Bayesian selection of dynamic causal models for fMRI Will Penny Olivier David, Karl Friston, Lee Harrison, Andrea Mechelli, Klaas Stephan The brain as.

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Presentation transcript:

Bayesian selection of dynamic causal models for fMRI Will Penny Olivier David, Karl Friston, Lee Harrison, Andrea Mechelli, Klaas Stephan The brain as a dynamical system ? Bridging the gap between models and data, HBM Workshop, Budapest, Hungary, June V1 V5 SPC V1 V5 SPC Wellcome Department of Imaging Neuroscience, ION, UCL, UK.

Single region u2u2 u1u1 z1z1 z2z2 z1z1 u1u1 a 11 c

Multiple regions u2u2 u1u1 z1z1 z2z2 z1z1 z2z2 u1u1 a 11 a 22 c a 21

Modulatory inputs u2u2 u1u1 z1z1 z2z2 u2u2 z1z1 z2z2 u1u1 a 11 a 22 c a 21 b 21

Reciprocal connections u2u2 u1u1 z1z1 z2z2 u2u2 z1z1 z2z2 u1u1 a 11 a 22 c a 12 a 21 b 21

DCM for fMRI Neurodynamics: Inputs Change in Neuronal Activity Neuronal Activity Intrinsic Connectivity Matrix Modulatory Connectivity Matrices Input Connectivity Matrix V1 V5 SPC

Hemodynamics Hemodynamic variables For each region: Hemodynamic parameters Seconds Dynamics

Model Comparison I V1 V5 SPC Model, m Parameters: Prior Posterior Likelihood Evidence Laplace, AIC, BIC approximations Model fit + complexity

Model Comparison II V1 V5 SPC Model, m Parameters: Prior Posterior Likelihood Prior Posterior Evidence Parameter Model

Model Comparison III V1 V5 SPC Model, m=i V1 V5 SPC Model, m=j Model Evidences: Bayes factor: 1 to 3: Weak 3 to 20: Positive 20 to 100: Strong >100: Very Strong

Attention to Visual Motion STIMULI 250 radially moving dots at 4.7 degrees/s PRE-SCANNING 5 x 30s trials with 5 speed changes (reducing to 1%) Task - detect change in radial velocity SCANNING (no speed changes) 6 normal subjects, scan sessions; each session comprising 10 scans of 4 different condition 1.Photic 2.Motion 3.Attention Experimental Factors Buchel et al. 1997

Specify regions of interest Identify regions of Interest eg. V1, V5, SPC GLM analysis V1 V5 SPC Motion Photic Att Model 1

V1 V5 SPC Motion Photic Att Model 1 V1 V5 Estimation SPC Time (seconds)

V1 V5 SPC Motion Photic Att Model 1 Motion Photic Att V1 V5 SPC Model 2 Bayes Factor B 12 > Very Strong

V1 V5 SPC Motion Photic Att Model 1 V1 V5 SPC Motion Photic Att Model 3 Bayes Factor B 13 =3.6 Positive

V1 V5 SPC Motion Photic Att Model 1 Motion Photic Att V1 V5 SPC Model 4 Bayes Factor B 14 =2.8 Weak

Further Applications FG left FG right LG left LG right RVF LVF LD|LVF LD LD: Letter decision LVF: left visual field FG: 1. Klaas Stephan et al. HBM 04 – Poster TH154, Thurs 1pm Dominant right->left modulation during letter tasks LG and FG important for hemispheric integration (not MOG) 2. Olivier David et al. BIOMAG 04 – DCM for ERPs Fusiform gyrus LG: Lingual gyrus