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Methods for Dummies 2010. Dynamic Causal Modelling for fMRI. Justin Grace Marie-Hélène Boudrias. DCM Motivation. Dynamic Causal Modelling (DCM) was born out of a simple problem: - PowerPoint PPT Presentation
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Dynamic Causal Modelling for fMRI
Justin Grace
Marie-Hélène Boudrias
Methods for Dummies 2010
Dynamic Causal Modelling (DCM) was born out of a simple problem:
• Cognitive neuroscientists want to talk about activation at the level of neuronal systems in order to hypothesize about cognitive processes
• Imaging techniques do not generate data at this level, but give output relating to non-linear correlates, such as haemodynamics (BOLD signal) in the case of fMRI
• Moreover, it would be useful to be able to talk about causality in neuronal populations, since we know that signals propagate in a wave-like manner from some input through a system
• DCM attempts to tackle these problems
DCM Motivation
DCM History
• Introduced in 2002 for fMRI data (Friston, 2002)• DCM is a generic approach for inferring hidden (unobserved)
neuronal states from measured brain activity.• The mathematical basis and implementation of DCM for fMRI
have since been refined and extended repeatedly.• DCMs have also been implemented for a range of
measurement techniques other than fMRI, including EEG, MEG (to be presented next week), and LFPs obtained from invasive recordings in humans or animals.
1. Structural connectivity – the physical structure of the brain
2. Functional connectivity – the likelihood that 2 neuronal populations share associated activity
3. Effective connectivity – a union between structural and functional connectivity.
=> DCM
Structural, functional, and effective connectivity matrices.•Binary or Non-binary – reflecting proximity between elements (presence or magnitude respectively)•Symmetrical or Non-symmetrical – reflecting direction independence, or directional effects respectively
Recap on Connectivity
Overview
• Dynamic causal models (DCMs)– Basic idea
– Neural level
– haemodynamic level
– Priors & Parameter estimation
• Rules of good practice of DCM with fMRI data
• DCM allows us to model interactions among neuronal populations at a cortical level using approximations of neural activity. Today, since we are discussing DCM using fMRI, our source data reflects the haemodynamic time series.
• Using these models we can begin to make inferences about the coupling among brain areas, & how that coupling can be manipulated by changes to the experimental context.
• This approach requires several components that build on prior knowledge about that brain & neural systems established by previous research…
Basic Idea
Components of DCM
V1
V4
BA37
STG
Perturbing inputsStimuli-bound u1(t)
{e.g. visual words}C Matrix
y
y y
y
y
The aim of DCMFunctional integration and the modulation of specific pathways
BA39
Basics of Dynamic Causal Modelling
DCM allows us to look at how areas within a network interact:
Investigate functional integration & modulation of specific cortical pathways
– Temporal dependency of activity within and between areas (causality)
Temporal dependence and causal relations
A B A B
Seed voxel approach, PPI etc. Dynamic Causal Models
timeseries (neuronal activity)
T0
T1
T2
….
T0
T1
T2
….
Input u(t)
connectivity parameters
systemstate z(t)
What is a system?
State changes of a system are dependent on:
– the current state– external inputs– its connectivity– time constants & delays
System = a set of elements which interact in a spatially and temporally specific fashion
),,( uzFdt
dz ),,( uzF
dt
dz
Linear Dynamic Model
X1= A11X1 + A21X2 + C11U1 X2= A22X2 + A12X1 + C22U2
2
1
22
11
2
1
2221
1211
2
1
0
0
U
U
C
C
AA
AA
xx
xx
The Linear Approximation
fL(x,u)=Ax + Cu
Intrinsic Connectivity Extrinsic (input) Connectivity
INPUT U1 INPUT U2
C11 C22
X1 X2A11
A12
A21
A22
INPUT U1 INPUT U2
C11 C22
X1 X2A11
A12
A21
A22
B2
21
X1= A11X1 + (A21+ B2
12U1(t))X + C11U1 X2= A22X2 + A12X1 + C22U2
2
1
22
11
2
12
122
2221
1211
2
1
0
0
00
0
U
U
C
CU
B
AA
AA
xx
xx
The Bilinear Approximation
fB(x,u)=(A+jUjBj)x + Cu
Intrinsic
Connectivity
Extrinsic (input)
ConnectivityINDUCED CONNECTIVITY
INPUT U1 INPUT U2
C11 C22
X1 X2A11
A12
A21
A22
B2
21
Bi-Linear Dynamic Model (DCM)
Neurodynamics: 2 nodes with input
u2
u1
z1
z2
activity in z2 is coupled to z1 via coefficient a21
u1
21a
001
01211
2
1
212
1
au
c
z
z
as
z
z
z1
z
2
Neurodynamics: positive modulation
000
00
1
01 2211
2
1221
22
1
212
1
bu
c
z
z
bu
z
z
as
z
z
u2
u1
z1
z2
modulatory input u2 activity through the coupling a21
u1
u2
index, not squared
z1
z
2
Neurodynamics: reciprocal connections
0,,00
00
1
1 22121121
2
1221
22
1
21
12
2
1
baau
c
z
z
bu
z
z
a
as
z
z
u2
u1
z1
z2
reciprocal connectiondisclosed by u2
u1
u2z1
z
2
This completes the neuronal model – hopefully you now have some understanding as to how the neuronal model generates output relating to neuronal activity.
