Computational Psychiatry Course - 29th-30th April 2015
Venue: Basement Lecture Theatre, 33 Queen Square, London, WC1N 3BG
The computational anatomy of psychosis
Karl Friston
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 symptoms and signs of schizophrenia
Bleuler
Dysmorphophobia
Delusions
Delusional mood
False beliefs
Delusional systems
Depersonalisation
Psychomotor property
Cognitive deficits
Compulsions
Intrusive thoughts
Obsessional
beliefs
Hallucinations
False percepts
Affective
symptoms
Dissociation
syndromes
Thought disorder
Capgras
syndrome
Listening of associations
Disintegration of the psyche
Functional
medical
syndromes
Anxiety
Persecutory
beliefs
…
Aberrant beliefs and
false inference
Soft neurological signs
Abnormal eye movements
Abnormal mismatch negativity
Pathophysiological and aetiological theories
Bleuler
Genetic
Dopamine hypothesis
Abnormal plasticity
Aberrant salience
Glutamate hypothesis
NMDA receptor dysfunction
Aberrant synchrony
GABAergic hypothesis
Aberrant gain control
Abnormal E-I balance
Neurodevelopmental
Aberrant neuromodulation
and synaptic gain control
Psychotomimetic drugs
Psychosocial
Autoimmune
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
Wernicke C. Grundrisse der Psychiatrie. 1906:
Sejunction – disruption of associative connectivity
Anatomical disconnection
Bleuler E. Dementia Praecox oder Gruppe der Schizophrenien,
1911: Disintegration – of conscious processing (the psyche)
Functional dysconnection
Aberrant neuromodulation
and synaptic gain control
Which computational (formal) framework?
Reinforcement learning, optimal control
and expected utility theory
Information theory and minimum
redundancy
Pavlov
Barlow
Self-organisation, synergetics and
allostasis
Haken
Bayesian brain, Bayesian decision theory
and predictive coding
Helmholtz
Which computational (formal) framework?
Reinforcement learning, optimal control
and expected utility theory
Information theory and minimum
redundancy
Pavlov
Barlow
Self-organisation, synergetics and
allostasis
Haken
Bayesian brain, Bayesian decision theory
and predictive coding
Helmholtz
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)
“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
Hermann von Helmholtz
Richard Gregory
Geoffrey Hinton
From the Helmholtz machine to the
Bayesian brain
Thomas Bayes
Richard Feynman
Bayesian filtering and predictive coding
 t   D      
prediction
update

  s  g ( )
prediction error
Making our own sensations
sensations – predictions
Prediction error
Action
Perception
Changing
sensations
Changing
predictions
Generative models
Discrete states
Continuous states
v(3)
, 
 v(2)

x(2)
 x(2)
Action


(2)
v
a0 ,
Control states
, at 1
ut ,
(1)
v
C
, uT
B
 x(1)
 x(1)
 v(1)
 v(0)
Hidden states

(i )
ot 1
ot
st 1
Variational Bayes
(belief updating)
  D     
(i )
st
A
Bayesian filtering
(predictive coding)
(i )
st 1
(i )
(i )
(i )
 ( i ) 0

st
st   (ln A  ot  ln B(at 1 )st 1 )
Hierarchical generative models
what
(3)
 v(3)

v
Ascending
prediction errors
the 
(2)
v
(2)
 x(2)

x
 x(2)
(2)
 v(2)

v
 v(1)
(1)
 x(1)

x
 x(1)
(1)
 v(1)

v
v(0)
where
Descending
predictions
  D      
Sensory
fluctuations
David Mumford
Predictive coding with reflexes
a   a s   v(1)
oculomotor
signals
reflex arc
Action
proprioceptive input
pons
retinal input
Perception
Prediction error (superficial pyramidal cells)
frontal eye fields
Top-down or backward
predictions
Precision
geniculate
 (i )
VTA

v(i )   (vi ) v(i )   (vi ) (v(i 1)  g (i ) ( x(i ) , v(i ) ))
 x(i )   (xi ) x(i )   (xi ) (D x(i )  f (i ) ( x(i ) , v(i ) ))
Conditional predictions (deep pyramidal cells)
Bottom-up or forward
prediction error
visual cortex
 (i )
v(i )  Dv(i )   v (i )   (i )   v(i 1)
 x(i )  D x(i )   x (i )   (i )
Unconditioned stimulus (US)
Bayesian belief updating
a
Motor Cortex
Condition stimulus (CS)
ot
Striatum
st
Simulated (US) responses
400

350
Inferotemporal Cortex
Q
300
Rate
250
200
150
Prefrontal Cortex

100
50
0
VTA/SN
1
2
3
4
Peristimulus time (sec)
Simulated (CS & US) responses
400
Action selection
Incentive salience
st   (ln A  ot  ln B(at 1 ) st 1 )
   (  Q)
 

