rosalyn moran virginia tech carilion research institute bradley department of electrical &...
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Rosalyn Moran
Virginia Tech Carilion Research InstituteBradley Department of Electrical & Computer Engineering
Department of Psychiatry and Behavioral Medicine, VTC School of Medicine
Dynamic Causal Modelling For Cross-Spectral Densities
Data Features in DCM for CSDGenerative Models in the time domain
Generative Models in the frequency domainDCM Inversion procedure
Example 1: L-Dopa Modulations of theta spectra using DCM for CSDExample 2: Propofol Modulations of Delta and Gamma spectra using DCM for CSD
Outline
Data Features in DCM for CSDGenerative Models in the time domain
Generative Models in the frequency domainDCM Inversion procedure
Example 1: L-Dopa Modulations of theta spectra using DCM for CSDExample 2: Propofol Modulations of Delta and Gamma spectra using DCM for CSD
Outline
Dynamic Causal Modelling: Generic Framework
simple neuronal model
(slow time scale)
fMRI
detailed neuronal model
(synaptic time scales)
EEG/MEG
),,( uxFdt
dx
Neural state equation:
Hemodynamicforward model:neural activity BOLD
Time Domain Data
Resting State Data
Electromagneticforward model:
neural activity EEGMEGLFP
Time Domain ERP DataPhase Domain Data
Time Frequency DataSpectral Data
Dynamic Causal Modelling: Generic Framework
simple neuronal model
(slow time scale)
fMRI
detailed neuronal model
(synaptic time scales)
EEG/MEG
),,( uxFdt
dx
Neural state equation:
Hemodynamicforward model:neural activity BOLD
Time Domain Data
Resting State Data
Electromagneticforward model:
neural activity EEGMEGLFP
Time Domain ERP DataPhase Domain Data
Time Frequency DataSpectral Data Frequency (Hz)
Pow
er (m
V2 )
“theta”
DCM for Steady State ResponsesUnder linearity and stationarity assumptions, the model’s
biophysical parameters (e.g. post-synaptic receptor density and time constants) prescribe the cross-spectral density of responses measured directly (e.g. local field potentials) or indirectly through
some lead-field (e.g. electroencephalographic and magnetoencephalographic data).
Steady State
Statistically:A “Wide Sense Stationary” signal has 1st and 2nd moments that do
not vary with respect to time
Dynamically:A system in steady state has settled to some equilibrium after a
transient
Data Feature:Quasi-stationary signals that underlie Spectral Densities in the
Frequency Domain
Dynamic Causal Modelling: Framework
Generative M
odel
Baye
sian
Inve
rsio
n
Empirical Data
Model Structure/ Model Parameters
Explanandum
Competing Hypotheses (Models)
Optimization under model constraints
Spectral Densities
0 5 10 15 20 25 300
5
10
15
20
25
30
Frequency (Hz)
Po
wer
(u
V2 )
0 5 10 15 20 25 300
5
10
15
20
25
30
Frequency (Hz)
Po
wer
(u
V2 )
Spectral Density in Source 1
Spectral Density in Source 2
Spectral Densities
0 5 10 15 20 25 300
5
10
15
20
25
30
Frequency (Hz)
Po
wer
(u
V2 )
0 5 10 15 20 25 300
5
10
15
20
25
30
Frequency (Hz)
Po
wer
(u
V2 ) 0 5 10 15 20 25 30
0
5
10
15
20
25
30
Frequency (Hz)
Po
wer
(u
V2 )
Cross-Spectral Density between Sources 1 & 2
Spectral Density in Source 1
Spectral Density in Source 2
Cross Spectral Density: The Data E
EG
- M
EG
– L
FP
Tim
e S
eri
es
Cro
ss
Sp
ec
tral D
en
sity
1
1
2
2 3
3
4
4
1
2
3
4
A few LFP channels or EEG/MEG spatial modes
Autoregressive Model used to extract spectral representations from dataImaginary Numbers RetainedAveraged over trial types
npnpnnn eyyyy ....2211
ijijijij HHg )()()(
iwpijp
iwijiwijij eeeH
......
