Realistic modeling and interpretation of depth-EEG signals ...

1

Realistic modeling and interpretation of depth-EEG signals recorded

during inter-ictal to ictal transition in temporal lobe epilepsy

F. WendlingINSERM U642 - University of Rennes

Laboratory of Signal and Image ProcessingRennes – France

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

LTSI

International Workshop on Advanced Epilepsy Treatment - CADET 2009 – 28-30 March, 2009Kitakyushu 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

3

Electrophysiological observations

Neuron

Neuronal population

Cerebral structure

Cerebral region

Brain

Cell

Organ

Experimental models (animals)

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

Preictalactivity

Fast onsetactivity

Ictal burstactivity

Ictal burstactivity

(slower frequency)

Seizurestart

Seizuretermination

Backgroundactivity

…

…

…

…

Sup

erfic

ialE

CD

eep

EC

5 sKlaus

Goldbrandsen

Neurological InstituteCarlo Besta, MilanM. 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 explorationSEEG exploration





Epilepsy Unit, CHR La timone, Marseille

4

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 eventsand ruptures of dynamics (more or less abrupt)

5

Amygdala

Ant. hippocampus

Post. hippocampus

Entorhinal cortex

5 sec

Interictal and pre-onset activity (TLE)Depth-EEG

6

Amygdala

Ant. hippocampus

Post. hippocampus

Entorhinal cortex

Seizure onset5 sec

Depth-EEG

7

Amygdala

Ant. hippocampus

Post. hippocampus

Entorhinal cortex

Ictal activity5 sec

Depth-EEG

8

Interictal / ictal transition

Ant. hippocampus

Post. hippocampus

Amygdala

Entorhinal cortex

9

Ant

. hip

.P

ost.

hip.

Power spectral densities

10

Ant

. hip

.P

ost.

hip.

Ant

. hip

.P

ost.

hip.

Interictal

PSD(V²/Hz)

f (Hz)

PSD(V²/Hz)

f (Hz)

OnsetPSD(V²/Hz)

f (Hz)

PSD(V²/Hz)

f (Hz)

IctalPSD(V²/Hz)

f (Hz)

PSD(V²/Hz)

f (Hz)

Power spectral densities

11

Time-frequency representationHIP

Freq

uenc

y(H

z)

5 s

Time (s)

?

Approach : physiological modeling of depth-EEG signals

12

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

13

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

14

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 brainchaos, 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 orderordinarydifferentialequation

16

Example : Freeman ’s model (2/2)

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

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excitatoryinhibitory

Main cells(Pyramidal)

Inhibitoryinterneurons

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 PSPAfferentinput

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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 impluseresponse given by (excitatory case) at

e Aatetuth −= ).()(

he(t)AP PSP)t(za)t(za)t(xAa)t(z

)t(z)t(z2

11

1

2 −−=

=

&

&

- Wave to pulse : the average density of action potentials fired by theneurons depends on a nonlinear transform of the average membrane potential (threshold + saturation effect)

→ represented by the sigmoid function

)(0

012)( vvre

evS −+=S(v) APPSP

19

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 impluseresponse given by (excitatory case) at

e Aatetuth −= ).()(

he(t)AP PSP)t(za)t(za)t(xAa)t(z

)t(z)t(z2

11

1

2 −−=

=

&

&

t (ms)

Ave

rage

pote

ntia

l(m

v)

Average EPSP

Average IPSP

- Wave to pulse : the average density of action potentials fired by theneurons depends on a nonlinear transform of the average membrane potential (threshold + saturation effect)

→ represented by the sigmoid function

)(0

012)( vvre

evS −+=S(v) APPSP

v (mV)

S(v

)

(v0, e0)

20

Block diagram, equations and generated signals

excitatoryinhibitoryexcitatoryinhibitory





S(v) he(t) C1C2

S(v)

S(v) he(t) C3C4

he(t)

hi (t)

p(t) +++- Model output

EPSPEPSP

IPSPIPSP

Nonlinear dynamical system (ODEs)

