Large scale models of the brain

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19 Οκτ 2013 (πριν από 3 χρόνια και 9 μήνες)

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Large scale models of the brain

Institut

des Sciences du
Mouvement


Viktor
Jirsa

Theoretical Neuroscience Group

Anandamohan

Ghosh

Rolf
Kötter

Randy McIntosh

Young
-
Ah Rho

Michael
Breakspear

Stuart Knock

Gustavo Deco

Honey et al PNAS 2007,
Ghosh

et al
Plos

CB 2008, Deco et al PNAS 2009

Izhikevich

&
Edelman 2008

Henry
Markram



Blue Brain

Ananthanarayanan

et al. IBM 2009

Information processing carried out by
large scale neural networks

Motivation

Mean field models collapse the dynamic characteristics of a
voxel

into a
single
neurocomputational

unit of neurons with similar statistics

Deco et al.
PLoS

CB2009

Globally coupled network of Fitzhugh
-
Nagumo neurons

Network Dynamics



Define
a continuous field
parametrized

by the dispersed
parameter



Rewrite
the network equations in terms of
q(
z,t
)

Coupled mean fields

Assisi,
Jirsa
, Kelso
PRL2005

Stefanescu
,
Jirsa

Plos

CB 2009


Express the dynamics of the network in z
-
space in terms of z
-
spatial modes and the corresponding time dependent
amplitudes.

Assisi,
Jirsa
, Kelso
PRL2005

Stefanescu
,
Jirsa

Plos

CB 2009

The mode equations are given by,

Mode Equations

Assisi,
Jirsa
, Kelso
PRL2005

Stefanescu
,
Jirsa

Plos

CB 2009

Mode Dynamics

Contour lines of equal mean field amplitude in
space

Network dynamics

Mode dynamics

Assisi,
Jirsa
, Kelso
PRL2005

Stefanescu
,
Jirsa

Plos

CB 2009

Jirsa

&
Stefanescu

Bul. Math.
Biol

(in press)

Neural field models











Full network







Reduced neural field


Origin of ultraslow fluctuations: neural activity?


Simultaneous EEG

and
fMRI

study finds cross
-
correlations between
BOLD signal and the
power fluctuations in
each frequency band.



Mantini

et al. PNAS
2007

Generation of the rest state activity? Function?


Product of
chaotic

processes

involving

the
thalamocortical

loop

(Lopes da Silva et al. 1997;
Niedermeyer

1997)


Distinct alpha
generators

(
Nunez

et al 2001)



Large
scale

connectivity

matrix

and
chaotic

neural
activity

(
Honey

et
al PNAS 2007)


Noise
driven

exploration of the
high
-
dimensional

phase
space

defined

by the network
with

time
delays

(
Ghosh

et al
Plos

CB2008)


Stochastic

Resonance

in the network
with

time
delays

(
Deco

et al
PNAS 2009)



«

Rest

state fluctuations
reflect

unconstrained

but
consciously

directed

mental
activity

»


Rest

state network fluctuations
observed

in
anaesthesized

monkeys

(Vincent et al., Nature 2007)





Regional map of the
primate
brain (
Kötter

&
Wanke
, 2005)

Monkey

Human

Ghosh et al. PLoS CB 2008

Ghosh

et al
Plos

CB 2008; Deco et al PNAS 2009

Implementation of large scale model

Assisi,
Jirsa

Kelso PRL 2005

Stefanescu
,
Jirsa

PLoS

CB 2009

Jirsa
,
Stefanescu

Bull.Math.Biol

(in press)

Linear stability analysis

linearization

Let the solution be

characteristic equation

in


For
N

coupled FHN oscillators the characteristic equation is factorizable:

Characteristic equation:

Ghosh

et al.
PLoS

CB 2008

Ghosh

et al.
PLoS

CB 2008

Hemodynamic model:
combining Balloon/Windkessel Model
with a model of how synaptic activity causes changes in regional flow

Nonlinear coupling
term:

Balloon/
Windkessel

model

Linear coupling
term:


How evoked changes in blood flow
are transformed into a blood
oxygenation level dependent(BOLD)

Case 1

Case 3

Compare to Fox et al. PNAS 2005

Resting state network in BOLD signals

Fox et
al. PNAS
(2005)


Task
-
negative regions
:
MPF(medial prefrontal cortex),
PCC(posterior cingulate
precuneus
), LP(Lateral parietal
cortex)



Task
-
positive regions
:
IPS(
intraparietal

sulcus

cortex),
FEF(the frontal eye field),
MT(middle temporal region)


CCP

FEF

PCI

PCIP

PFCM

VACD

CCP

+

+

-

+

-

+

FEF

+

+

-

-


-

+

PCI

-

-

+

+

+

-

PCIP

+

-


+

+

-

-

PFCM

-

-

+

-

+

-

VACD

+

+

-

-

-

+

Cross correlations between six
areas:
Ghosh

et al PLOS CB 2008

Compare to Fox et al. PNAS 2005

15uV

-
15uV

0 uV

400fT

-
400fT

0fT

Forward EEG/MEG solution in realistic
head models

Qf = 1, Qs = 1

Honey et al PNAS 2007;

Ghosh

et al
Plos

CB 2008;
Jirsa

Phil. Trans. Royal Soc. A 2009

What is the dynamic mechanism leading to the
emergence of these coherent fluctuations?


Synchronization?

F

FitzHugh
-
Nagumo

Neuron

Rho,
Jirsa

& McIntosh (in preparation)

Rho,
Jirsa

& McIntosh (in preparation)

BOLD in CCA is correlated with coherence between PCI and CCP, and BOLD
time series are shifted with time lag(2.4sec).

Rho,
Jirsa

& McIntosh (in preparation)

Rho,
Jirsa

& McIntosh (in preparation)

Other working points, maybe self
-
sustained
oscillations?

Deco, Jirsa, McIntosh
et al.
PNAS (2009)

Different working point:


What is the role of
synchronization?

Two clusters of synchronization

Synchronization of clusters


Red


cluster 1

Black


cluster 2

Blue


difference





Power spectrum of ultraslow
oscillations with and without time
delay








Stochastic Resonance


Cross correlation as a function
of noise level


Maximal Power as a function of
noise level

Deco, Jirsa, McIntosh
et al.
PNAS (2009)

Summary of results


Rest state activity is interpreted as the «

noise
-
driven exploration of
the equilibrium state of the brain network

»



The space
-
time structure is crucial for the emergence of the rest
state networks.



Intermittent synchronization of subnetworks gives rise to ultra
-
slow
oscillations in BOLD signal.


Codebox Research

ATIP (CNRS)

James S. McDonnell Foundation

Thank you