1)(
221
Real and Imaginary
Data features
Cross Spectral Density: The Data
Default order 8
AR coefficients prescribe the spectral densities
Outline
Data Features in DCM for CSDGenerative Models in the time domain
Generative Models in the frequency domainDCM Inversion procedure
Example 1: L-Dopa Modulations of theta spectra using DCM for CSDExample 2: Propofol Modulations of Delta and Gamma spectra using DCM for CSD
A selection of intrinsic architectures in SPM
A suite of neuronal population models including neural masses, fields and
conductance-based models…expressed in terms of sets of differential equations
Neural Mass Models in DCM
neuronal (source) model
State equations
Extrinsic Connections
,,uxFx
Granular Layer
Supragranular Layer
Infragranular Layer
Intrinsic Connections
Internal Parameters
EEG/MEG/LFPsignal
EEG/MEG/LFPsignal
Properties of tens of thousands of neurons approximated by their average response
Conductance-Based Neural Mass Models in DCM
))((
)(
, gVg
VVgVC
affthresholdaffaff
rev
Current in
Conductance
Potential Difference Noise Term: Since properties of tens of thousands of neurons approximated by their average response
Two governing equations: V = IR ……….. Ohms Law I = C dV/dt ……. for a capacitor
))((
)(
, gVg
VVgVC
affthresholdaffaff
rev
Current in
Conductance
Potential Difference Noise Term: Since properties of tens of thousands of neurons approximated by their average response
Time constant: κ Afferent Spikes :Strength of connection x σ
Channels already open: g
Conductance-Based Neural Mass Models in DCM
Two governing equations: V = IR ……….. Ohms Law I = C dV/dt ……. for a capacitor
))((
)(
, gVg
VVgVC
affthresholdaffaff
rev
Current in
Conductance
Potential Difference Noise Term: Since properties of tens of thousands of neurons approximated by their average response
Time constant: κ Channels already open: g
σ
μ - V
Afferent Spikes :Strength of connection x σ
Conductance-Based Neural Mass Models in DCM
Two governing equations: V = IR ……….. Ohms Law I = C dV/dt ……. for a capacitor
))((
)(
, gVg
VVgVC
affthresholdaffaff
rev
Intrinsic Afferents
Extrinsic Afferents
Conductance-Based Neural Mass Models in DCM
Different Neurotransmitters and Receptors?
Different Cell Types in 3/6 Layers?
Conductance-Based Neural Mass Models in DCM
Spiny stellate cells
Pyramidal cells
Inhibitory interneuron
))((
)(
, gVg
VVgVC
affthresholdaffaff
rev
Current Conductance Reversal Pot – Potential Diff
Afferent Firing No. open channelsTime ConstantConductance
Unit noise
Firing Variance
Exogenous input
E13
)(tI
Excitatory spiny cells in granular layers
Excitatory pyramidal cells in extragranular layers
Inhibitory cells in extragranular layers
Measured response
)( )3(Vg
E31
E23I
32
EERVE
EE
VEELL
gVg
IVVgVVgVC
)),((
)()()1()3()3(
13)1(
)1()1()1()1(
IIRVI
II
EERVE
EE
VIIEELL
gVg
gVg
VVgVVgVVgVC
)),((
)),((
)()()(
)2()2()2(22
)2(
)2()3()3(23
)2(
)2()2()2()2()2()2(
IIRVI
II
EERVE
EE
VIIEELL
gVg
gVg
VVgVVgVVgVC
)),((
)),((
)()()(
)3()2()2(32
)3(
)3()1()1(31
)3(
)3()3()3()3()3()3(
I22
Conductance-Based Neural Mass Models in DCM
vivHi
iv
dthvt
tetHthhv
ieieaffieie
tt
2//// 2)(
)(0;0
0;.)(;
Spiny stellate cells
Pyramidal cells
Inhibitory interneuron
MaximumPost Synaptic Potential
Parameterised Sigmoid
Inverse TimeConstant
Synaptic Kernel
H
Intrinsic connectivity
Convolution-Based Neural Mass Models in DCM
Extrinsic Forward Input
Extrinsic Backward Input
Extrinsic Backward Input
vivHi
iv
dthvt
tetHthhv
ieieaffieie
tt
2//// 2)(