{ }

{ }

& ( ) ( )& ( ) ( ) ( ) ( )& ( ) ( )& ( ) ( ) [ ( )] ( ) ( )& ( ) ( )& ( ) ( ( ) ( ) ( )

y t y t

y t AaS y y ay t a y ty t y ty t Aa p t C S C y t ay t a y ty t y t

y t Bb C S C y t by t b y t

0 3

3 1 2 32

0

1 4

4 2 1 0 42

1

2 5

5 4 3 0 52

2

2

2

2

=

= − − −=

= + − −

=

= − −

input

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Block diagram, equations and generated signals






S(v) he(t) C1C2

S(v)

S(v) he(t) C3C4

he(t)

hi (t)

p(t) +++- Model output

EPSPEPSP

IPSPIPSP

Simulated signal (~LFP)

Nonlinear dynamical system (ODEs)

input

Time (s)

Am

plitu

de (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



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

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Bifurcation diagram

• However some activities are not represented in the model (fast onset activity)

• Simulated signals exhibit properties similar to those of real signals

-30

-20

-10

0

10

20

30

0 5 10 15 20 25 30 35 40 45 50

A = 5 mV

Max

. am

plitu

de o

fmod

elou

tput

sig

nal

B (mV)

Bkg activitySporadic spikesRhythmic 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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+

+++

_

_

Pyr

amid

al c

ells

Inte

rneu

rons

_

+

+++ +++

_

_

Pyr

amid

al c

ells

Inte

rneu

rons

_

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



dendritic somatic






SDI

FSI

EXC

Fast (FSI)Slow (SDI)

excitatoryinhibitory

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

Nor

mal

ized

PSD

Frequency (Hz)

Nor

mal

ized

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)

31

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 dynamicsH

ippo

cam

pus

SeizureBackground activity Pre-ictal activity

fastslower

Goal: interpret, using the model, observed transitionsas a function excitation- and inhibition-relatedparameters (EXC, SDI, FSI)

Parameter sensitivity analysis

Temps (s)

33

Parameter space and classes of simulated signals

(EXC,SDI,FSI)

Som

atic

inhi

bitio

n (F

SI)

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

Som

atic

inhi

bitio

n (F

SI)

Dendritic inhibition (SDI)

123

4

35

Simulation of the ‘interictal to ictal’ transition

EXC = constant (but increased w.r.t. ‘normal’ value)

Mod

elpa

ram

eter

sS

imul

ated

sign

alP

SD

(V²/H

z)

Dendritic inhibitionSomatic inhibitionSDI

FSI

Frequency (Hz)Wendling et al., EJN, 2002

36

Real vs simulated signals1 2 3 4 5

SDI FSIEXC = 5 mV

5 s

5 s

1 2 3 4 5

Nor

mal

ized

pow

er

Frequency (Hz)

Sim

ulat

ion

Hum

anH

IP

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 (PYR PYR & PYR IN)2. Decreased inhibitory drive on the dendrites of PYR cells3. 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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KlausGoldbrandsen

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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+

EntorhinalCortex

Superficiallayers

Deeplayers

EntorhinalCortex

Superficiallayers

Deeplayers

Dentate Gyrus

HIPCA3

Subiculum

Mossy Fibers

HIPCA1

-

+(FF) -

SchafferCollaterals

+(FF)

-

+ (FF) -

+ -

(FF)

+ -

(FF)

+(FF) -

+(FF) -

- (FF)+

(FF) -+

+-

-

+-

+

+

temporo-amonic pathwayPerforant pathway

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

Dee

pla

yers

(V-V

I)S

uper

ficia

llay

ers

(I-II-

III)