we have explained how any 2 elements of interest interact with each other and the stimulus input;
How elements can be combined to talk about a neuronal system state;
And how we can identify change in this system over time;
In order to discuss intrinsic and induced connectivity with respect to extrinsic stimulus effects.
Neuronal level summary
Basics of Dynamic Causal Modelling
DCM allows us to look at how areas within a network interact:
Investigate functional integration & modulation of specific cortical pathways
– Temporal dependency of activity within and between areas (causality)
– Separate neuronal activity from observed BOLD responses
• Cognitive system is modelled at its underlying neuronal level (not directly accessible for fMRI).
• The modelled neuronal dynamics (x) are transformed into area-specific BOLD
signals (y) by a haemodynamic model (λ). λ
x
y
The aim of DCM is to estimate parameters at the neuronal level such that the modelled and measured BOLD signals are maximally* similar.
Basics of DCM: Neuronal and BOLD level
},,,,,{ h},,,,,{ h
important for model fitting, but of no interest for statistical inference
,)(
signal BOLD
qvty
The haemodynamic model
)(
activity
tx
• 6 haemodynamic parameters:
• Computed separately for each area region-specific HRFs!
sf
tionflow induc
(rCBF)
s
v
v
q q/vvEf,EEfqτ /α
dHbchanges in
100 )( /αvfvτ
volumechanges in
1
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fγsxs
signalryvasodilato
s
f
Friston et al. 2000, NeuroImageStephan et al. 2007, NeuroImage
stimulus functionsut
neural state equation
haemodynamic state equations
Estimated BOLD response
0 20 40 60
0
2
4
0 20 40 60
0
2
4
seconds
Haemodynamics: reciprocal connections
blue: neuronal activity
red:
BOLD response
h1
h2
u1
u2z1
z
2
h(u,θ) represents the BOLD response (balloon model) to input
BOLD(without noise)
BOLD(without noise)
0 20 40 60
0
2
4
0 20 40 60
0
2
4
seconds
Haemodynamics: reciprocal connections
BOLDwith Noise added
BOLDwith Noise added
y1
y2blue: neuronal activity
red: BOLD response
u1
u2z1
z
2
euhy ),(
y represents simulated observation of BOLD response, i.e. includes noise
So we now have models of BOLD signal output based on our predicted neuronal model.
Marie-Hélène will describe the process of setting these models with appropriate priors and using parameter estimation to identify the most appropriate model.
Overview
• Dynamic causal models (DCMs)– Basic idea
– Neural level
– Hemodynamic level
– Parameter estimation, priors & inference
• Rules of good practice of DCM with fMRI data
DCM roadmap
fMRI data
Posterior densities of parameters
Neuronal dynamics
Haemodynamics
Model comparison
Bayesian Model inversion
State space Model
Priors
Dynamic Causal Modelling of fMRIDynamic Causal Modelling of fMRI
sf (rCBF)induction -flow
s
v
f
stimulus function u
modeled BOLD response
vq q/vvf,Efqτ /α1)(
dHbin changes
/αvfvτ 1
in volume changes
f
q
)1(
signalry vasodilatodependent -activity
fγszs
s
( , , )h x u
Overview:parameter estimation
ηθ|y
neuronal stateequation CuzBuAz j
j )(• Specify model (neuronal and
haemodynamic level).
• Bayesian parameter estimation to minimise difference between data and model.
• Result:Gaussian a posteriori coupling parameter distributions, characterised by mean ηθ|y and covariance Cθ|y.
parameters
},{
},...,{
},,,,{1
nh
mn
h
CBBA
hemodynamic state equations
Measured vs Modelled BOLD signal
The aim of DCM is to estimate- neural parameters {A, B, C}- hemodynamic parameters such that the modelled and measured BOLD signals are maximally similar.
