Advanced Applications of Convolution Modelling in GLM and SPM MEEG Course 2019

Addressing difficulties in experimental design such as baseline correction, temporally overlapping neural responses, and systematic differences in response timings using a convolution GLM, similar to first-level fMRI analysis. The course focuses on the stop-signal task, EEG correlates of stopping a planned movement, parameterizing behavior, recording neural activity in MEG, isolating stopping, and applying contrasts to MEG data for studying the neural correlates of successful stop-signals.

• Convolution Modelling
• GLM
• SPM
• MEEG
• Neural Correlates

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1. Convolution modelling Advanced applications of the GLM, SPM MEEG Course 2019 Ashwani Jha, UCL

2. Outline Experimental Scenario (stop-signal task) Difficulties arising from experimental design Baseline correction Temporally overlapping neural responses Systematic differences in response timings Using a convolution GLM to deal with these problems* *just like first level fMRI

4. What is the problem were trying to address? Baseline correction Temporally overlapping neural responses Systematic differences in response timings ... an example

5. The task: stop-signal task What is the EEG correlate of stopping a planned movement ? Parameterise behaviour: stop-signal task Record neural activity: MEG Behavioural contrast of interest: Isolate stopping Apply equivalent contrast to MEG data MEG correlate of stopping

6. The task: stop-signal task GO trial STOP trial + trial n+1 + trial n < X > Go signal Go signal Stop signal SOA time time X response response Error Correct

7. The task: stop-signal task X < +

8. What is the neural correlate of a successful stop-signal? TF MEG + + + > > > + > + + > > X Correct + + + > > > X + > + + > > Error

9. What is the neural correlate of a successful stop-signal? TF MEG + + + > > > + > + + > > X Correct + + + > > > X + > + + > > Error A: Trial-based method 1) Cut into trials 2) Average response over trials 3) Compare with another trial

10. M1l M1r SMA preSMA rIFG lIFG 80 80 80 80 80 80 Frequency (Hz) 60 60 60 60 60 60 40 40 40 40 40 40 20 20 20 20 20 20 0 1 0 1 0 1 0 1 0 1 0 1 What is the neural correlate of a successful stop-signal? 80 80 M1l M1r SMA preSMA rIFG lIFG 80 80 80 80 Frequency (Hz) 60 60 60 60 60 60 40 40 40 40 40 40 TF MEG 20 20 20 20 20 20 + + + > > > Time (s) 0 1 Time (s) 0 1 Time (s) 0 1 Time (s) 0 1 Time (s) 0 1 Time (s) 0 1 + > + + > > X Correct + + + > > > X + > + + > > Error A: Trial-based method All sorts of problems: 1) Cut into trials 2) Average response over trials 3) Compare with another trial 1) Temporally overlapping neural responses 2) Where do you put the baseline? 3) Variable (absent) response timings

11. How do we address these problems? Baseline correction Temporally overlapping neural responses Systematic differences in response timings ... A convolution model?

12. Concept of convolution model TF MEG > > + + + + X X All trials + > X PST

13. Concept of convolution model TF MEG > > + + + + X X All trials + > X X PST Accounts for temporally overlapping responses and differences in response timings (beware of correlation)

14. The Convolution model (half way) + X Y

15. The Convolution model (half way) + X Y At different frequencies

16. The Convolution model (full model) * Note baseline drift

17. Example output of convolution model M1l M1r SMA preSMA rIFG lIFG M1l M1l 80 80 80 80 80 80 0.2 Frequency (Hz) GO signal 60 60 60 60 60 60 80 80 0.15 40 40 40 40 40 40 RMS amplitude (a.u.) 0.1 0.1 Frequency (Hz) Frequency (Hz) 20 20 20 20 20 20 60 60 0.05 0 1 0 1 0 1 0 1 0 1 0 1 0 0 40 40 M1l M1r SMA preSMA rIFG lIFG -0.05 80 80 80 80 80 80 -0.1 Frequency (Hz) -0.1 20 20 60 60 60 60 60 60 Button press -0.15 40 40 40 40 40 40 -0.2 0 1 0 1 20 20 20 20 20 20 Time (s) 0 1 0 1 0 1 0 1 0 1 0 1 Time (s) Time (s) Time (s) Time (s) Time (s) Time (s)

18. Heirarchical model analysis First-level convolution model Subject > SMA + M1r X M1l preSMA rIFG lIFG 80 80 80 80 80 80 Frequency (Hz) 60 60 60 60 60 60 1 40 40 40 40 40 40 20 20 20 20 20 20 0 1 0 1 0 1 0 1 0 1 0 1 M1l M1r SMA preSMA rIFG lIFG 80 80 80 80 80 80 M1l M1r SMA preSMA rIFG lIFG Frequency (Hz) 80 60 80 60 80 60 80 60 80 60 80 60 Frequency (Hz) 60 40 60 40 60 40 60 40 60 40 60 40 2 40 20 40 20 40 20 40 20 40 20 40 20 20 20 20 20 20 20 0 1 0 1 0 1 0 1 0 1 0 1 Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) 0 1 0 1 0 1 0 1 0 1 0 1 M1l M1l M1r M1r SMA SMA preSMA preSMA rIFG rIFG lIFG lIFG 80 80 80 80 80 80 80 80 80 80 80 80 Frequency (Hz) 60 60 60 60 60 60 60 60 60 60 60 60 Frequency (Hz) 40 40 40 40 40 40 40 40 40 40 40 40 3 20 20 20 20 20 20 20 20 20 20 20 20 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 0 1 1 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 M1l M1r SMA preSMA rIFG lIFG 80 80 80 80 80 80 Frequency (Hz) 60 60 60 60 60 60 40 40 40 40 40 40 20 20 20 20 20 20 0 1 0 1 0 1 0 1 0 1 0 1 Time (s) Time (s) Time (s) Time (s) Time (s) Time (s)

