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DCM a practical perspective

This guide provides an overview of Dynamic Causal Modelling (DCM), including theory, experiment design, and practical examples. Learn how DCM can be used to model brain activity and investigate effective connectivity.

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DCM a practical perspective

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  1. DCM a practical perspective For Dummies Stefania Benetti and Vladimir Litvak

  2. Outline • Theoryrevision: What can DCM do for you? • Designing a DCM experiment: What to keep in mind? • How to do DCM: Practical example

  3. Outline • Theoryrevision: What can DCM do for you? • Designing a DCM experiment: What to keep in mind? • How to do DCM: Practical example

  4. Principles of organisation Functional specialization Functional intregration HOW?? WHERE??

  5. Effective Connectivity Studies of effective connectivity investigate the influence that one brain region exerts over another and how this varies with the experimental context Auto-regressive (AR) models Volterra Kernels Structural Equation Modelling Dynamic Causal Modelling

  6. 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.

  7. Neurodynamics: 2 nodes with input u1 u2 z1 z2 activity in is coupled to via coefficient

  8. Neurodynamics: positive modulation u1 u2 z1 z2 modulatory input u2 activity through the coupling

  9. Neurodynamics: reciprocal connections u1 u2 z1 z2 reciprocal connection disclosed by u2

  10. Haemodynamics: reciprocal connections a11 Simulated response Bold Response a12 Bold Response a22 green: neuronal activity red:bold response

  11. Haemodynamics: reciprocal connections a11 Simulated response Bold Response Noise added a12 Bold Response Noise added a22 green: neuronal activity red:bold response

  12. What DCM can tell you... 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. Outline • Theoryrevision: What can DCM do for you? • Designing a DCM experiment: What to keep in mind? • How to do DCM: Practical example

  14. 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.

  15. Defining the hypothesis & the model DCM is not exploratory!! Specify your hypotheses as precisely as possible. This requires neurobiological expertise (read lots of papers!). Look for convergent evidence from multiple methodologies and disciplines. • Structure: which areas, connections and inputs? • Which parameters represent my hypothesis? • What are the alternative models to test? DCM is tricky! Ask the experts during the design stage

  16. Outline • Theoryrevision: What can DCM do for you? • Designing a DCM experiment: What to keep in mind? • How to do DCM: Thepractical side

  17. Attention to motion in the visual system • Stimuli 250 radially moving dots at 4.7 degrees/s • Scanning (no speed changes) • 6 normal subjects, 4 x 100 scan sessions; • each session comprising 10 scans of 4 different conditions. • F A F N F A F N S ................. • F - fixation point only • A - motion stimuli with attention (detect changes) • N - motion stimuli without attention • S - no motion Attention vs. No attention Büchel & Friston 1997, Cereb. Cortex Büchel et al.1998, Brain

  18. PPC V1 PFC V5 A DCM of visual system V1 is driven by any kind of visual stimulation (direct input “photic”) Motion-related responses in V5 explained through an increase in the influence of V1  V5 whenever the stimuli are moving (modulation by “motion”) PFC  PPC and PPCV5 is enhanced by attention attention Motion Photic

  19. static moving No attent Attent. Specify design matrix & SPM analysis Experimental design DCM analysis regressors • -Vision (photic) • -motion • -attention • Sensory input factor Photic Motion Attention Contextual factor No motion/ no attention Motion / no attention Motion / attention No motion/ attention Normal SPM regressors

  20. Extraction of time series (VOIs definition) 1.DCM for a single subject analysis (i.e. no 2nd-level analysis intended): determine representative co-ordinates for each brain region from the appropriate contrast (e.g. V1 from “pothic” contrast ) • Subject specific DCM, but results will eventually be entered into a 2nd-level analysis: determine group maximum for the area of interest (e.g. from RFX analysis) in the appropriate contrast in each subject, jump to local maximum nearest to the group maximum, using the same contrast and a liberal threshold (p<0.05, uncorrected)

  21. VOIs definition: V5 Contrast Name Co-ordinates

  22. Definition of DCM name DCM button In order! In Order!! In Order!!

  23. DCM parameters estimation over to Vladimir...

  24. Look at the results

  25. Look at the results Latent (intrinsic) connectivity (A) V1 V5 PPC PFC V1 V5 PPC PFC

  26. Look at the results Modulation of connections (B) V1 V5 PPC PFC

  27. Look at the results Input (C) V1 V5 PPC

  28. Contrasts on parameters

  29. Model outputs

  30. Model averaging

  31. PPC V1 PFC V5 An alternative DCM of visual system V1 is driven by any kind of visual stimulation (direct input “photic”) Motion-related responses in V5 explained through an increase in the influence of V1  V5 whenever the stimuli are moving (modulation by “motion”) V1  V5 forward connection is modulated by attention (as opposed to PPCV5 connection in the previous model) attention Motion Photic

  32. Models comparison and selection Model 2 better than model 1

  33. Models selection Bayes Information Criterion (BIC) and Akaike’s Information Criterion (AIC) BIC is biased towards simple models AIC is biased towards complex ones For any pairs of models a model is selected only if AIC and BIC are in agreement. Interpretation of BF (Raftery, 1995) 1 to 3: Weak 3 to 20: Positive 20 to 100: Strong >100: Very Strong Penny et al., 2004. Neuroimage

  34. Models comparison and selection Consistent evidence in favour of model 2 Bayes Factor >=4587

  35. SPC PPC V1 V1 V5 V5 Comparison of three simple models Photic Model 3:attentional modulationof V1→V5 and SPC→V5 Model 1:attentional modulationof PPC→V5 Model 2:attentional modulationof V1→V5 PPC 0.85 0.70 0.84 1.36 V1 Photic Attention Attention -0.02 Photic 0.57 V5 0.55 0.03 0.86 0.85 0.75 0.70 Motion 0.23 1.42 1.36 0.89 0.85 Attention -0.02 0.56 0.57 -0.02 Motion Motion 0.23 Attention What’s wrong with this example?

  36. A DCM in 6 easy(?) steps… • Specify design matrix & conventional SPM analysis • Extraction of time series (VOIs definition) • Definition of DCM • DCM parameters estimation • Look at the result • Model comparison and selection

  37. Thanks to Klaas E. Stephan & previous (former!) dummies

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