Constraints on•Haemodynamic parameters•Connections (coupling parameters)
Models of•Combined haemodynamics andneural parameter set
Bayesian estimation
)(p
)()|()|( pypyp
)|( yp
posterior
priorlikelihood term
Estimation: Bayesian framework
The posterior probability of the parameters given the data is an optimal combination of prior knowledge and new data, weighted by their relative precision.
},...,{
},,,,{1 CBBA mn
h
• The model parameters are distributions that have a mean ηθ|y and covariance Cθ|y .
• Quantify the probability that a parameter (ηθ|y ) is above a chosen threshold γ:
ηθ|y
Interpretation of parameters
• By default, γ is chosen as zero ("does this coupling parameter
exist?").
• Model evidence involves integrating out the dependency of the model parameters:
• BMS is an established procedure in statistics that rests on computing the model evidence i.e., the probability of the data y, given some model m.
• The model evidence, which can be considered the “holy grail” of model comparison, quantifies the properties of a good model.
• It explains the data as accurately as possible and, at the same time, has minimal complexity.
Bayesian Model Selection (BMS)
dmpmypmyp )|(),|()|(
)|( myp
Given competing hypotheses on structure & functional mechanisms of a system, which model is the best?
For which model m does p(y|m) become maximal?
Which model represents thebest balance between model fit and model complexity?
Model comparison
• In BMS, models are usually compared via their Bayes factor,i.e., the ratio of their respective evidences:
• The “Bayes factor” is a summary of the evidence in favour of one model as opposed to another.
• i.e. Given candidate models m1 and m2, a Bayes factor of 20 corresponds to a belief of 95% in the statement ‘m1 is better than m2’.
Bayesian Model Selection (BMS)
)|(
)|(
2
112 myp
mypB
B12 p(m1|y) Evidence
1 to 3 50-75% weak
3 to 20 75-95% positive
20 to 150 95-99% strong
150 99% Very strong
Overview• Dynamic causal models (DCMs)
– Basic idea
– Neural level
– Hemodynamic level
– Parameter estimation, priors & inference
• Rules of good practice of DCM with fMRI data
Rules of good practice
• DCM is dependent on experimental perturbations
– Experimental conditions enter the model as inputs that either drive the local responses or change connections strengths.
– If there is no evidence for an experimental effect (no activation detected by a GLM) → inclusion of this region in a DCM is not meaningful.
• Use the same optimization strategies for design and data acquisition that apply to conventional GLM of brain activity:
– preferably multi-factorial (e.g. 2 x 2)– one factor that varies the driving (sensory) input – one factor that varies the contextual input
Define the relevant model space
• Define sets of models that are plausible, given prior knowledge about the system, this could be e.g.:• derived from principled considerations • informed by previous empirical studies using neuroimaging, electrophysiology, TMS,
etc. in humans or animals.
• Use anatomical information and computational models to refine your DCMs.
• The definition of the relevant model space should be as transparent and systematic as possible, and it should be described clearly in any article.
Motivate model space carefully
• Models are never true; by construction, they are meant to be helpful caricatures of complex phenomena, such that mechanisms underlying these phenomena can be tested.
• The purpose of model selection is to determine which model, from a set of plausible alternatives, is most useful i.e., represents the best balance between accuracy and complexity.
• The critical question in practice is how many plausible model alternatives exist?
– For small systems (i.e., networks with a small number of nodes), it is possible to investigate all possible connectivity architectures.
– With increasing number of regions and inputs, evaluating all possible models becomes practically impossible very rapidly.
What you can not do with BMS
• Model evidence is defined with respect to one particular data set. This means that BMS cannot be applied to models that are fitted to different data.
• Specifically, in DCM for fMRI, one cannot compare models with different numbers of regions, because changing the regions changes the data.
• Maximum of 8 regions with SPM8.
Fig. 1. This schematic summarizes the typical sequence of analysis in DCM, depending on the question of interest. Abbreviations: FFX=fixed effects, RFX=random effects, BMS=Bayesian model selection, BPA=Bayesian parameter averaging, BMA=Bayesian model averaging, ANOVA=analysis of variance. 10 Simple Rules for DCM (2010). Stephan et al. NeuroImage 52.
V1
V4
BA37
STG
BA39Perturbing inputs
C matrix
y
y y
y
y
Fig. 1. This schematic summarizes the typical sequence of analysis in DCM, depending on the question of interest. Abbreviations: FFX=fixed effects, RFX=random effects, BMS=Bayesian model selection, BPA=Bayesian parameter averaging, BMA=Bayesian model averaging, ANOVA=analysis of variance. 10 Simple Rules for DCM (2010). Stephan et al. NeuroImage 52.