19. Heirarchical model analysis Take contrasts of interest to second level First-level convolution model Subject M1l M1r SMA preSMA rIFG lIFG 80 80 80 80 80 80 > SMA SMA > + M1r X Frequency (Hz) 60 60 60 60 60 60 M1l preSMA rIFG lIFG 40 40 40 40 40 40 M1l M1r preSMA rIFG lIFG 80 80 80 80 80 80 80 80 80 80 80 80 20 20 20 20 20 20 Frequency (Hz) 60 Frequency (Hz) 60 60 60 60 60 60 60 60 60 60 60 1 SMA 20 > 0 1 0 1 0 40 1 0 1 0 1 0 1 40 40 40 40 40 40 40 40 40 40 40 20 20 20 20 20 20 20 20 M1l 20 M1r 20 preSMA 20 rIFG lIFG 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 80 1 0 80 1 0 1 0 1 0 1 80 80 80 80 Frequency (Hz) M1l M1r SMA preSMA rIFG lIFG M1l M1r SMA preSMA rIFG lIFG 60 60 60 60 60 60 80 80 80 80 80 80 M1l M1r SMA preSMA rIFG M1l lIFG M1r SMA preSMA rIFG lIFG 80 80 80 80 80 80 40 40 40 40 40 40 Frequency (Hz) 80 60 Frequency (Hz) 80 60 80 60 80 60 80 60 60 80 80 60 60 80 60 80 60 80 60 80 60 80 Frequency (Hz) Frequency (Hz) 20 20 20 20 20 20 60 40 60 40 60 40 60 40 60 40 40 60 60 40 40 60 40 60 40 60 40 60 40 60 2 40 20 40 20 40 20 40 20 40 20 20 40 40 20 20 40 20 40 20 40 40 20 40 0 20 0 1 0 1 1 0 1 0 1 0 1 Time (s) 20 Time (s) 20 Time (s) 20 Time (s) 20 Time (s) Time (s) 20 20 20 20 20 20 20 20 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) 0 Time (s) Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 0 1 0 1 0 1 0 1 0 1 1 0 1 1 1 1 1 1 M1l M1l M1r M1r SMA SMA preSMA preSMA rIFG rIFG M1l M1l lIFG lIFG M1r M1r SMA SMA preSMA preSMA rIFG rIFG lIFG lIFG 80 80 80 80 80 80 80 80 80 80 Frequency (Hz) 80 80 80 80 80 80 80 80 80 80 80 80 80 80 Frequency (Hz) 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 Frequency (Hz) Frequency (Hz) 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 3 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 0 1 1 0 1 1 0 1 1 0 1 1 0 0 1 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 0 1 1 0 1 1 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 Time (s) 0 M1l M1r SMA preSMA rIFG M1l lIFG M1r SMA preSMA rIFG lIFG 80 80 80 80 80 80 80 80 80 80 80 80 Frequency (Hz) Frequency (Hz) 60 60 60 60 60 60 60 60 60 60 60 60 40 40 40 40 40 40 40 40 40 40 40 40 20 20 20 20 20 20 20 20 20 20 20 20 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) Time (s) Time (s)

20. Example results of stop-signal task Mean Succ - unsucc 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 Left M1 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 The model has accounted for: 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 M1l M1l 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 0.2 0. 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 SMA 0.15 1 1) 2) Slow drifting baseline Temporarily overlapping induced responses Systematic differences in reaction time between conditions 80 80 Frequency (Hz) 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 Frequency (Hz) Frequency (Hz) 0.1 RMS amplitude 0 0 60 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 3) 0.05 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 pre- SMA 0 0 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 40 40 -0.05 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 (a.u.) 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 -0.1 20 20 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 Right IFG -0.15 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 -0.1 -0.2 0 1 0 0 0 1 0 0 1 1 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 60 60 60 Time (s) 60 60 60 60 60 60 60 60 60 60 60 60 60 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40 Left IFG 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 20 0 0 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 Time relative to stop/change signal (s)

21. Summary Sometimes the standard trigger-based epoching approach doesn t work, especially if: No well-defined baseline period Temporally overlapping neural responses (i.e. long responses such as induced response and fMRI BOLD) Systematic differences in reaction times (probably a lot of studies!) A hierarchical convolution model is better in these circumstances (but be careful of correlated regressors in trial-design) Other advantages include the potential to model parametric regressors and continuous regressors. References: 1) Litvak V, Jha A, Flandin G, Friston K. Convolution models for induced electromagnetic responses. Neuroimage. 2013 Jan 1;64:388-98. doi: 10.1016/j.neuroimage.2012.09.014 2) Jha A, Nachev P, Barnes G, Husain M, Brown P, Litvak V. The Frontal Control of Stopping. Cereb Cortex. 2015 Nov;25(11):4392-406. doi: 10.1093/cercor/bhv027