Realistic modeling and interpretation of depth-EEG signals recorded - - PowerPoint PPT Presentation

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Realistic modeling and interpretation of depth-EEG signals recorded during inter-ictal to ictal transition in temporal lobe epilepsy

• F. Wendling

INSERM U642 - University of Rennes Laboratory of Signal and Image Processing Rennes – France

http://perso.univ-rennes1.fr/fabrice.wendling/

LTSI

International Workshop on Advanced Epilepsy Treatment - CADET 2009 – 28-30 March, 2009 Kitakyushu Science & Research Park, Kitakyushu, Japan

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• Neurological disorder characterized by recurrent seizures
• Excessive firing in neuronal cells, abnormally-high synchronization

processes in neuronal networks

• Imbalance between excitation- and inhibition-related processes
• Poorly understood mechanisms of:
• epileptogenesis (property of a neuronal tissue to become epileptic)
• ictogenesis (transition from interictal to ictal activity)

Epilepsies Development of numerous techniques allowing for the observation of neuronal activity Development of models

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Electrophysiological observations

Neuron Neuronal population Cerebral structure Cerebral region Brain

Cell Organ

Experimental models (animals)

• Field activity
• Cellular activity (1 or a few cells)

Preictal activity Fast onset activity Ictal burst activity Ictal burst activity

(slower frequency) Seizure start Seizure termination

Background activity

… … … …

Superficial EC Deep EC 5 s

Klaus Goldbrandsen

Neurological Institute Carlo Besta, Milan

• M. de Curtis

Cerveau isolé

Human

• Local field activity (intracerebral EEG, ECoG)
• Global activity (scalp EEG, MEG)

Intracerebral multiple lead electrodes (lead: ∅ 0.8 mm, L 2mm) SEEG exploration SEEG exploration Intracerebral multiple lead electrodes (lead: ∅ 0.8 mm, L 2mm) SEEG exploration SEEG exploration Intracerebral multiple lead electrodes (lead: ∅ 0.8 mm, L 2mm) SEEG exploration SEEG exploration

Epilepsy Unit, CHR La timone, Marseille

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Objective of this work: “To interpret” depth-EEG signals A difficult issue:

Observations are incomplete

• In time: epilepsy = progressive disease, observation window is limited
• In space: spatial undersampling, some structures can not be recorded

(difficult access)

Pathophysiological mechanisms occur at different temporal scales

• Epileptic « spikes »: a few hundred of ms
• Seizures: a few tens of seconds up to several minutes (prediction?)
• Frequency of seizures : a few/day up to a few/month (regulations ?)

Complexity of recorded systems (specific cytoarchitectonics,

nonlinear mechanisms, different spatial scales, short/long term plasticity) Depth-EEG is a non-stationary signal with transient events and ruptures of dynamics (more or less abrupt)

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Amygdala

• Ant. hippocampus
• Post. hippocampus

Entorhinal cortex 5 sec

Interictal and pre-onset activity (TLE)

Depth-EEG

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Amygdala

• Ant. hippocampus
• Post. hippocampus

Entorhinal cortex

Seizure onset

5 sec Depth-EEG

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Amygdala

• Ant. hippocampus
• Post. hippocampus

Entorhinal cortex

Ictal activity

5 sec Depth-EEG

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Interictal / ictal transition

• Ant. hippocampus
• Post. hippocampus

Amygdala Entorhinal cortex

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• Ant. hip.
• Post. hip.

Power spectral densities

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• Ant. hip.
• Post. hip.
• Ant. hip.
• Post. hip.

Interictal

PSD

(V²/Hz)

f (Hz) PSD

(V²/Hz)

f (Hz)

Onset

PSD

(V²/Hz)

f (Hz) PSD

(V²/Hz)

f (Hz)

Ictal

PSD

(V²/Hz)

f (Hz) PSD

(V²/Hz)

f (Hz)

Power spectral densities

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Time-frequency representation

HIP Frequency (Hz) 5 s Time (s)

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Approach : physiological modeling of depth-EEG signals

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• F. Wendling, Computational models of epileptic activity: a bridge between observation and

pathophysiolocial interpretation, Expert Review of Neurotherapeutics (2008)

Models used in the study of epileptic phenomena

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Why a ‘population-oriented’ approach ?

