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

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.

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…

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

Functional Connectivity M D INPUT

Effective Connectivity (on a region) M D INPUT

Effective Connectivity (on a region) M D INPUT

Effective Connectivity (on a region) M D INPUT

Effective Connectivity (on a region) M D INPUT

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

Effective Connectivity (on a connection only) M D INPUT

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

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

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

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.

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.

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

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

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.

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

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

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).

PPC PFC

DCM button ‘specify’ NB: in order!

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

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

Output Latent (intrinsic) connectivity (A)

Modulation of connections (B) Photic Attention Motion

Input (C)

? 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. 2004 Jul;22(3):1157-72.

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

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