)(0;0
0;.)(;
Spiny stellate cells
Pyramidal cells
Inhibitory interneuron
12
1611
11
2))(( viIvHi
iv
eeee
5g
Exogenous input
1)(tI
Excitatory spiny cells being granular layers
Excitatory pyramidal cells in extragranular layers
Inhibitory cells in extragranular layers
Measured response
)( 6vg
2
34547
52
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55
42
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44
2)(
2)(
iiv
vivHi
iv
vivHi
iv
iiii
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32
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33
22
2122
22
2)(
2)(
iiv
vivHi
iv
vivHi
iv
iiii
eeee
MaximumPost Synaptic Potential
Parameterised Sigmoid
Inverse TimeConstant
Synaptic Kernel
H
Intrinsic connectivity
Convolution-Based Neural Mass Models in DCM
Extrinsic Forward Input
Extrinsic Backward Input
Extrinsic Backward Input
Spiny stellate
Pyramidal cells
Inhibitory interneuron
Extrinsic Output
Extrinsic Forward Input
Extrinsic Backward Input
Extrinsic Backward Input
GABA Receptors
AMPA Receptors
NMDA Receptors
))((
)(
, gVg
VVgVC
affthresholdaffaff
rev
4 population CanonicalMicro-Circuit (CMC)
Spiny stellate
Superficial pyramidal
Inhibitory interneuron
Deep pyramidal
4-subpopulationCanonical Microcircuit
BackwardExtrinsic Output
ForwardExtrinsic Output
Extrinsic Forward Input
Extrinsic Backward Input
Extrinsic Backward Input
Temporal Derivatives
Outline
Data Features in DCM for CSDGenerative Models in the time domain
Generative Models in the frequency domainDCM Inversion procedure
Example 1: L-Dopa Modulations of theta spectra using DCM for CSDExample 2: Propofol Modulations of Delta and Gamma spectra using DCM for CSD
Time Differential Equations
)(
)(
xly
Buxfx
State Space Characterisation
Cxy
BuAxx
Transfer FunctionFrequency Domain
BAsICsH )()(
Linearise
mV
State equations to Spectra
Moran, Kiebel, Stephan, Reilly, Daunizeau, Friston (2007) A neural mass model of spectral responses in electrophysiology. NeuroImage
u: spectral innovationsWhite and colored noise
State Space Characterisation
Cxy
BuAxx
Generative Model of Spectra
Moran, Kiebel, Stephan, Reilly, Daunizeau, Friston (2007) A neural mass model of spectral responses in electrophysiology. NeuroImage
010010000000
2000000000
010000000000
000000110000
0002000000
000010000000
0000020000
0000000200
0000000200
000000100000
000000010000
000000001000
52
32
.42
22
12
gH
gH
gH
gH
gH
A
iiii
eeee
iiii
eeee
eeee
0
0
0
0
0
0
0
0
0
0
0
eeH
B
0
0
0
1
0
0
0
0
0
0
0
0
TC
Populated According to the neural mass model equations
The Output State(Pyramidal Cells)
The Input State(Stellate Cells)
State Space Characterisation
Cxy
BuAxx
010010000000
2000000000
010000000000
000000110000
0002000000
000010000000
0000020000
0000000200
0000000200
000000100000
000000010000
000000001000
52
32
.42
22
12
gH
gH
gH
gH
gH
A
iiii
eeee
iiii
eeee
eeee
0
0
0
0
0
0
0
0
0
0
0
eeH
B
0
0
0
1
0
0
0
0
0
0
0
0
TC
Modulation Transfer FunctionAn analytic mixture of state space parameters
Output Spectrum (Y) = Modulation Transfer Function x Spectrum of Innovations
Generative Model of Spectra
Freq
uenc
yNMDA connectivty
Posterior Cingulate Cortex
4 5 6 7 8
4
6
8
10
12
14
162 4 6 8 10 12 14 16
0
0.5
1
1.5
2
2.5
3
3.5
4
Frequency
Log
Powe
r
Posterior Cingulate Cortex
Freq
uenc
y
NMDA connectivty
Anterior Cingulate Cortex
4 5 6 7 8
4
6
8
10
12
14
16
2 4 6 8 10 12 14 160
2
4
6
8
10
12
Frequency
Log
Powe
r
Anterior Cingulate Cortex
)),(( )2()2()2()2(NMDARVNMDAINMDA gVg
Generative Model of Spectra