• Main figures:
• Cerebral cortex : 10 billions of neurons
• Each neuron is connected to a large number of neurons

(100 to 100 000 synapses/neuron)

• Interactions between subpopulations of cells Ensemble

dynamics (positive or negative loops, feedback/feedforward)

• EEG dynamics
• reflection of these ensemble interactions
• summation of PSP generated by a large number of cells

activated quasi-synchronously

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Background

• Population models : Wilson & Cowan (1972), Freeman (~1970), Lopes da Silva

(~1970), Jansen (1993, 1995), Wendling (2000), Suffczynski (2001), and others

W.J. Freeman, Tutorial on neurobiology: From single neurons to brain chaos, Int. J. Bif. Chaos, 1992

Main features

• Relevant variable: firing-rate
• Synaptic inputs sum linearly

into the soma (mean-field approximation)

• Firing-rate computed from the

total current delivered by synaptic inputs

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Example : Freeman ’s model (1/2)

Olfactory system (receptors → olf. bulb → Ant olf. nucleus → prepyiform cortex)

2nd order

• rdinary

differential equation

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Example : Freeman ’s model (2/2)

W.J. Freeman, Simulation of chaotic EEG patterns with a Dynamic Model of the Olfactory System, Biol. Cyb., 1987

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excitatory inhibitory

Main cells (Pyramidal) Inhibitory interneurons

Neuronal population model : basic principles

Wendling F, Chauvel P, “Transition to ictal activity in Temporal Lobe Epilepsy: insights from macroscopic models”, in Computational Neuroscience in Epilepsy,. I. Soltesz & K. Staley eds., 2008

Neuronal population

From other subset(s) of cells To other subset(s) of cells From other subset(s) of cells To other subset(s) of cells

« Wave-to-Pulse » (nonlinear function) PSP APs « Pulse-to-Wave » (linear transfer function) APs PSP Afferent input

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Pulse-to-wave and wave-to-pulse conversion operations

• Pulse to wave : the average membrane potential results from passive

integration of PPS’s related to afferent AP’s (mainly at the dendrites) → represented by a second order transfer function of impluse response given by (excitatory case)

at e

Aate t u t h

−

= ). ( ) (

he(t) AP PSP

) t ( z a ) t ( z a ) t ( x Aa ) t ( z ) t ( z ) t ( z

2 1 1 1

2 − − = = & &

• Wave to pulse : the average density of action potentials fired by the

neurons depends on a nonlinear transform of the average membrane potential (threshold + saturation effect) → represented by the sigmoid function

) (

1 2 ) (

v v r

e e v S

−

+ =

S(v) AP PSP

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Pulse-to-wave and wave-to-pulse conversion operations

• Pulse to wave : the average membrane potential results from passive

integration of PPS’s related to afferent AP’s (mainly at the dendrites) → represented by a second order transfer function of impluse response given by (excitatory case)

at e

Aate t u t h

−

= ). ( ) (

he(t) AP PSP

) t ( z a ) t ( z a ) t ( x Aa ) t ( z ) t ( z ) t ( z

2 1 1 1

2 − − = = & &

t (ms) Average potential (mv)

Average EPSP Average IPSP

• Wave to pulse : the average density of action potentials fired by the

neurons depends on a nonlinear transform of the average membrane potential (threshold + saturation effect) → represented by the sigmoid function

) (

1 2 ) (

v v r

e e v S

−

+ =

S(v) AP PSP

v (mV) S (v) (v0, e0)

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Block diagram, equations and generated signals

excitatory inhibitory excitatory inhibitory

Main cells (Pyramidal) Inhibitory interneurons Main cells (Pyramidal) Inhibitory interneurons

S(v) he(t)

C1 C2

S(v) S(v) he(t)

C3 C4

he(t) hi (t) p(t) + + +

• Model output

EPSP EPSP IPSP IPSP

Nonlinear dynamical system (ODEs)

