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Physiology-based modeling and quantification of auditory evoked potentials. Cliff Kerr Complex Systems Group School of Physics, University of Sydney. Introduction. Aim: to develop a physiology-based method of evoked potential (EP) analysis, in order to: Provide a means to quantify EPs - PowerPoint PPT Presentation
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Physiology-based modeling and quantification of auditory evoked
potentials
Cliff Kerr Complex Systems Group
School of Physics, University of Sydney
Introduction
• Aim: to develop a physiology-based method of evoked potential (EP) analysis, in order to:– Provide a means to quantify EPs– Relate EP data to brain physiology
• Implementation: biophysical modeling and deconvolution of EEG data
Outline• What are evoked potentials?• Fitting:
– Methods: theory, data, implementation
– Results: group average waveforms – Application: arousal
• Deconvolution:– Motivation– Theory– Results: synthetic and experimental
data• Discussion and summary• Challenges and future directions
What are EPs?
V(
V)
t(s)
EEG:
EP:
V(
V)
t(s)
Time-locked averaging
stimulus:
Traditional analysis: scoring
Feature Amplitude
Latency Feature Amplitude
LatencyP50 1.2 mV 56 msN1 8.0 mV 120 msP2 -8.0 mV 264 ms
N1 6.5 mV 112 msN2 3.4 mV 224 msP3 -19.6 mV 320 ms
Standard Target
e
i
r
s
n
Cortex
Thalamus
Brainstem
Theory
• Physiology-based continuum modeling: uses 11 vs. 1,000,000,000,000,000 connections
• Five populations of neurons: – Sensory (excitatory; labeled n)– Cortical (excitatory & inhibitory; e &
i )– Thalamic relay (excitatory; s)– Thalamic reticular (inhibitory; r)
• Five neuronal loops: – cortical (Gee , Gei )
– thalamic (Gsrs )
– thalamocortical (Gese , Gesre)
e
i
r
s
n
Theory
Theory• Model has 14 parameters:
– 5 for neuronal coupling strength (Gee , Gei , Gese , Gesre , Gsrs )
– 4 for neuronal network properties (, , , t0)– 5 for stimulus properties (tos , ts , ros , rs)
• Most important parameters are the gains Gab (coupling strength between neuron populations)
• Model describes conversion process (auditory stimulus → neuronal activity → scalp electrical field) using an analytic transfer function e/n:
n
einout SS
Theory
• Direct impulse:
• Cortical modulation:
• Corticothalamic modulation:
• Transfer function:
srs
esnti
GLGLeI 2
22/
1
0
eeeiec LGLGDM )1(
srs
esreeseti
t GLGLGLeM 2
32
1)(0
tcn
e
MMIT
),(),(),(
kkk
Theory
• Impulse:
• Time-domain impulse response:
kkkr k 23 dd),(),()2(1),(
tirin eeTtR
2
||21
22
2),(
s
r
s
ttt
n re
tet
s
os
s
os
rr
r
Data• Sampled from 1527 normal subjects:
– Aged 6-80 years– Equal numbers male & female– No neurological diseases, chemical
dependencies, etc.
• Stimulus: 1 tone/second for 6 minutes (280 standard tones, 80 target tones)
• Used to produce group average standard and target EPs (generated using >100,000 single trials!)
2
P1
P2
.
Fitting1) Initial parameters are chosen
2
P1
P2
.
Fitting2) Gradient descent algorithm reduces 2
of fit
2
P1
P2
Fitting3) Process is repeated using different
initialisations
• Excellent fits to standards (up to 400 ms)
Results
• Excellent fits to targets (up to 300 ms)
Results
Results• Possible changes in neuronal network
properties:
Results• Probable changes in neuronal coupling
strengths:
Results• Definite changes in stability parameters:
Application: arousal
task duration (m
in)
0.1 s-5 μV
0
6
4
2
• Same task (auditory oddball)
• 43 subjects
• Averaged over ten time intervals of 40 seconds each
Application: arousal• Increased cortical activity → decreased
acetylcholine?
Deconvolution: motivation• In model,
thalamocortical loop → N2 feature of targets
• Could target response = standard response + delayed standard response?
Deconvolution: motivation
Theory• Assumption: responses are product of
task-dynamic and task-invariant properties:
• Fourier transform:
• Take the ratio of the two:
• Inverse Fourier transform to get the result:
)]()([)( 1 IDtR SSF )]()([)( 1 IDtR TT
F
)()()]([ IDtR SS F )()()]([ IDtR TT F
)()()(
)()()()(
CS
T
S
T DDD
IDID
)()]([1 tDD CC F
Theory• Direct deconvolution is uselessly noisy:
• Hence, use Wiener deconvolution:
NSRR
RRRD
S
S
S
TC 2
2
|)(||)(|
)()()(
Synthetic data
Group average data
Single-subject data
Discussion and summary• Physiology-based EP fitting can be achieved• Offers significant advantages over traditional
methods• Results tentatively suggest physiology
underlying stimulus perception:– Increase in stability: required for a transient
response– Arousal determined by thalamocortical activity:
standards show increased inhibition, targets show increased excitation
– Standards generated by ≈1 thalamocortical impulse, targets by ≈2
Challenges• Fitting challenges
– Degeneracy– Constraints– Testability
• Deconvolution challenges– Noise and artifact– What are we looking for?
• Physiological challenges– Only 1D information– What’s signal?
Future directions• How does the brain change with age?
Standard Target
Future directions• Can our model account for depression?
Future directions• Modeling the ERP “zoo”
– modality
– arousal
– disease
– drugs
Visual: Somatosensory:
Bipolar: Radiculopathy:
Carbonyl sulfide:
Ecstasy:
Quiet sleep:Oddball:
Acknowledgements
Chris J. Rennie
Peter A. Robinson
Jonathon M. Clearwater
Andrew H. Kemp
Brain Resource Ltd.