. 1 st level analysis 2 nd level analysis Space-Time SPMs Time-frequency analysis Conclusion. Voxel Space. (revisited). 2/3D images over peri-stimulus time bins. 2D scalp projection (interpolation in sensor space).

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Contrasts & Inference - EEG & MEG

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  1. Contrasts & Inference - EEG & MEG Himn Sabir

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

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

  4. 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

  5. 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

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

  7. 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

  8. 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

  9. 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

  10. 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 • Especially useful for time-frequency power analysis • Disadvantages: • not possible to make inferences about the temporal extent of evoked responses

  11. Space-Time SPMs (Sensor Maps over Time) How this is done in SMP8 Example:EEG data / 1 subject / 2 conditions (344 trials) • Choose 2D-to-3D image on the SPM8 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

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

  13. 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:

  14. Time-Frequency analysis How this is done in SPM8: Example:MEG data / 1 subject / 2 conditions (86 trials) • 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 mt1_e_eeg.mat and mt2_e_eeg.mat 4. Display 5. 2D Time-Frequency SPMs

  15. 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)

  16. 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.

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

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