{ } { }

& ( ) ( ) & ( ) ( ) ( ) ( ) & ( ) ( ) & ( ) ( ) [ ( )] ( ) ( ) & ( ) ( ) & ( ) ( ( ) ( ) ( ) y t y t y t AaS y y ay t a y t y t y t y t Aa p t C S C y t ay t a y t y t y t y t Bb C S C y t by t b y t

3 3 1 2 3 2 1 4 4 2 1 4 2 1 2 5 5 4 3 5 2 2

2 2 2 = = − − − = = + − − = = − −

input

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Block diagram, equations and generated signals

excitatory inhibitory excitatory inhibitory

Main cells (Pyramidal) Inhibitory interneurons Main cells (Pyramidal) Inhibitory interneurons

S(v) he(t)

C1 C2

S(v) S(v) he(t)

C3 C4

he(t) hi (t) p(t) + + +

• Model output

EPSP EPSP IPSP IPSP

Simulated signal (~LFP) Nonlinear dynamical system (ODEs)

input

Time (s)

Amplitude (a.u)

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Single population model

Model configuration :

Single population + progressive increase of the E/I ratio (excitation/inhibition)

Similarity with real intracerebral EEG signals

Main cells (Pyramidal) Inhibitory interneurons

Wendling et al., Biol. Cyb., 2000

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Model of multiple coupled populations

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Introduction of a recurrent

connection: isolated spikes sustained discharges of spikes

Influence of couplings

Model configuration :

3 populations, unidirectional couplings: isolated spikes propagate from P1 to P3

Real intracerebral EEG

signals recorded during seizure (TLE) Wendling et al., Biol. Cyb., 2000

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Exemple of model simulation 1 2

Legends E/I + : increase of the Excitation/Inhibition ratio C+ : increase of the coupling from P1 to P2 Legends E/I + : increase of the Excitation/Inhibition ratio C+ : increase of the coupling from P1 to P2

26 excitatory inhibitory excitatory inhibitory

Main cells (Pyramidal) Inhibitory interneurons Main cells (Pyramidal) Inhibitory interneurons

Bifurcation diagram

• However some activities are not represented in the model (fast onset activity)
• Simulated signals exhibit properties similar to those of real signals
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• 20
• 10

10 20 30 5 10 15 20 25 30 35 40 45 50

A = 5 mV

• Max. amplitude of model
• utput signal

B (mV)

Bkg activity Sporadic spikes Rhythmic spikes α-like activity Bkg activity

Excitation = cste Decreasing Inhibition

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Fast onset activity

SEEG recording

1 sec

Seizure onset

Wendling et al., Brain, 2003

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+ + + + _ _

Pyramidal cells Interneurons _

+ + + + + + + _ _

Pyramidal cells Interneurons _

Data related to the topic

2) Somatic interneurons activity (GABAA,fast circuit ) is depressed by that of dendritic interneurons (GABAA,slow ) → nested rhythms (Banks, Neuron 2000) 1) The generation of gamma-band activities is probably linked to the behavior of interneurons (« inhibition- based rhythms ») (Traub, Jefferys, …, 1999) 3) In the experimental model of focal epilepsy (kainate acid), the alteration of GABAergic inhibition is not uniform: dendritic-projecting interneurons are altered whereas perisomatic inhibition is preserved

(Cossart et al., Nature Neurosc. 2001)

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From generic to specific model

Wendling et al., European J. Neurosc., 2002, J. Clin Neurophysiol. 2005

Inhibitory interneurons Main cells (Pyramidal)

dendritic somatic

Inhibitory interneurons

Main cells (Pyramidal) Inhibitory interneurons Main cells (Pyramidal) Inhibitory interneurons

SDI FSI EXC Fast (FSI) Slow (SDI)

excitatory inhibitory

EXC EXC EXC

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Human HIP – Background interictal activity Model – Normal activity Human HIP – Pre-onset activity Model – Sporadic spikes

1 sec 1 sec 10 Hz Normalized PSD Frequency (Hz) Normalized PSD

0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70

a) b)

Simulated activity vs Real activity (interictal)

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Simulated activity vs Real activity (ictal)

Human HIP – Fast onset activity Model – Fast activity (β, low γ) Human HIP – Ictal activity Model – Narrow band activity (θ, α) Human HIP – Ictal activity Model – Rhytmic spiking activity (θ)

