Dynamic Causal Modelling Introduction SPM Course (fMRI), October 2015 Peter Zeidman Wellcome Trust Centre for Neuroimaging University College London

is a framework for creating, estimating and comparing generative models of neuroimaging timeseries Dynamic Causal Modelling We use these models to investigate effective connectivity of neuronal populations

Contents Overview of DCM –Effective connectivity, DCM framework, generative models Model specification –Neural model, haemodynamic model Model estimation –Model inversion, parameter inference Example

Contents Overview of DCM –Effective connectivity, DCM framework, generative models Model specification –Neural model, haemodynamic model Model estimation –Model inversion, parameter inference Example

The system of interest Stimulus from Buchel and Friston, 1997 Brain by Dierk Schaefer, Flickr, CC 2.0CC 2.0 Experimental Stimulus(Hidden) Neural ActivityObservations (BOLD) time Vector y BOLD ? off on time Vector u

Connectivity Structural Connectivity Physical connections of the brain Functional Connectivity Dependencies between BOLD observations Effectivity Connectivity Causal relationships between brain regions "Connectome" by jgmarcelino. CC 2.0 via Wikimedia Commons Figure 1, Hong et al PLOS ONE. KE Stefan, SPM Course 2011

DCM Framework Stimulus from Buchel and Friston, 1997 Figure 3 from Friston et al., Neuroimage, 2003 Brain by Dierk Schaefer, Flickr, CC 2.0CC 2.0 Experimental Stimulus (u) Observations (y) z = f(z,u,θ n ). How brain activity z changes over time y = g(z, θ h ) What we would see in the scanner, y, given the neural model? Neural Model Observation Model

DCM Framework Stimulus from Buchel and Friston, 1997 Figure 3 from Friston et al., Neuroimage, 2003 Brain by Dierk Schaefer, Flickr, CC 2.0CC 2.0 Experimental Stimulus (u) Observations (y) Neural Model Observation Model Generative model p(u,y)

DCM Framework Experimental Stimulus (u) Observations (y) Neural Model Observation Model

DCM Framework Experimental Stimulus (u) Observations (y) Neural Model Observation Model Experimental Stimulus (u) Observations (y) Neural Model Observation Model Model 1: Model 2: Model comparison: Which model best explains my observed data?

DCM Framework 1.We embody each of our hypotheses in a generative model. The generative model separates neural activity from haemodynamics 2.We perform model estimation (inversion) This identifies parameters θ = {θ n,θ h } which make the model best fit the data and the free energy (log model evidence) 3.We inspect the estimated parameters and / or we compare models to see which best explains the data.

Contents Overview of DCM –Effective connectivity, DCM framework, generative models Model specification –Neural model, haemodynamic model Model estimation –Model inversion, parameter inference Example

The Neural Model “How does brain activity, z, change over time?” Driving input u 1 V1 z1z1 a c u1u1 z1z1 z2z2 Inhibitory self-connection (Hz). Rate constant: controls rate of decay in region 1. More negative = faster decay.

The Neural Model “How does brain activity, z, change over time?” V5 V1 Driving input u 1 z1z1 z2z2 a 11 a 22 a 21 c 11 Change of activity in V1: Change of activity in V5: Self decayV1 input

The Neural Model “How does brain activity, z, change over time?” V5 V1 z1z1 z2z2 a 11 a 22 c 11 Columns are outgoing connections Rows are incoming connections Driving input u 1 a 21

The Neural Model “How does brain activity, z, change over time?” V5 V1 z1z1 z2z2 a 11 a 22 c 11 u1u1 z1z1 z2z2 Driving input u 1 a 21

“How does brain activity, z, change over time?” The Neural Model V5 V1 z1z1 z2z2 a 11 a 22 c 11 Driving input u 1 a 21 u2u2 u1u1 z1z1 z2z2 b 21 Attention u 2 Could model be used to model a main effect and interaction

The Neural Model “How does brain activity, z, change over time?” V5 V1 z1z1 z2z2 a 11 a 22 c 11 Driving input u 1 a 21 Change of activity in V1: b 21 Attention u 2 Change of activity in V5: Self decayV1 input Modulatory input

The Neural Model “How does brain activity, z, change over time?” V5V5 V5V5 V1V1 V1V1 z1z1 z2z2 a 11 a 22 c 11 Driving input u 1 a 21 b 21 Attention u 2 For m inputs: A: Structure B: Modulatory Input C: Driving Input Change in activity per region External input 2 (attention) Current activity per region All external input

