2. Abstract We will use schizophrenia as a case study of computational psychiatry. We first review the basic phenomenology and pathophysiological theories of schizophrenia. These motivate the choice of a formal or computational framework within which to understand the symptoms and signs of schizophrenia. This framework is the Bayesian brain or Bayesian decision theory. We will focus on the encoding of uncertainty or precision within predictive coding implementations of the Bayesian brain to demonstrate how computational approaches can disclose the nature of hallucinations and delusions. The computational anatomy of psychosis Karl Friston Computational Psychiatry Course - 29th-30th April 2015 Venue: Basement Lecture Theatre, 33 Queen Square, London, WC1N 3BG

3. The symptoms and signs of schizophrenia Delusions False beliefs Delusional systems Hallucinations False percepts Thought disorder Listening of associations Disintegration of the psyche Psychomotor property Cognitive deficits Soft neurological signs Abnormal eye movements Abnormal mismatch negativity Bleuler Dysmorphophobia Delusional mood Depersonalisation Compulsions Intrusive thoughts Obsessional beliefs Affective symptoms Dissociation syndromes Capgras syndrome Functional medical syndromes Anxiety Persecutory beliefs … Aberrant beliefs and false inference

4. Pathophysiological and aetiological theories Dopamine hypothesis Abnormal plasticity Aberrant salience Glutamate hypothesis NMDA receptor dysfunction Aberrant synchrony GABAergic hypothesis Aberrant gain control Abnormal E-I balance Genetic Neurodevelopmental Psychotomimetic drugs Psychosocial Autoimmune Bleuler Aberrant neuromodulation and synaptic gain control

5. Bleuler E. Dementia Praecox oder Gruppe der Schizophrenien, 1911: Disintegration – of conscious processing (the psyche) Wernicke C. Grundrisse der Psychiatrie. 1906: Sejunction – disruption of associative connectivity Anatomical disconnectionFunctional dysconnection Dysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull. 2009 May;35(3):509-27. Klaas E. Stephan, Karl J. Friston and Chris D. Frith Aberrant neuromodulation and synaptic gain control

6. Which computational (formal) framework? Reinforcement learning, optimal control and expected utility theory Information theory and minimum redundancy Self-organisation, synergetics and allostasis Bayesian brain, Bayesian decision theory and predictive coding Pavlov Haken Helmholtz Barlow

7. Which computational (formal) framework? Reinforcement learning, optimal control and expected utility theory Information theory and minimum redundancy Self-organisation, synergetics and allostasis Bayesian brain, Bayesian decision theory and predictive coding Pavlov Haken Helmholtz Barlow

8. Active inference, predictive coding and precision Precision and false inference Simulations of : Auditory perception (and omission related responses) Smooth pursuit eye movements (under occlusion) Sensory attenuation (and the force matching illusion)

9. “Objects are always imagined as being present in the field of vision as would have to be there in order to produce the same impression on the nervous mechanism” - von Helmholtz Thomas Bayes Geoffrey Hinton Richard Feynman From the Helmholtz machine to the Bayesian brain Richard Gregory Hermann von Helmholtz

10. Bayesian filtering and predictive coding prediction update prediction error

11. Making our own sensations Changing sensations sensations – predictions Prediction error Changing predictions Action Perception

12. Generative models Hidden states Action Control states Continuous states Discrete states Bayesian filtering (predictive coding) Variational Bayes (belief updating)

13. the Descending predictions Descending predictions Ascending prediction errors whatwhere Sensory fluctuations Hierarchical generative models

14. frontal eye fields geniculate visual cortex retinal input pons oculomotor signals Prediction error (superficial pyramidal cells) Conditional predictions (deep pyramidal cells) Top-down or backward predictions Bottom-up or forward prediction error proprioceptive input reflex arc Perception VTA David Mumford Predictive coding with reflexes Action Precision

15. Bayesian belief updating VTA/SN Prefrontal Cortex Motor Cortex Inferotemporal Cortex Striatum Condition stimulus (CS) Unconditioned stimulus (US) Perception Action selection Incentive salience

16. Active inference, predictive coding and precision Precision and false inference Simulations of : Auditory perception (and omission related responses) Smooth pursuit eye movements (under occlusion) Sensory attenuation (and the force matching illusion)

17. + - De-compensation (trait abnormalities) Compensation (to psychotic state) Neuromodulatory failure (of sensory attenuation) Attenuated violation responses Loss of perceptual Gestalt SPEM abnormalities Psychomotor poverty Resistance to illusions Hallucinations Delusions

18. Active inference, predictive coding and precision Precision and false inference Simulations of : Auditory perception (and omission related responses) Smooth pursuit eye movements (under occlusion) Sensory attenuation (and the force matching illusion)

19. Generative model Syrinx Neuronal hierarchy Time (sec) Frequency (KHz) sonogram 0.511.5 Frequency (Hz) percept prediction error Predictive coding 500100015002000 -6 -4 -2 0 2 4 6 8 10 peristimulus time (ms) LFP ( micro-volts )