1 sec 1 sec 1 sec

0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70 0 10 20 30 40 50 60 70

c) d) e)

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Transitions of dynamics

Hippocampus

Seizure Background activity Pre-ictal activity fast slower

Goal: interpret, using the model, observed transitions as a function excitation- and inhibition-related parameters (EXC, SDI, FSI)

Parameter sensitivity analysis

Temps (s)

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Parameter space and classes of simulated signals

(EXC,SDI,FSI)

Somatic inhibition (FSI) Dendritic inhibition (SDI)

EXC=3 EXC=3.5 EXC=4 EXC=4.5 EXC=5 EXC=5.5 EXC=6 EXC=6.5 EXC=7

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Interictal → ictal transition: model-based interpretation

1 2 3 4

Somatic inhibition (FSI) Dendritic inhibition (SDI)

1 2 3 4

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Simulation of the ‘interictal to ictal’ transition

EXC = constant (but increased w.r.t. ‘normal’ value) Model parameters Simulated signal PSD (V²/Hz)

Dendritic inhibition Somatic inhibition

SDI FSI Frequency (Hz)

Wendling et al., EJN, 2002

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Real vs simulated signals

1 2 3 4 5

SDI FSI EXC = 5 mV

5 s 5 s

1 2 3 4 5

Normalized power Frequency (Hz) Simulation Human HIP Real Simulated

1 2 3 4 5

Adapted from: Suffczynski P, Wendling F, Bellanger J-J, Lopes Da Silva FH, Some insights into computational models of (Patho)physiological brain activity. Proceedings of the IEEE 94(4):784- 804, 2006

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Simulated EEG for the identified scenario

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Insights from the « hippocampus » model

• The transition from interictal to ictal activity is explained by a “gradual”

decrase of inhibition at the level of PYR cell dendrites

• The model reproduces a sequence of “classical” electrophysiological

patterns observed in MTLE: Bkg activity spiking activity fast onset activity ictal activity

• Necessary conditions to generate fast onset activity:
• 1. Increased excitatory drive (PYRPYR & PYRIN)
• 2. Decreased inhibitory drive on the dendrites of PYR cells
• 3. Preserved inhibitory drive on the perisomatic region of PYR cells
• An experimental validation was reported recently by M. de Curtis’ team

(Ann. Neurol. 2008) Fast onset activity = reflection of fast IPSPs on PYR cells represented by the fast feedback inhibitory loop

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Gnatkovsky et al., Ann. Neurol. 2008

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• Confirmation of particular experimental results
• alteration of interneurons (targeting the dendrites of Pyr cells)
• role of inhibitory interneurons (targeting the soma of Pyr cells) in the

generation of fast oscillations

• Macroscopic level of the model (population) nature of real EEG signals

(intracerebral macroelectrodes).

• Class of models can be specifically adapted to explored brain structures

(ex: hippocampus) or macrocircuits (thalamo-cortical loop, olfactory system)

Discussion about the « macroscopic approach »

However

• Several structures are often involved simultaneously (hippocampus-

entorhinal cortex system in MTLE)

• Identified parameters remain « macroscopic » (excitation, inhibition)
• Non-invasive data (scalp EEG, MEG) also contain relevant information

Work in progress

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Work in progress

• Several structures are often involved simultaneously (hippocampus-

entorhinal cortex system in MTLE) 1) Towards « larger scale models »

• Identified parameters remain « macroscopic » (excitation, inhibition)

2) From « population » models to « detailed » models

• Non-invasive data (scalp EEG, MEG) also contain relevant information

3) Relationships between scalp and intracerebral data

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+ Entorhinal Cortex Superficial layers Deep layers Entorhinal Cortex Superficial layers Deep layers Dentate Gyrus HIP CA3 Subiculum

Mossy Fibers

HIP CA1

• +

(FF) -

Schaffer Collaterals

+ (FF)

• +

(FF) - +

• (FF)

+

• (FF)

+ (FF) - + (FF) -

• (FF)

+ (FF) - + +

• +
• +

+ temporo-amonic pathway Perforant pathway

1) Towards « larger scale models » (brain region)