DCM Framework Stimulus from Buchel and Friston, 1997 Figure 3 from Friston et al., Neuroimage, 2003 Brain by Dierk Schaefer, Flickr, CC 2.0CC 2.0 Experimental Stimulus (u) Observations (y) z = f(z,u,θ n ). How brain activity z changes over time y = g(z, θ h ) What we would see in the scanner, y, given the neural model? Neural Model Observation Model

The Haemodynamic Model

Contents Overview of DCM –Effective connectivity, DCM framework, generative models Model specification –Neural model, haemodynamic model Model estimation –Model inversion, parameter inference Example

DCM Framework Stimulus from Buchel and Friston, 1997 Figure 3 from Friston et al., Neuroimage, 2003 Brain by Dierk Schaefer, Flickr, CC 2.0CC 2.0 Experimental Stimulus (u) Observations (y) z = f(z,u,θ n ). How brain activity z changes over time y = g(z, θ h ) What we would see in the scanner, y, given the neural model? Neural Model Observation Model

Bayesian Models posterior  likelihood ∙ prior new dataprior knowledge parameter estimates

Priors Connection strength (Hz) Prior on between-region coupling N(0,1/64) Prior means stored in DCM.M.pE, covariance in DCM.M.pC

Model Estimation Posterior mean stored in DCM.Ep Posterior variance stored in DCM.Vp. Noise precision stored in DCM.Ce Free energy stored in DCM.F

Bayesian Model Reduction Option 1: Individually fit each model to the data (then inspect or compare) Option 2: Fit only the full model (model 1) then use ‘post-hoc model reduction’ (Bayesian Model Reduction) to estimate the others Model 1 Model 2 Model 3

Contents Overview of DCM –Effective connectivity, DCM framework, generative models Model specification –Neural model, haemodynamic model Model estimation –Model inversion, parameter inference Example

PREPARING DATA

Choosing Regions of Interest We generally start with SPM results

+ ROI Options 1. A sphere with given radius Positioned at the group peak Allowed to vary for each subject, within a radius of the group peak or 2. An anatomical mask

Pre-processing 1.Regress out nuisance effects (anything not specified in the ‘effects of interest f-contrast’) 2.Remove confounds such as low frequency drift 3.Summarise the ROI by performing PCA and retaining the first component st eigenvariate: test time \{seconds\} 230 voxels in VOI from mask VOI_test_mask.nii Variance: 81.66% New in SPM12: VOI_xx_eigen.nii (When using the batch only)

EXAMPLE

Reading > fixation (29 controls) Lesion (Patient AH)

1. Extracted regions of interest. Spheres placed at the peak SPM coordinates from two contrasts: A. Reading in patient > controls B. Reading in controls 2. Asked which region should receive the driving input

Seghier et al., Neuropsychologia, 2012 Key: Controls Patient Bayesian Model Averaging

Seghier et al., Neuropsychologia, 2012

TROUBLESHOOTING

spm_dcm_fmri_check(DCM)spm_dcm_explore (DCM) From Jean Daunizeau’s website

Further Reading The original DCM paperFriston et al. 2003, NeuroImage Descriptive / tutorial papers Role of General Systems TheoryStephan 2004, J Anatomy DCM: Ten simple rules for the clinicianKahan et al. 2013, NeuroImage Ten Simple Rules for DCMStephan et al. 2010, NeuroImage DCM Extensions Two-state DCMMarreiros et al. 2008, NeuroImage Non-linear DCMStephan et al. 2008, NeuroImage Stochastic DCMLi et al. 2011, NeuroImage Friston et al. 2011, NeuroImage Daunizeau et al. 2012, Front Comput Neurosci Post-hoc DCMFriston and Penny, 2011, NeuroImage Rosa and Friston, 2012, J Neuro Methods A DCM for Resting State fMRIFriston et al., 2014, NeuroImage

EXTRAS

Approximates:The log model evidence: Posterior over parameters: The log model evidence is decomposed: The difference between the true and approximate posterior Free energy (Laplace approximation) AccuracyComplexity - Variational Bayes

The Free Energy Accuracy Complexity - posterior-prior parameter means Prior precisions Occam’s factor Volume of posterior parameters Volume of prior parameters (Terms for hyperparameters not shown) Distance between prior and posterior means

DCM parameters = rate constants The coupling parameter ‘a’ thus describes the speed of the exponential change in x(t) Integration of a first-order linear differential equation gives an exponential function: Coupling parameter a is inversely proportional to the half life  of x(t):

Main effects → driving inputs Interactions → modulatory inputs FF A A my Face Valence From a factorial design: A factorial design translates easily to DCM A (fictitious!) example of a 2x2 design: Factor 1: Stimulus (face or inverted face) Factor 2: Valence (neutral or angry) Main effect of face: FFA Interaction of Stimulus x Valence: Amygdala