1. DCM - the practical bits
Manuel Carreiras and Helmut Laufs
Thanks to previous [former] dummies, Andrea Mechelli, Stefan Kiebel and Lee Harrison, Klaas E. Stephan

2. Structure 1. Quick recap on what DCM can do for you.
2. What to keep in mind when designing a DCM analysis
3. How to do DCM. What buttons to press etc.

3. Functional Specialization & Functional Integration
The organization of the primate brain is based upon two complementary principles:
1) Functional Specialization (each area performs unique operations – Joseph Gall, 1810)
2) Functional Integration (functions are emergent properties of interacting brain areas – Pierre Flourens, 1823)
Until recently, neuropsychological and functional imaging
studies have focused on functional specialization…

4. Functional & Effective Connectivity
Studies of functional connectivity investigate the temporal
correlations between neuronal activity in different areas
Inferior
Frontal
Inferior
Temporal
Studies of effective connectivity investigate the influence that one brain region exerts over another and how this varies with the experimental context
Inferior
Frontal
Inferior
Temporal
Inferior
Frontal
Inferior
Temporal

5. Functional ConnectivityM D
INPUT

6. Effective Connectivity
(on a region) M D
INPUT

7. Effective Connectivity
(on a region) M D
INPUT

8. Effective Connectivity
(on a region) M D
INPUT

9. Effective Connectivity
(on a region) M D
INPUT

10. Effective Connectivity
(on a region and a connection) M D
INPUT

11. Effective Connectivity
(on a connection only) M D
INPUT

12. Methods for the study of Functional & Effective Connectivity
Correlation Analysis
Psychophysiological Interaction (PPI)
Effective:
Auto-regressive (AR) models
Volterra Kernels
Structural Equation Modelling
Dynamic Causal Modelling

13. What to keep in mind if you want to do a DCM analysis
Multifactorial design ( ... is optimal)
at least 1 factor for stimulus input
e.g. Static vs moving
at least 1 factor for contextual input
e.g. attentional set

14. 2. Defined model to test 3. TR < 2 sec
DCM is not an exploratory technique!
Model dependent. Hypothesis driven
3. TR < 2 sec
1st inaccuracy
2nd inaccuracy

15. 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
Static
Moving
No attent
Attent.
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.

16. define specific a priori hypotheses…. DCM is not exploratory!
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.

17. 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

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

19. 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. DCM is tricky, ask the experts during the design stage. They are very helpful.

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

21. Specify design matrix Normal SPM regressors -no motion, no attention
-no motion, attention
-motion, attention
DCM analysis regressors (main effects)
-no motion (photic)
-motion
-attention

22. 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).

23. PPC
PFC

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

25. Can select:
Effects of each condition
Intrinsic connections
Contrast of connections

26. Bilinear state equation in DCM
state changes
intrinsic
connectivity
modulation of
connectivity
system state
direct
inputs
m external inputs

27. Output
Latent (intrinsic) connectivity (A)

28. Modulation of connections (B)
Photic
Attention
Motion

29. Input (C)

30. ? Comparing models See what model best explains the data, e.g.
Original Model
Attention modulates V1 to V5
Alternative Model
Attention modulates V5 ?
Penny WD, Stephan KE, Mechelli A, Friston KJ. Comparing dynamic causal models. Neuroimage Jul;22(3):

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

32. PRACTICAL EXERCISE state changes intrinsic connectivity
PPC
PFC
state changes
intrinsic
connectivity
m external inputs
system state
direct
inputs
modulation of