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 i ID: 1039569
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1. AbstractWe 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 FristonComputational Psychiatry Course - 29th-30th April 2015Venue: Basement Lecture Theatre, 33 Queen Square, London, WC1N 3BG
2. The symptoms and signs of schizophreniaDelusionsFalse beliefsDelusional systemsHallucinations False perceptsThought disorder Listening of associationsDisintegration of the psychePsychomotor propertyCognitive deficitsSoft neurological signsAbnormal eye movementsAbnormal mismatch negativityBleulerDysmorphophobiaDelusional moodDepersonalisationCompulsionsIntrusive thoughtsObsessional beliefsAffective symptomsDissociation syndromesCapgras syndromeFunctional medical syndromesAnxietyPersecutory beliefs…Aberrant beliefs and false inference
3. Pathophysiological and aetiological theoriesDopamine hypothesisAbnormal plasticityAberrant salienceGlutamate hypothesisNMDA receptor dysfunctionAberrant synchronyGABAergic hypothesisAberrant gain controlAbnormal E-I balanceGeneticNeurodevelopmentalPsychotomimetic drugsPsychosocialAutoimmuneBleulerAberrant neuromodulation and synaptic gain control
4. 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 connectivityAnatomical disconnectionFunctional dysconnectionDysconnection in schizophrenia: from abnormal synaptic plasticity to failures of self-monitoring. Schizophr Bull. 2009 May;35(3):509-27Klaas E. Stephan, Karl J. Friston and Chris D. FrithAberrant neuromodulation and synaptic gain control
5. Which computational (formal) framework?Reinforcement learning, optimal control and expected utility theoryInformation theory and minimum redundancySelf-organisation, synergetics and allostasisBayesian brain, Bayesian decision theory and predictive codingPavlovHakenHelmholtzBarlow
6. Which computational (formal) framework?Reinforcement learning, optimal control and expected utility theoryInformation theory and minimum redundancySelf-organisation, synergetics and allostasisBayesian brain, Bayesian decision theory and predictive codingPavlovHakenHelmholtzBarlow
7. Active inference, predictive coding and precisionPrecision and false inferenceSimulations of :Auditory perception (and omission related responses)Smooth pursuit eye movements (under occlusion)Sensory attenuation (and the force matching illusion)
8. “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 BayesGeoffrey HintonRichard FeynmanFrom the Helmholtz machine to the Bayesian brainRichard GregoryHermann von Helmholtz
9. Bayesian filtering and predictive codingprediction updateprediction error
10. Making our own sensationsChanging sensationssensations – predictionsPrediction errorChanging predictionsActionPerception
11. Generative modelsHidden statesActionControl statesContinuous statesDiscrete statesBayesian filtering (predictive coding)Variational Bayes(belief updating)
12. theDescendingpredictionsAscending prediction errorswhatwhereSensory fluctuationsHierarchical generative models
13. frontal eye fieldsgeniculatevisual cortexretinal inputponsoculomotor signalsPrediction error (superficial pyramidal cells)Conditional predictions (deep pyramidal cells)Top-down or backward predictionsBottom-up or forward prediction errorproprioceptive inputreflex arcPerceptionVTADavid MumfordPredictive coding with reflexesActionPrecision
14. Bayesian belief updatingVTA/SNPrefrontal CortexMotor CortexInferotemporal CortexStriatum1234050100150200250300350400Simulated (CS & US) responsesPeristimulus time (sec)Rate1234050100150200250300350400Simulated (US) responsesPeristimulus time (sec)RateCondition stimulus (CS)Unconditioned stimulus (US)PerceptionAction selectionIncentive salience
15. Active inference, predictive coding and precisionPrecision and false inferenceSimulations of :Auditory perception (and omission related responses)Smooth pursuit eye movements (under occlusion)Sensory attenuation (and the force matching illusion)
16. +-De-compensation(trait abnormalities)Compensation (to psychotic state)Neuromodulatory failure (of sensory attenuation)Attenuated violation responsesLoss of perceptual GestaltSPEM abnormalitiesPsychomotor povertyResistance to illusionsHallucinationsDelusions
17. Active inference, predictive coding and precisionPrecision and false inferenceSimulations of :Auditory perception (and omission related responses)Smooth pursuit eye movements (under occlusion)Sensory attenuation (and the force matching illusion)
18. Generative modelSyrinxNeuronal hierarchy Time (sec)Frequency (KHz)sonogram0.511.5Frequency (Hz)perceptprediction errorPredictive coding500100015002000-6-4-20246810peristimulus time (ms)LFP (micro-volts)
19. Reduced prior precisionCompensatory attenuation of sensory precisionOmission related responses, MMN and hallucinosis