Observer Model in the Frequency Domain
Frequency (Hz)
Frequency (Hz)
Frequency (Hz)
Pow
er (m
V2 )Po
wer
(mV2 )
Pow
er (m
V2 )
Spectrum channel/mode 1
Spectrum mode 2
Cross-spectrum modes 1& 2..),:()(2 ,/ ieieHfH
..),:()(12 ,/ ieieHfH
..),:()(1 ,/ ieieHfH
+ White Noise in Electrodes
Interconnected Neural mass models
Lead Field
Sensor LevelSpectral Responses
Summary: Neural Mass Models in DCM
Outline
Data Features in DCM for CSDGenerative Models in the time domain
Generative Models in the frequency domainDCM Inversion procedure
Example 1: L-Dopa Modulations of theta spectra using DCM for CSDExample 2: Propofol Modulations of Delta and Gamma spectra using DCM for CSD
Dynamic Causal Modelling: Inversion & Inference
fMRIfMRIEEG/MEGEEG/MEG
Neural
state equation:
Electromagneticforward model:
Hemodynamicforward model:
Generative M
odel
Baye
sian
Inve
rsio
n
Empirical Data
Model Structure/ Model Parameters
Inference on models
Dynamic Causal Modelling: Inversion & InferenceBa
yesi
an In
vers
ion
)|(
)|(),|(),|(
myp
mpmypmyp
Bayes’ rules:
Model 1Model 2 Model 1
Free Energy: )),()(()(ln mypqDmypF max
-2 -1 0 1 2 3 4 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
%1.99)|0( yconnp
Inference on parameters
)|(
)|(
2
1
myp
mypBF
Model comparison via Bayes factor:
accounts for both accuracy and complexity of the model
allows for inference about structure (generalisability) of the model
Inference on models
Inference on parameters
Dynamic Causal Modelling: Inversion & InferenceBa
yesi
an In
vers
ion
)|(
)|(
2
1
myp
mypBF
Model comparison via Bayes factor:
)|(
)|(),|(),|(
myp
mpmypmyp
Bayes’ rules:
accounts for both accuracy and complexity of the model
allows for inference about structure (generalisability) of the model
Model 1Model 2 Model 1
Free Energy: )),()(()(ln mypqDmypF max
-2 -1 0 1 2 3 4 50
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
),()( mypq
%1.99)|0( yconnp
A Neural Mass Model
Inversion in the real & complex domain
0 10 20 30 40 500
0.5
1
1.5
2
2.5
3
3.5
Frequency (Hz)
real
prediction and response: E-Step: 32
0 10 20 30 40 50-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
Frequency (Hz)
imag
inar
y
prediction and response: E-Step: 32
0 10 20 30 40 50 60 70 80-2
-1.5
-1
-0.5
0
0.5
1
1.5
parameter
conditional [minus prior] expectation
Outline
Data Features in DCM for CSDGenerative Models in the time domain
Generative Models in the frequency domainDCM Inversion procedure
Example 1: L-Dopa Modulations of theta spectra using DCM for CSDExample 2: Propofol Modulations of Delta and Gamma spectra using DCM for CSD
Dopaminergic modulation in Humans
Aim: Infer plausible synaptic effects of dopamine in humans via non-invasive imaging
Approach: Double blind cross-over (within subject) design, with participants on placebo or
levodopa
Use MEG to measure effects of increased dopaminergic transmission
Study a simple paradigm with “known” dopaminergic effects (from the animal literature): working memory maintenance
Apply DCM to one region (a region with sustained activity throughout maintenance prefrontal)
Moran, Symmonds, Stephan, Friston, Dolan (2011) An In Vivo Assay of Synaptic Function Mediating Human Cognition, Current Biology
• Animal unit recordings have shown
selective persistent activity of
dorsolateral prefrontal neurons
during the delay period of a delayed-
response visuospatial WM task
(Goldman-Rakic et al, 1996)
• The neuronal basis for sustained
activity in prefrontal neurons involves