20. Reduced prior precision Compensatory attenuation of sensory precision Omission related responses, MMN and hallucinosis

21. Active inference, predictive coding and precision Precision and false inference Simulations of : Auditory perception (and omission related responses) Smooth pursuit eye movements (under occlusion) Sensory attenuation (and the force matching illusion)

22. Generative processGenerative model retinal input pons proprioceptive input Angular position of target in intrinsic coordinates Angular direction of gaze in extrinsic coordinates Angular direction of target time visual channels Smooth pursuit eye movements

23. eye (reduced precision) 50010001500200025003000 -2 0 1 2 Angular position displacement (degrees) 50010001500200025003000 -20 -10 0 10 20 30 40 50 time (ms) velocity (degrees per second) Angular velocity eye target Eye movements under occlusion and reduced prior precision

24. 1002003004005006007008009001000 -2 0 1 2 target and oculomotor angles time (ms) displacement (degrees) 1002003004005006007008009001000 -30 -20 -10 0 10 20 30 target and oculomotor velocities time (ms) velocity (degrees per second) eye (reduced precision) eye target Paradoxical responses to violations

25. Active inference, predictive coding and precision Precision and false inference Simulations of : Auditory perception (and omission related responses) Smooth pursuit eye movements (under occlusion) Sensory attenuation (and the force matching illusion)

26. Generative process Generative model Making your own sensations

27. motor reflex arc thalamus sensorimotor cortex prefrontal cortex ascending prediction errors descending modulation descending predictions descending motor predictions descending sensory predictions

28. Self-made acts

29. and psychomotor poverty

30. 102030405060 -0.5 0 0.5 1 1.5 2 hidden states Force matching illusion 102030405060 -0.5 0 0.5 1 1.5 2 prediction and error Time (bins) Sensory attenuation 102030405060 -0.5 0 0.5 1 1.5 hidden causes Time (bins) 102030405060 -0.5 0 0.5 1 1.5 Time (bins) perturbation and action Perceived as lessReproduced as more Intrinsic and extrinsic

31. 00.511.522.53 0 0.5 1 1.5 2 2.5 3 External (target) force Self-generated(matched) force External (target) force Self-generated(matched) force SimulatedEmpirical (Shergill et al) Compensated failures of sensory attenuation Normal subjects Schizophrenic subjects

32. Failure of sensory attenuation and delusions of control 102030405060 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 prediction and error Time (bins) 102030405060 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 hidden states Time (bins) 102030405060 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 hidden causes Time (bins) 102030405060 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 Time (bins) perturbation and action

33. We act by predicting our action to create (attenuated) prediction errors that are suppressed reflexively A failure of sensory attenuation subverts our predictions and precludes action ( psychomotor poverty ) Compensatory increases in prior precision reinstate (unattenuated) prediction errors Unattenuated prediction errors can only be explained by (antagonistic) external forces ( delusions of control and made acts ) A computational account of delusions of agency

34. Signs (of trait abnormalities) Attenuated violation responses Loss of perceptual Gestalt SPEM abnormalities Psychomotor poverty Resistance to illusions Symptoms (of psychotic state) Hallucinations Delusions + - Neuromodulatory failure (of sensory attenuation) Summary

35. What is the functional deficit? What is the pathophysiology? How can we measure it? What is the aetiology? What is the intervention? Summary False inference due to aberrant encoding of precision A neuromodulatory failure of postsynaptic excitability: Aberrant DA/NMDA subunit interactions Aberrant synchronous gain and fast (gamma) dynamics Aberrant cortical gain control and E-I (GABAergic) balance Aberrant dendritic integration (neuro-morphology) Modelling of behaviour and noninvasive brain responses Computational modelling of choice behaviour Computational fMRI Dynamic casual modelling of intrinsic (precision) gain control …

36. And thanks to collaborators: Rick Adams Ryszard Auksztulewicz Andre Bastos Sven Bestmann Harriet Brown Jean Daunizeau Mark Edwards Chris Frith Thomas FitzGerald Xiaosi Gu Stefan Kiebel James Kilner Christoph Mathys Jérémie Mattout Rosalyn Moran Dimitri Ognibene Sasha Ondobaka Will Penny Giovanni Pezzulo Lisa Quattrocki Knight Francesco Rigoli Klaas Stephan Philipp Schwartenbeck And colleagues: Micah Allen Felix Blankenburg Andy Clark Peter Dayan Ray Dolan Allan Hobson Paul Fletcher Pascal Fries Geoffrey Hinton James Hopkins Jakob Hohwy Mateus Joffily Henry Kennedy Simon McGregor Read Montague Tobias Nolte Anil Seth Mark Solms Paul Verschure And many others Thank you

37. V5 V1 IT PC Visual input Prefrontal input control subjects - predictable control subjects - unpredictable schizophrenia - predictable schizophrenia - unpredictable V1R V5L V5R ITL ITR PCL PC -2 -1.5 -0.5 0 0.5 1 1.5 cortical source log modulation Effects of predictability on recurrent inhibition control subjects schizophrenics