  Q 
350
300
250
Rate
Perception
200
150
100
50
0
1
2
3
Peristimulus time (sec)
4
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)
De-compensation
(trait abnormalities)
+
Neuromodulatory failure
(of sensory attenuation)
Attenuated violation responses
Loss of perceptual Gestalt
SPEM abnormalities
Psychomotor poverty
Resistance to illusions
Hallucinations
Delusions
-
Compensation
(to psychotic state)
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)
Neuronal hierarchy
Generative model
(1)
1
v
(1)
2
v
f (2)
18 x2(2)  18 x1(2)


(2)
(2) (2)
(2) 
 32 x1  2 x3 x1  x2 
 2 x (2) x (2)  8 x(2)

3 3
 1 2

f (1)
18 x2(1)  18 x1(1)

 (1) (1)
(1) (1)
(1) 
  v1 x1  2 x3 x1  x2 
 2 x (1) x (1)  v (1) x (1)

2
3
 1 2

 x (1)   s 
g (1)   2(1)    1 
 x3   s2 
 x (2)  v (1) 
g (2)   2(2)    1(1) 
 x3  v2 
Syrinx
Predictive coding
v( i )  Dv( i )   v ( i )   ( i )   v( i 1)
 x( i )  D x( i )   x (i )   ( i )
sonogram
percept
 v(0)
 v(1)
prediction error
micro-volts)
8
LFP (
Frequency (KHz)
Frequency (Hz)
10
6
4
2
0
-2
-4
-6
0.5
1
Time (sec)
1.5
500
1000
1500
peristimulus time (ms)
2000
percept
response to violation
Omission related
responses, MMN
and hallucinosis
100
5000
4500
LFP (micro-volts)
Frequency (Hz)
50
4000
3500
3000
0
-50
2500
2000
0.5
1
time (sec)
-100
1.5
percept
500
1000
1500
peristimulus time (ms)
2000
attenuated mismatch negativity
100
5000
4500
Reduced prior
precision
LFP (micro-volts)
Frequency (Hz)
50
4000
3500
3000
0
-50
2500
2000
0.5
1
time (sec)
-100
1.5
500
percept
1000
1500
peristimulus time (ms)
2000
hallucination
100
5000
4500
Compensatory
attenuation of
sensory precision
LFP (micro-volts)
Frequency (Hz)
50
4000
3500
3000
0
-50
2500
2000
0.5
1
time (sec)
1.5
-100
500
1000
1500
peristimulus time (ms)
2000
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)
Smooth pursuit eye
movements
Angular direction of target
x t(1)
Angular direction of gaze in extrinsic coordinates
x (1)
o
Angular position of target in intrinsic
xt(1)  x (1)
o
coordinates
so  x(1)
o
proprioceptive input
pons
retinal input
st
visual channels
time
ù
és ù é
xo(1)
ú+ wv(1)
s = ê o ú= ê
êst ú êO( xt(1) ) ×exp(- ([- 8,¼ ,8] + xo(1) - xt(1) ) 2 )ú
ë û ë
û
(1) ù
(1)
éx&
é
ù
xo¢
ê o ú ê
ú
(1)
(1)
(1)
ê1 (v - x (1) ) - 1 x ¢(1) ú+ w(1)
¢ ú
x& = êêx&
=
o
o
o
x
4
2
ú ê
ú
(1)
(1)
êx&(1) ú ê
ú
v - xt
ú ëê
ú
ëê t û
û
és ù
s = ê o ú=
êst ú
ë û
éx&o ù
ê ú
ú=
x&= êx&¢
ê oú
êx&ú
ë tû
é
ù
xo
ê
ú+ ω (1)
v
2
êO(xt ) ×exp(- ([- 8, K ,8] + x o - xt ) )ú
ë
û
é x¢
ù
o
ê
ú
ê1 a - 1 x o¢ú+ ω (1)
x
4
8
ê
ú
Generative process
ê v- x ú
ú
t û
ëê
v (1) = x1(2) + wv(2)
éx&(2) ù
x&(2) = ê 1(2) ú=
êx& ú
ë2 û
v (2) = h + wv(3)
1
8
é x (2) ù
v (2) ê 2 (2) ú+ wx(2)
ê- x ú
ë 1 û
Generative model
Eye movements under occlusion
and reduced prior precision
Angular position
displacement (degrees)
2
1
target
eye
0
-1
eye (reduced precision)
-2
500
1000
1500
2000
2500
3000
2000
2500
3000
velocity (degrees per second)
Angular velocity
50
40
30
20
10
0
-10
-20
500
1000
1500
time (ms)
displacement (degrees)
Paradoxical responses
to violations
target and oculomotor angles
2
eye
1
target
0
eye (reduced
precision)
-1
-2
100
200
300
400
500
600
time (ms)
700
800
900
1000
900
1000
velocity (degrees per second)
target and oculomotor velocities
30
20
10
0
-10
-20
-30
100
200
300
400
500
600
time (ms)
700
800
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)
Making your own sensations
s   x 
s   p    i   s
 ss   xi  xe 
 xi   vi  14  xi 
x 
  x
1
 xe  ve  4  xe 
v 
v   i   v
ve 
a
 s   xi 
s   p  
  ωs
 ss   x i  v e 
x  xi   (a )  14  xi  ω x
sp
ω s ~ N (0, e 8 I )
ω x ~ N (0, e 8 I )
ss
s ~ N (0, e I )
 x ~ N (0, e4 I )
xi
Generative process
ve
