Dynamic Causal Modeling (DCM)
A Practical Perspective
Ollie Hulme
Barrie Roulston
Zeki lab
Disclaimer
The following speakers have never
used DCM. Any impression of
expertise or experience is entirely
accidental.
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.
A Re-cap for Dummies
You can ask different types of questions
about brain processing.
Questions of Where
Questions of How
Functional Specialization is a question
of Where?
•Where in the brain is a
certain cognitive/perceptual
attribute processed?
•What are the Regionally
specific effects
•your normal SPM analysis (GLM)
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
2 Categories of Functional integration
analysis
Functional connectivity
= the temporal correlation between
spatially remote areas
MODEL-FREE
PPI
Effective connectivity
= the influence one area exerts over
another
MODEL-DEPENDENT
Hypothesis driven
DCM!
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.
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
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.
No
attent
Attent.
Moving
•
TR should be as short as
possible < 2 seconds
•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
Planning a DCM-compatible study
2
1
visual
input
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
•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
When attending to
motion…….
attention modulates
V5 directly
Parietal areas
+
+
V5
Hypothesis B
Attention modulates effective
connectivity between PPC to
V5
V1
1. 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
4.Evaluate whether DCM can answer your question
Can DCM distinguish between your hypotheses?
Parietal areas
V5
Direct influence
Indirect influence
V1
Pulvinar
DCM cannot distinguish between direct and indirect!
In case of
Hypotheses of this nature cannot be tested
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.
A DCM in 5 easy steps…
1. Specify the design matrix
2. Define the VOIs
3. Enter your chosen model
4. Look at the results
5. Compare models
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
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).
DCM button
‘specify’
NB: in order!
Can select:
-effects of each condition
-intrinsic connections
-contrast of connections
Output
Latent (intrinsic) connectivity (A)
Modulation of connections (B)
Photic
Motion
Attention
Input (C)
Comparing models
See what model best explains the data, e.g.
Original Model
Alternative Model
Attention
modulates V1 to
V5
Attention
modulates V5
?
DCM button
‘compare’
The read-out in MatLab indicates which model is most likely