Dynamic Causal Modeling (DCM ) A Practical Perspective

1. Dynamic Causal Modeling (DCM) A Practical Perspective Ollie Hulme Barrie Roulston Zeki lab

2. Disclaimer The following speakers have never used DCM. Any impression of expertise or experience is entirely accidental.

3. Structure • 1. Quick recap on what DCM can do for you. • 2. What to think about when designing a DCM experiment • 3. How to do DCM. What buttons to press etc.

4. A Re-cap for Dummies You can ask different types of questions about brain processing. Questions of Where Questions of How

5. Functional Specialization is a question of Where? • Wherein the brain is a certain cognitive/perceptual attribute processed? • What are the Regionally specific effects • your normal SPM analysis (GLM)

6. Functional Integration is a question of HOW How does the system work? What are the inter-regional effects? How do the components of the system interact with each other? Experimentally designed input

7. 2 Categories of Functional integration analysis Functional connectivity = the temporal correlation between spatially remote areas Effective connectivity = the influence one area exerts over another MODEL-FREE MODEL-DEPENDENT Hypothesis driven PPI DCM!

8. DCM overview • DCM allows you model brain activity at the neuronal level (which is not directly accessible in fMRI) taking into account the anatomical architecture of the system and the interactions within that architecture under different conditions of stimulus input and context. • The modelled neuronal dynamics (z) are transformed into area-specific BOLD signals (y) by a hemodynamic forward model (λ). The aim of DCM is to estimate parameters at the neuronal level so that the modelled BOLD signals are most similar to the experimentally measured BOLD signals.

9. Static Moving No attent Attent. Planning a DCM-compatible study • Experimental design: • preferably multi-factorial (e.g. at least 2 x 2) • 1.Sensory input factor • At least one factor that varies the sensory input… changing the stimulus… a perturbation • to the system 2. Contextual factor At least one factor that varies the context in which the perturbation occurs. Often attentional factor, or change in cognitive set etc.

10. Planning a DCM-compatible study • TR should be as short as possible < 2 seconds 2 • Timing problems in DCM: • Due to the sequential acquisition of multiple slices there will be temporal shifts between regional time series which lie in different slices. This causes timing misspecification. At short TR’s this is not too much of a problem since the information in the response variable is predominantly contained in the relative amplitudes and shapes of hemodynamic response rather than their timings. Consequently DCM is robust against timing errors up to 1 second slice acquisition 1 visualinput • Possible corrections for longer TR’s 1. slice-timing 2. Restrict model to proximate regions. The closer they are along z axis the lower the temporal discrepancy

11. Hypothesis and model: • define specific a priori hypotheses…. • DCM is not exploratory! Specify your hypotheses as precisely as possible. This requires neurobiological expertise (the fun part)… read lots of papers! Look for convergent evidence from multiple methodologies and disciplines. Anatomy is your friend.

12. Defining your hypothesis Hypothesis A attention modulates V5 directly When attending to motion……. + Parietal areas + V5 Hypothesis B Attention modulates effective connectivity between PPC to V5 V1

13. Which parameters do you think are most relevant? • Which parameters represent my hypothesis? • Which are the most relevant intrinsic anatomical • Connections? • Which are the most relevant changes in effective • connectivity/connection strength ? • Which are the relevant sensory inputs ? • 2.Defining criteria for inference: • single-subject analysis: • What statistical threshold? What contrasts? • group analysis: Which 2nd-level model? • Paired t-test for parameter a> parameter b, • One-sample t-test: parameter a > 0 • rmANOVA (in case of multiple sessions per subject) • 3.Ensure that the model you generate is able to test your • hypotheses • The model should incorporate every component of the hypothesis

14. Indirect influence Pulvinar • 4.Evaluate whether DCM can answer your question • Can DCM distinguish between your hypotheses? Parietal areas V5 Direct influence V1 DCM cannot distinguish between direct and indirect! Hypotheses of this nature cannot be tested In case of

15. 1.Specify your main hypothesis and its competing hypotheses as precisely as possible using convergent evidence from the empirical and theoretical literature 2.Think specifically about how your experiment will test the hypothesis and whether the hypothesis is suitable for DCM to test. 3.Klaas emphasises that you should ‘Test your model before conducting the experiment using synthetic data. Simulation is the key!’ 4. DCM is tricky, ask the experts during the design stage. They are very helpful.

16. A DCM in 5 easy steps… • Specify the design matrix • Define the VOIs • Enter your chosen model • Look at the results • Compare models

17. Specify design matrix • Normal SPM regressors • -no motion, no attention • -motion, no attention • -no motion, attention • -motion, attention • DCM analysis regressors • -no motion (photic) • -motion • -attention

18. Defining VOIs • Single subject: choose co-ordinates from appropriate contrast. • e.g. V5 from motion vs. no motion • RFX: DCM performed at 1st level, but define group maximum for area of interest, then in single subject find nearest local maximum to this using the same contrast and a liberal threshold (e.g. P<0.05, uncorrected).

19. DCM button ‘specify’ NB: in order!

20. Can select: • effects of each condition • intrinsic connections • contrast of connections

21. Output Latent (intrinsic) connectivity (A)

22. Photic Motion Attention Modulation of connections (B)

23. Input (C)

24. Comparing models See what model best explains the data, e.g. Original Model Attention modulates V1 to V5 Alternative Model Attention modulates V5 ?

25. DCM button ‘compare’ The read-out in MatLab indicates which model is most likely