Deep layers (V-VI) Superficial layers (I-II-III) P1 P2 P2 Stellate N GABAa slow GABAa slow GABAa fast GABAa fast IN Gly IN Gly Excitatory IN Excitatory IN Excitatory IN Excitatory IN GD - CA3 Subiculum – CA1 Subiculum Subiculum Cortex + subiculum

Lamina dissecans

GABAa slow GABAa slow GABAa fast GABAa fast GABAb GABAb GABAb GABAb

• +

+ + + + + + + + + + + + + + + + + + + + + +

• -

+

• Inhibitory

interneurons Main cells (Pyramidal)

dendritic somatic

Inhibitory interneurons

Hippocampus Entorhinal cortex Objective: To study the role of the HIP-EC « closed-loop » system in MTLE

Lopes da Silva FH, Witter MP, Boeijinga PH, and Lohman AH. Anatomic organization and physiology of the limbic cortex. Physiol Rev 70: 453-511, 1990. Witter MP. Organization of the entorhinal-hippocampal system: a review of current anatomical data. Hippocampus 3 Spec No: 33-44, 1993.

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1) Entorhinal cortex model and evaluation on experimental data

COMPUTATIONAL MODEL ENTORHINAL CORTEX (isolated brain)

Labyt et al., J. Neurophysiol, 2006, IEEE TITB 2007

Collaboration Institut C. Besta, Milan

Klaus Goldbrandsen Deep layers (V-VI) Superficial layers (I-II-III) P1 P2 P2 Stellate N GABAa slow GABAa slow GABAa fast GABAa fast IN Gly IN Gly Excitatory IN Excitatory IN Excitatory IN Excitatory IN GD - CA3 Subiculum – CA1 Subiculum Subiculum Cortex + subiculum

Lamina dissecans

GABAa slow GABAa slow GABAa fast GABAa fast GABAb GABAb GABAb GABAb

• +

+ + + + + + + + + + + + + + + + + + + + + +

• -

+

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1) Entorhinal cortex model and evaluation on experimental data

COMPUTATIONAL MODEL ENTORHINAL CORTEX (isolated brain)

Role of inhibition (GABAa receptors ) // experimental protocol (bicuculline)

Labyt et al., J. Neurophysiol, 2006, IEEE TITB 2007

Collaboration Institut C. Besta, Milan

Klaus Goldbrandsen Deep layers (V-VI) Superficial layers (I-II-III) P1 P2 P2 Stellate N GABAa slow GABAa slow GABAa fast GABAa fast IN Gly IN Gly Excitatory IN Excitatory IN Excitatory IN Excitatory IN GD - CA3 Subiculum – CA1 Subiculum Subiculum Cortex + subiculum

Lamina dissecans

GABAa slow GABAa slow GABAa fast GABAa fast GABAb GABAb GABAb GABAb

• +

+ + + + + + + + + + + + + + + + + + + + + +

• -

+

• Deep layers

Superficial layers

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Work in progress

• Several structures are often involved simultaneously (hippocampus-

entorhinal cortex system in MTLE) 1) Towards « larger scale models »

• Identified parameters remain « macroscopic » (excitation, inhibition)

2) From « population » models to « detailed » models

• Non-invasive data (scalp EEG, MEG) also contain relevant information

3) Relationships between scalp and intracerebral data

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2) From « population » models to « detailed » models

Objective : to interpret observations as a function of cellular parameters (epilepsy and « channelopathy ») Methods:

• Detailed models (networks) // population models
• Cell level: ion channels (Hodgkin & Huxley), membrane receptors

+ + + + _ _

Pyramidal cells Interneurons _

+ + + + + + + _ _

Pyramidal cells Interneurons _

Neuronal population Neuronal networks (~ 104 Cell.)

Field activity (~ intracerebral EEG) Unit activities

1 sec 500 msec

?

0 mV

• 65 mV

Dendrite s

Soma Idendrite Isoma INaP IKS Ileak INa

+

IK

+

Ileak IC IKAH

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2) From « population » models to « detailed » models

Objective : to interpret observations as a function of cellular parameters (epilepsy and « channelopathy ») Methods:

• Detailed models (networks) // population models
• Cell level: ion channels (Hodgkin & Huxley), membrane receptors

+ + + + _ _

Pyramidal cells Interneurons _

+ + + + + + + _ _

Pyramidal cells Interneurons _

Neuronal population Neuronal networks (~ 104 Cell.)