recurrent excitation among pyramidal
neurons and is modulated by
dopamine (Gao, Krimer, Goldman-
Rakic, 2001)
• Dose dependant inverted U
Working Memory
Dopamine in Working Memory
• DA terminals converge on pyramidal cells
and inhibitory interneurons in PFC (Sesack
et al, 1998)
• DA modulation occurs through several pre
and post synaptic mechanisms (Seamans
& Yang, 2004)
- Increase in NMDA mediated responses in pyramidal cells – postsynaptic D1 mechanism
- Decrease in AMPA EPSPs in pyramidal cells – presynaptic D1 mechanism
- Increase in spontaneous IPSP Amplitude and Frequency in GABAergic interneurons
- Decrease in extrinsic input current
Gao et al, 2001
Wang et al, 1999
Seamans et al, 2001
Memory
Probe Image
Target Image
. . . .4 sec
. . 300 ms
. . . . 2 sec
. . 300 ms
Memory
e.g. match e.g. no match
WM Paradigm in MEG on and off levodopa
Maintenance Period
Load titrated to 70% accuracy(predrug)
Behavioural Results
Memory
Probe Image
Target Image
match
68
69
70
71
72
73
74
75
76
77
Placebo L-Dopa
Titration
*
% A
ccu
racy
Activity at sensors during maintenance
• Localised main effect and interaction in right prefrontal cortex
• Significant effects of memory in different frequency bands (channels over time)
• Sustained effect throughout maintenance in delta - theta - alpha bands
Broad Band Low Frequency Activity
P A P AP A
Tim
e (s
)0
4
sensors
Interaction: Memory and Dopamine
c
Time (msec)
Fre
qu
ency
(H
z)
0 2 4 6 8 10 12 14 16 180.7
0.8
0.9
1
1.1
1.2
1.3
1.4
Frequency (Hz)
No
rmal
ised
Po
wer
(a.
u.)
L-Dopa
Placebo
Sustained Activity during memory maintenance:Sensor Space
DCM Architecture
AMPA receptors
NMDA receptors
GABAa receptors
Receptor Types
Pyramidal Cell (Population 3)
Inhibitory Interneurons (Population 2)
Spiny Stellates (Population 1)
Cell Populations
3,2
2,1
1,32,3
3,3
3,1
γ : The strengths of presynaptic inputs to and postsynaptic conductances of transmitter-receptor pairs
i.e. a coupling measure that absorbs a number of biophysical processes, e.g.:Receptor DensityTransmitter Reuptake
Synaptic Hypotheses
-100 -50 0 500
0.2
0.4
0.6
0.8
1
Membrane Potential (mV)
pyramidal
cellspyrami
dal cells
spiny stellate
cells
inhibitory interneurons
pyramidal cells
Ext
rin
sic
Co
rtic
al I
np
ut (
u)
NMDANMDARVNMDANMDA
AMPAAMPARVAMPAAMPA
VEMgNMDAEAMPALL
gVg
gVg
VVVfgVVgVVgVC
)),((
)),((
))(()()(
)2()3()3(3,2
)2(
)2()3()3(3,2
)2(
)2()2()2()2()2()2()2(
GABAaGABAaRVGABAaGABAa
NMDANMDARVRVNMDANMDA
AMPAAMPARVRVAMPAAMPA
VIGABAaEMgNMDAEAMPALL
gVg
gVVg
gVVg
VVgVVVfgVVgVVgVC
)),((
))],(),(([
))],(),(([
)())(()()(
)3()2()2(2,3
)3(
)3()3()3(3,3
)1()1(1,3
)3(
)3()3()3(3,3
)1()1(1,3
)3(
)3()3()3()3()3()3()3()3()3(
GABAaGABAaRVGABAaGABAa
AMPAAMPARVAMPAAMPA
VIGABAaEAMPALL
gVg
gVg
VVgVVgVVgVC
)),((
)),((
)()()(
)1()2()2(2,1
)1(
)1()3()3(3,1
)1(
)1()1()1()1()1()1(
3,31,3
3,22,3
3,1
2,1
L-Dopa relative to Placebo, Memory – No Memory Trials
1. Decrease in AMPA coupling (decreased γ1,3) 2. Increased sensitivity by NMDA receptors (increased α) 3. Increase in GABA coupling (increased γ3,2) 4. Decreased exogenous input (decreased u)
Parameter Estimates
L-Dopa : Memory – No Memory:
Interaction of Parameter and Task on L-Dopa ( p = 0.009)
L-Dopa : Memory – No Memory
MA
P P
aram
eter
est
imat
es
γ1,3 α γ 3,2 u
u
-0.09
-0.08
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
-8
-7
-6
-5
-4
-3
-2
-1
0x 10-4
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16 *
*
L-Dopa relative to Placebo, Memory – No Memory Trials
