Data and dynamic causal modelling Karl Friston Giuseppe Arcimboldo , - PowerPoint PPT Presentation

Data and dynamic causal modelling Karl Friston

Giuseppe Arcimboldo , The Vegetable Gardener (c.1590). Oil on panel. Our percepts are constrained by what we expect to see and the hypotheses that can be called upon to explain our sensory input. Arcimboldo, "a 16th century Milanese artist who was a favorite of the Viennese, illustrates this dramatically by using fruits and vegetables to create faces in his paintings.

Making sense of data = −   μ μ ( ) ( , ) Q F s = −      μ D prediction update μ  = − μ ( ) s g prediction error

Watching the brain make sense of data and fMRI data Acquire MEG data Multimodal DCM (Bayesian fusion) Statistical parametric mapping fMRI data MEG data Lamina specific (canonical DCM for electromagnetic ECD priors microcircuit) DCM for fMRI responses Neuronal priors Bayesian belief updating or evidence accumulation

The forward (dynamic causal) model causes consequences

The forward (dynamic causal) model Bayesian model inversion Posterior density ( ) causes      | , ( | ) p m q ( )     ln | ( , ) p m F Log model evidence (Free energy) consequences Richard Feynman

Dynamic causal modelling of synaptopathy Karl Friston

The forward (dynamic causal) model (2) infection Susceptible Immune Infected Infectious (1) location (3) clinical Home asymptomatic Symptom Morgue Work Deceased s (4) testing CCU ARDS Untested Negative Waiting Positive Death • Recorded death Positive • Test for coronavirus

consequences causes