Field activity (~ intracerebral EEG) Unit activities

1 sec 500 msec

?

0 mV

• 65 mV

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2) Neural network model: main features

b) x y z

DG CA3 CA1 1 mm

a)

PYR OLM BAS Inhibitory drive (GABA) Excitatory drive (AMPA, NMDA) Excitatory drive (AMPA)

• Hippocampus, CA1 subfield, PYR, OLM & basket cells

V

i

d r

i

u r

x y z c)

• Reconstruction of the field activity (forward problem, dipole theory)

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Electrode

Stimulation of the network

• Simulation of volley of afferent APs on randomly-selected cells (from CA3)

Membrane potential (mV) Time (ms)

ΔT Stimulation parameters:

• Number of stimulated cells in the network
• Variance of the delay between afferent APs

DG CA3 CA1 1 mm

Demont-Guignard et al., IEEE Conf. Neural Engineering, 2009

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2) Simulated activity in « hyperexcitable » networks

For ALL pyramidal cells

• Increased conductances NMDA- and AMPA-mediated synaptic currents
• increased reversal potential of GABA-mediated synaptic currents (-70 to -50 mV)

Simulated Local Field Potential 100 ms Inhibitory Interneurons (OLM) Pyramidal cells Inhibitory Interneurons (BAS) Intracellular activity

1 sec 200 µV

Real data (depth-EEG, HIP)

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Work in progress

• Several structures are often involved simultaneously (hippocampus-

entorhinal cortex system in MTLE) 1) Towards « larger scale models »

• Identified parameters remain « macroscopic » (excitation, inhibition)

2) From « population » models to « detailed » models

• Non-invasive data (scalp EEG, MEG) also contain relevant information

3) Relationships between scalp and intracerebral data

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EEG (surface) Depth-EEG (intracerebral)

F7-F3 F3-Fz Fz-F4 F4-F8 FT9-FC5 FC5-FC1 FC2-FC6 FC6-FT10 T3-C3 C3-Cz Cz-C4 C4-T4 TP9-CP5 CP5-CP1 CP2-CP6 CP6-FT10 T5-P3 P3-Pz Pz-P4 P4-T6

A’ B’ C’ TP’ H’ TB’ GC’ B

1 sec 1 sec

? ? ? 3) Relationship between scalp and intracerebral data

Électrode intracérébrale multi-capteurs

C’ B’ A’ C’ B’ A’

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Cerveau Scalp Electrode SEEG Electrodes EEG

• Extended source: dipole layer + neuronal

population model

• Realistic head model (IRM)
• Electrical potentials :

Forward problem (sourcessensors)

Population neuronale (LFP) Moment ~ LFP

Cosandier-Rimélé et al., IEEE TBME 2007

Réel Simulé

Capteurs latéraux Capteurs mésiaux

Comparaison

1 2 3 4 5 6 7 8 9 10 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300

Réel Simulé Réel Simulé Capteur Amplitute ( μV)

3) Modeling of scalp and intracerebral EEG

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Simulated data (EEG/MEG)

MEG

• Parametric study of the model : influence of source parameters related to

space and time (extension of sources, position, synchronisation degree)

temps

EEG

Real data (EEG/MEG)

MEG EEG

3) Model-based evaluation of localization methods

Method 1 Method 2 Method 3 Method 4

Simulated EEG signals

Localization results

Cosandier-Rimélé et al., Neuroimage 2008

Albera et al., TBME, 2008

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General conclusion

Approach combining signal processing and modeling in order to interprete the observations and to identify some pathological mechanisms “Epilepsy is a complex dynamical disease” (F. Lopes da Silva) Intervalidation with experimental models is required (intimate link between models and experiments ) Open questions

Development of « multi-level » approaches Relationship between the sources of activity and the signals that are collected on sensors (forward problem, biophysics) The use of multimodal data (fMRI, EEG, MEG, depth-EEG) in epilepsy

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Thank you for your attention

Wendling F, Expert Review

• f Neurotherapeutics

2008