EEG-data assimilation for the resting human brain

Takumi Sase (Research Scientist)

Keiichi Kitajo(Unit Leader)

Rhythm-based Brain Information Processing Unit

RIKEN BSI-TOYOTA Collaboration Center

RIKEN Brain Science Institute

Overview

Introduction Motivation

Research Strategy1st stage

2nd stage 3rd stage

Conclusions

Results

Our previous work

What is EEG:

Keywords: Resting state, Metastability

Micro- to Macro-dynamics in the brain

Vareta et al., Nat. Rev. Neurosci., 2001

micro

macroH

iera

rch

ical le

ve

l

Introduction

LFPs

iEEG

EEG

Single units

EEG LFPs

Spontaneous macro-dynamics

https://en.wikipedia.org/wiki/Resting_state_fMRI

Lehmann, Scholarpedia, 2009

Lee et al., PLOS ONE, 2012

clustering

Introduction

Koenig et al., NeuroImage, 2002

clustering

Mathematical view of spontaneous brain

No fluctuation Driven by fluctuations

Torus

Limit cycle

Chaos

Fixed point

Tsuda, 2016

Introduction

Note: Tsuda is proposing chaotic itinerancy as a possible mechanism of spontaneous brain activity.

Metastable states

Spontaneous and evoked brain dynamics

Luczak et al., Neuron, 2009

Spontaneous

Introduction

Evoked

Execute a task

Tsuda, 2016

Our previous studySase & Kitajo, in preparationMotivation

Pote

ntia

l ene

rgy

Pote

ntia

l energ

y

Our previous studyMotivation Sase & