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![Page 1: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/1.jpg)
Physiology-based modeling and quantification of auditory evoked
potentials
Cliff Kerr Complex Systems Group
School of Physics, University of Sydney
![Page 2: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/2.jpg)
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
![Page 3: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/3.jpg)
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
![Page 4: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/4.jpg)
What are EPs?
V(V)
t(s)
EEG:
EP:
V(V)
t(s)
Time-locked averaging
stimulus:
![Page 5: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/5.jpg)
Traditional analysis: scoring
Feature Amplitude
Latency Feature Amplitude
Latency
P50 1.2 mV 56 ms
N1 8.0 mV 120 ms
P2 -8.0 mV 264 ms
N1 6.5 mV 112 ms
N2 3.4 mV 224 ms
P3 -19.6 mV 320 ms
Standard Target
![Page 6: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/6.jpg)
e
i
r
s
n
Cortex
Thalamus
Brainstem
Theory
![Page 7: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/7.jpg)
• 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
![Page 8: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/8.jpg)
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
![Page 9: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/9.jpg)
Theory
• Direct impulse:
• Cortical modulation:
• Corticothalamic modulation:
• Transfer function:
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GLeI
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srs
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k
kk
![Page 10: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/10.jpg)
Theory
• Impulse:
• Time-domain impulse response:
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1),(
tiri
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![Page 11: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/11.jpg)
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!)
![Page 12: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/12.jpg)
2
P1
P2
.
Fitting1) Initial parameters are chosen
![Page 13: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/13.jpg)
2
P1
P2
.
Fitting2) Gradient descent algorithm reduces 2
of fit
![Page 14: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/14.jpg)
2
P1
P2
Fitting3) Process is repeated using different
initialisations
![Page 15: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/15.jpg)
• Excellent fits to standards (up to 400 ms)
Results
![Page 16: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/16.jpg)
• Excellent fits to targets (up to 300 ms)
Results
![Page 17: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/17.jpg)
Results• Possible changes in neuronal network
properties:
![Page 18: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/18.jpg)
Results• Probable changes in neuronal coupling
strengths:
![Page 19: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/19.jpg)
Results• Definite changes in stability parameters:
![Page 20: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/20.jpg)
Application: arousal
task du
ratio
n (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
![Page 21: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/21.jpg)
Application: arousal• Increased cortical activity → decreased
acetylcholine?
![Page 22: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/22.jpg)
Deconvolution: motivation• In model,
thalamocortical loop → N2 feature of targets
• Could target response = standard response + delayed standard response?
![Page 23: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/23.jpg)
Deconvolution: motivation
![Page 24: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/24.jpg)
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 DD
D
ID
ID
)()]([1 tDD CC F
![Page 25: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/25.jpg)
Theory• Direct deconvolution is uselessly noisy:
• Hence, use Wiener deconvolution:
NSRR
R
R
RD
S
S
S
TC 2
2
|)(|
|)(|
)(
)()(
![Page 26: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/26.jpg)
Synthetic data
![Page 27: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/27.jpg)
Group average data
![Page 28: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/28.jpg)
Single-subject data
![Page 29: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/29.jpg)
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
![Page 30: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/30.jpg)
Challenges• Fitting challenges
– Degeneracy– Constraints– Testability
• Deconvolution challenges– Noise and artifact– What are we looking for?
• Physiological challenges– Only 1D information– What’s signal?
![Page 31: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/31.jpg)
Future directions• How does the brain change with age?
Standard Target
![Page 32: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/32.jpg)
Future directions• Can our model account for depression?
![Page 33: Physiology-based modeling and quantification of auditory evoked potentials Cliff Kerr Complex Systems Group School of Physics, University of Sydney.](https://reader035.fdocuments.us/reader035/viewer/2022062407/56649d4b5503460f94a28baf/html5/thumbnails/33.jpg)
Future directions• Modeling the ERP “zoo”
– modality
– arousal
– disease
– drugs
Visual: Somatosensory:
Bipolar: Radiculopathy:
Carbonyl sulfide:
Ecstasy:
Quiet sleep:Oddball:
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Acknowledgements
Chris J. Rennie
Peter A. Robinson
Jonathon M. Clearwater
Andrew H. Kemp
Brain Resource Ltd.