1. Decrease in AMPA coupling (decreased γ1,3) 2. Increased sensitivity by NMDA receptors (increased α) 3. Increase in GABA coupling (increased γ3,2) 4. Decreased exogenous input (decreased u)
Moran, Symmonds, Stephan, Friston, Dolan (2011) An In Vivo Assay of Synaptic Function Mediating Human Cognition, Current Biology
Individual Behaviour
L-Dopa : Memory – No Memory
MA
P P
ara
me
ter
es
tim
ate
s
γ1,3 α γ 3,2 u-0.09
-0.08
-0.07
-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
-8
-7
-6
-5
-4
-3
-2
-1
0x 10-4
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
*
*
• Decrease in AMPA coupling (decreased γ1,3)• Increased sensitivity by NMDA receptors
(increased α)
Performance Increase
AM
PA
co
nn
ecti
vity
γ1,
3
-10 -5 0 5 10 15 20-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
R = -0.51p < 0.05
Performance Increase
NM
DA
No
nlin
eari
ty α
-10 -5 0 5 10 15 20-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
R = 0.59p < 0.05
Moran, Symmonds, Stephan, Friston, Dolan (2011) An In Vivo Assay of Synaptic Function Mediating Human Cognition, Current Biology
Outline
Data Features in DCM for CSDGenerative Models in the time domain
Generative Models in the frequency domainDCM Inversion procedure
Example 1: L-Dopa Modulations of theta spectra using DCM for CSDExample 2: Propofol Modulations of Delta and Gamma spectra using DCM for CSD
Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness.
WakeMild Sedation: Responsive to commandDeep Sedation: Loss of Consciousness
Boly, Moran, Murphy, Boveroux, Bruno, Noirhomme, Ledoux, Bonhomme, Brichant, Tononi, Laureys, Friston, J Neuroscience, 2012
Propofol-induced loss of consciousness
WakeMild Sedation: Responsive to commandDeep Sedation: Loss of Consciousness
Anterior Cingulate/mPFC
Precuneus/Posterior Cingulate
WakeMild Sedation: Responsive to commandDeep Sedation: Loss of Consciousness
Increased gamma power in Propofol vs WakeIncreased low frequency power when consiousness is lost
Murphy et al. 2011
Propofol-induced loss of consciousness
Anterior Cingulate/mPFC
Precuneus/Posterior Cingulate
Bayesian Model Selection
WakeMild SedationDeep Sedation
Propofol-induced loss of consciousness
ACC PCCACC PCC ACC PCC
Thalamus Thalami
WakeMild SedationDeep Sedation
Propofol-induced loss of consciousness
ACC PCCACC PCC ACC PCC
Thalamus Thalami
Wake
Propofol-induced loss of consciousness
Parameters of Winning Model ACC PCC
Thalamus
Wake
Mild Sedation:Increase in thalamic excitability
Propofol-induced loss of consciousness
ACC PCC
Thalamus
ACC PCC
Thalamus
Wake
Mild Sedation:Increase in thalamic excitability
Propofol-induced loss of consciousness
ACC PCC
Thalamus
ACC PCC
Thalamus
Loss of Consciousness:Breakdown in Cortical Backward Connections
ACC PCC
Thalamus
Propofol-induced loss of consciousness
Loss of Consciousness
:Breakdown in Cortical Backward Connections
ACC PCC
Thalamus
Boly, Moran, Murphy,Boveroux, Bruno, Noirhomme, Ledoux, Bonhomme, Brichant, Tononi, Laureys, Friston, J Neuroscience, 2012
Summary
• DCM is a generic framework for asking mechanistic questions of neuroimaging data
• Neural mass models parameterise intrinsic and extrinsic ensemble connections and synaptic measures
• DCM for SSR is a compact characterisation of multi- channel LFP or EEG data in the Frequency Domain
• Bayesian inversion provides parameter estimates and allows model comparison for competing hypothesised architectures
• Empirical results suggest valid physiological predictions
Thank You
• FIL Methods Group