20. Active inference, predictive coding and precisionPrecision and false inferenceSimulations of :Auditory perception (and omission related responses)Smooth pursuit eye movements (under occlusion)Sensory attenuation (and the force matching illusion)
21. Generative processGenerative modelretinal inputponsproprioceptive inputAngular position of target in intrinsic coordinatesAngular direction of gaze in extrinsic coordinatesAngular direction of target timevisual channelsSmooth pursuit eye movements
22. eye (reduced precision)50010001500200025003000-2-1012Angular positiondisplacement (degrees) 50010001500200025003000-20-1001020304050time (ms)velocity (degrees per second)Angular velocity eye targetEye movements under occlusion and reduced prior precision
23. 1002003004005006007008009001000-2-1012target and oculomotor anglestime (ms)displacement (degrees) 1002003004005006007008009001000-30-20-100102030target and oculomotor velocitiestime (ms)velocity (degrees per second) eye (reduced precision) eye targetParadoxical responses to violations
24. Active inference, predictive coding and precisionPrecision and false inferenceSimulations of :Auditory perception (and omission related responses)Smooth pursuit eye movements (under occlusion)Sensory attenuation (and the force matching illusion)
25. Generative processGenerative modelMaking your own sensations
26. motor reflex arcthalamussensorimotor cortexprefrontal cortexascending prediction errorsdescending modulationdescending predictionsdescending motor predictionsdescending sensory predictions
27. Sensory attenuation51015202530-0.500.511.52prediction and errorTime (bins)51015202530-0.500.511.52hidden statesTime (bins)51015202530-0.500.51hidden causesTime (bins)51015202530-0.8-0.6-0.4-0.200.20.40.60.81Time (bins)perturbation and actionSelf-made acts
28. Failure of sensory attenuation51015202530-0.500.511.52prediction and errortime51015202530-0.500.511.52hidden statestime51015202530-0.500.51hidden causestime51015202530-0.8-0.6-0.4-0.200.20.40.60.81timeperturbation and actionand psychomotor poverty
29. 102030405060-0.500.511.52prediction and errorTime (bins)102030405060-0.500.511.52hidden statesTime (bins)102030405060-0.500.511.52hidden causesTime (bins)102030405060-0.500.511.52Time (bins)perturbation and action102030405060-0.500.511.52hidden statesForce matching illusion102030405060-0.500.511.52prediction and errorTime (bins)Time (bins)Sensory attenuation102030405060-0.500.511.5hidden causesTime (bins)102030405060-0.500.511.5Time (bins)perturbation and actionPerceived as lessReproduced as moreIntrinsic and extrinsic
30. 00.511.522.5300.511.522.53 External (target) forceSelf-generated(matched) forceExternal (target) forceSelf-generated(matched) forceSimulatedEmpirical (Shergill et al)Compensated failures of sensory attenuationNormal subjectsSchizophrenic subjects
31. Failure of sensory attenuation and delusions of control102030405060-0.500.511.522.533.5prediction and errorTime (bins)102030405060-0.500.511.522.533.5hidden statesTime (bins)102030405060-1-0.500.511.522.533.5hidden causesTime (bins)102030405060-0.500.511.522.533.5Time (bins)perturbation and action
32. 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
33. Signs (of trait abnormalities)Attenuated violation responsesLoss of perceptual GestaltSPEM abnormalitiesPsychomotor povertyResistance to illusionsSymptoms (of psychotic state)HallucinationsDelusions+-Neuromodulatory failure(of sensory attenuation)Summary
34. 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 precisionA neuromodulatory failure of postsynaptic excitability:Aberrant DA/NMDA subunit interactionsAberrant synchronous gain and fast (gamma) dynamicsAberrant cortical gain control and E-I (GABAergic) balanceAberrant dendritic integration (neuro-morphology)Modelling of behaviour and noninvasive brain responsesComputational modelling of choice behaviourComputational fMRIDynamic casual modelling of intrinsic (precision) gain control…
35. And thanks to collaborators:Rick AdamsRyszard AuksztulewiczAndre BastosSven BestmannHarriet BrownJean DaunizeauMark EdwardsChris FrithThomas FitzGeraldXiaosi GuStefan KiebelJames KilnerChristoph MathysJérémie MattoutRosalyn MoranDimitri OgnibeneSasha Ondobaka Will PennyGiovanni PezzuloLisa Quattrocki KnightFrancesco Rigoli Klaas StephanPhilipp Schwartenbeck And colleagues:Micah AllenFelix BlankenburgAndy ClarkPeter DayanRay DolanAllan HobsonPaul FletcherPascal FriesGeoffrey HintonJames HopkinsJakob HohwyMateus JoffilyHenry KennedySimon McGregorRead MontagueTobias NolteAnil SethMark SolmsPaul VerschureAnd many othersThank you
36. V5V5V1ITITPCPCVisual inputPrefrontal inputcontrol subjects - predictablecontrol subjects - unpredictableschizophrenia - predictableschizophrenia - unpredictableV1R V5L V5R ITL ITR PCL PC-2-1.5-1-0.500.511.5cortical sourcelog modulationEffects of predictability on recurrent inhibition control subjectsschizophrenics