V1
V4
BA37
STG
BA39
Contextual inputsB matrix
Perturbing inputsC matrix
y
y y
y
y
Practical steps of DCM
1) Standard Analysis of fMRI Data
2) Statistical Parametric Maps- Extract times series from chosen areas
3) Construction of a Connectivity Model- Add a forward model of how neuronal activity causes the signals you observe (e.g. BOLD) (Neural and hemodynanics models)
4) Evaluation of the Connectivity Model- Estimation of the parameters in your model (effective connectivity), given your observed data
5) BMS, BMA or BPA
Design matrix
SPMs
SMA
PMd
M1
PMv
M1
SMA
SMA
PMd
M1
PMv
M1
SMA A Matrix = rate constant in 1/s or Hz- Coupling represents the connection strength describing how fast and strong a response occur in the target region.
- If M1_LPMd_L is 0.36 s-1 this means that, per unit time, the increase in activity in PMd_L corresponds to 36% of the activity in M1_L.
B Matrix = Used to calculate the % of change of connection strength in the target region by the factor that varies the contextual input.
C Matrix = Entrance of the driving (sensory) input in the network
%836.0
03.0
Ay
By
Left Right
ηθ|y0
SUBJECT: VH Age:19 Model: Mod_43 Specify intrinsic connections from M1_L M1_R SMA_L SMA_R PMd_L PMd_R PMv_L PMv_R
to
M1_L -1.00 0.00 0.01 0.01 0.00 0.01 0.02 0.02M1_R -0.01 -1.00 -0.07 -0.04 -0.09 0.02 -0.04 -0.05
SMA_L 0.23 -0.01 -1.00 0.04 0.08 0.00 0.05 0.07SMA_R 0.13 0.00 0.03 -1.00 0.04 0.01 0.02 0.04PMd_L 0.36 -0.01 0.07 0.05 -1.00 0.00 0.06 0.09PMd_R 0.02 0.01 -0.07 -0.04 -0.11 -1.00 -0.04 -0.06PMv_L 0.13 -0.01 0.02 0.02 0.02 0.00 -1.00 0.03PMv_R 0.22 0.00 0.02 0.02 0.01 0.01 0.02 -1.00
Effects of
GripxForce^1 from
M1_L M1_R SMA_L SMA_R PMd_L PMd_R PMv_L PMv_R
to
M1_L 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00M1_R 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
SMA_L 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00SMA_R 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00PMd_L 0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00PMd_R 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00PMv_L -0.03 0.00 0.00 0.00 0.00 0.00 0.00 0.00PMv_R 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Effects of Grip
to
M1_L 1.643M1_R 0.000
SMA_L -0.093SMA_R 0.000PMd_L -0.302PMd_R 0.000PMv_L 0.066 .PMv_R 0.000
So, DCM….
• enables one to infer hidden neuronal processes from fMRI data
• tries to model the same phenomena as a GLM
– explaining experimentally controlled variance in local responses
– based on connectivity and its modulation
• allows one to test mechanistic hypotheses about observed effects
• is informed by anatomical and physiological principles.
• uses a Bayesian framework to estimate model parameters
• is a generic approach to modelling experimentally perturbed dynamic
systems.
– provides an observation model for neuroimaging data, e.g. fMRI, M/EEG
Some useful references
• The first DCM paper: Dynamic Causal Modelling (2003). Friston et al. NeuroImage
19:1273-1302.
• Physiological validation of DCM for fMRI: Identifying neural drivers with functional MRI:
an electrophysiological validation (2008). David et al. PLoS Biol. 6 2683–2697
• Hemodynamic model: Comparing hemodynamic models with DCM (2007). Stephan et
al. NeuroImage 38:387-401
• Nonlinear DCMs:Nonlinear Dynamic Causal Models for FMRI (2008). Stephan et al.
NeuroImage 42:649-662
• Two-state model: Dynamic causal modelling for fMRI: A two-state model (2008).
Marreiros et al. NeuroImage 39:269-278
• Group Bayesian model comparison: Bayesian model selection for group studies (2009).
Stephan et al. NeuroImage 46:1004-10174
• 10 Simple Rules for DCM (2010). Stephan et al. NeuroImage 52.
• Dynamic Causal Modelling: a critical review of the biophysical and statistical
foundations. Daunizeau et al. Neuroimage (2010), in press
• SPM Manual, SMP courses slides, last years presentations.
• THANKS Andre Marreiros!