P1

P2P2

Stellate N

GABAa slowGABAa slow

GABAa fastGABAa fast

IN GlyIN Gly

Excitatory INExcitatory IN

Excitatory INExcitatory INGD - CA3

Subiculum – CA1

Subiculum

Subiculum

Cortex + subiculum

Lamina dissecans

GABAa slowGABAa slowGABAa fastGABAa fast

GABAbGABAb

GABAbGABAb

--++

++

++

++

++

++

++++

++

++ ++

++

++

++

++++

++

++

++ ++

++

++

++

--

------

----

--

-- --

-- --

++ --

--



dendritic somatic


Hip

poca

mpu

s

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

KlausGoldbrandsen

Dee

pla

yers

(V-V

I)Su

perfi

cial

laye

rs(I-

II-III

)

P1

P2P2

Stellate N



IN GlyIN Gly



Subiculum – CA1

Subiculum

Subiculum

Cortex + subiculum

Lamina dissecans


GABAbGABAb

GABAbGABAb

--++

++

++

++

++

++

++++

++

++ ++

++

++

++

++++

++

++

++ ++

++

++

++

--

------

----

--

-- --

-- --

++ --

--

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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

KlausGoldbrandsen

Dee

pla

yers

(V-V

I)Su

perfi

cial

laye

rs(I-

II-III

)

P1

P2P2

Stellate N



IN GlyIN Gly



Subiculum – CA1

Subiculum

Subiculum

Cortex + subiculum

Lamina dissecans


GABAbGABAb

GABAbGABAb

--++

++

++

++

++

++

++++

++

++ ++

++

++

++

++++

++

++

++ ++

++

++

++

--

------

----

--

-- --

-- --

++ --

--

Dee

pla

yers

Sup

erfic

iall

ayer

s

45

Work in progress







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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

+

+++

_

_

Pyr

amid

al c

ells

Inte

rneu

rons

_

+

+++ +++

_

_

Pyr

amid

al c

ells

Inte

rneu

rons

_

Neuronalpopulation

Neuronalnetworks

(~ 104 Cell.)

Field activity (~ intracerebral EEG)

Unit activities

1 sec

500 msec

?0 mV

-65 mV

Dendrites

Soma

Idendrite

Isoma

INaP

IKS

Ileak

INa+

IK+

Ileak

IC

IKAH

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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

+

+++

_

_

Pyr

amid

al c

ells

Inte

rneu

rons

_

+

+++ +++

_

_

Pyr

amid

al c

ells

Inte

rneu

rons

_

Neuronalpopulation

Neuronalnetworks

(~ 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

zDG

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

idr

iur 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)

Mem

bran

e po

tent

ial(

mV)

Time (ms)

ΔT

Stimulation parameters:

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

DGCA3

CA1

1 mm

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

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2) Simulated activity in « hyperexcitable » networksFor ALL pyramidal cells- Increased conductances NMDA- and AMPA-mediated synaptic currents - increased reversal potential of GABA-mediated synaptic currents (-70 to -50 mV)

SimulatedLocal Field Potential

100 msInhibitoryInterneurons

(OLM)

Pyramidal cells

InhibitoryInterneurons

(BAS)

Intra

cellu

lara

ctiv

ity

1 sec200 µV

Real data (depth-EEG, HIP)

51

Work in progress







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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’

B1 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 + neuronalpopulation model• Realistic head model (IRM)• Electrical potentials : Forward problem (sources sensors)

Population neuronale (LFP)

Moment ~ LFP

Cosandier-Rimélé et al., IEEE TBME 2007

Réel Simulé

Cap

teur

s la

téra

uxC

apte

urs

més

iaux

Comparaison

1 2 3 4 5 6 7 8 9 100

100

200

300

400

500

600

700

800

900

1000

1100

1200

1300

RéelSimuléRéelSimulé

Capteur

Am

plitu

te( μ

V)

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)

tem

ps

EEG

Real data (EEG/MEG)

MEGEEG

3) Model-based evaluation of localization methods

Method 1

Method 2

Method 3

Method 4

Simulated EEG signals

Loca

lizat

ion

resu

lts

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

56Thank you for your attention

Wendling F, Expert Review

of Neurotherapeutics2008

Realistic modeling and interpretation of depth-EEG signals ...

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