  8     ( xi  vi )
v ~ N (0, e6 I )
Generative model
x
v
sensorimotor cortex
descending predictions
mx
descending sensory
predictions
descending modulation
thalamus
prefrontal cortex
mv
ascending prediction errors
v
descending motor
predictions
s
v
a
sp
ss
motor reflex arc
Self-made acts
prediction and error
hidden states
2
xi
2
1.5
ss
sp
1
1.5
1
0.5
0.5
0
0
-0.5
-0.5
5
10
15
20
25
30
xe
5
10
Time (bins)
15
20
25
30
Time (bins)
Sensory attenuation
hidden causes
1
perturbation and action
1
vi
a
0.8
0.6
0.5
ve
0.4
0.2
0
0
-0.2
-0.4
-0.5
-0.6
-0.8
5
10
15
20
Time (bins)
25
30
5
10
15
20
Time (bins)
25
30
and psychomotor
poverty
prediction and error
hidden states
2
2
1.5
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
5
10
15
20
25
30
5
10
time
15
20
25
30
time
Failure of sensory attenuation
hidden causes
perturbation and action
1
1
0.8
0.6
0.5
0.4
0.2
0
0
-0.2
-0.4
-0.5
-0.6
-0.8
5
10
15
time
20
25
30
5
10
15
time
20
25
30
Perceived as less
prediction and error
2
hidden states
2
1.5
Reproduced as more
hidden states
prediction and error
2
2
1.5
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
1.5
1
1
0.5
0.5
0
0
-0.5
-0.5
10
20
30
40
50
60
10
20
Time (bins)
30
40
50
60
10
20
Time (bins)
30
40
50
60
10
20
Time (bins)
Sensory attenuation
30
40
50
60
Time (bins)
Force matching illusion
Intrinsic and extrinsic
hidden causes
2
2
perturbation and action
hidden causes
perturbation and action
1.5
1.5
1
1
0.5
0.5
1.5
1.5
1
1
0.5
0.5
0
0
0
0
-0.5
-0.5
-0.5
10
20
30
40
Time (bins)
50
60
-0.5
10
20
30
40
Time (bins)
50
60
10
20
30
40
Time (bins)
50
60
10
20
30
40
Time (bins)
50
60
Compensated failures of sensory attenuation
Normal subjects
3
Simulated
Empirical
(Shergill et al)
Self-generated(matched) force
Self-generated(matched) force
2.5
2
1.5
1
Schizophrenic subjects
0.5
0
0
0.5
1
1.5
2
External (target) force
2.5
3
External (target) force
prediction and error
3.5
3
3
2.5
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
0
-0.5
10
20
30
40
50
hidden states
3.5
60
-0.5
10
20
Time (bins)
30
40
50
60
Time (bins)
Failure of sensory attenuation and delusions of control
hidden causes
3.5
3.5
3
3
2.5
2.5
2
2
1.5
1.5
1
1
0.5
0
0.5
-0.5
0
-1
perturbation and action
10
20
30
40
Time (bins)
50
60
-0.5
10
20
30
40
Time (bins)
50
60
A computational account of delusions of agency
• 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)
Summary
Signs (of trait abnormalities)
+
-
Neuromodulatory failure
(of sensory attenuation)
Attenuated violation responses
Loss of perceptual Gestalt
SPEM abnormalities
Psychomotor poverty
Resistance to illusions
Symptoms (of psychotic state)
Hallucinations
Delusions
Summary
• What is the functional deficit?
False inference due to aberrant encoding of
precision
A neuromodulatory failure of postsynaptic excitability:
• What is the pathophysiology?
•
•
•
•
• How can we measure it?
Modelling of behaviour and noninvasive brain
responses
• What is the aetiology?
• What is the intervention?
•
•
•
•
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)
Computational modelling of choice behaviour
Computational fMRI
Dynamic casual modelling of intrinsic (precision) gain control
…
Thank you
And thanks to collaborators:
And colleagues:
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
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
Prefrontal input
control subjects - predictable
control subjects - unpredictable
schizophrenia - predictable
schizophrenia - unpredictable
PC
PC
IT
IT
Effects of predictability on recurrent inhibition
1.5
V5
control subjects
schizophrenics
1
V5
V1
log modulation
0.5
0
-0.5
-1
-1.5
-2
Visual input
V1
R V5
L V5
R IT
cortical source
L IT
R PC
L PC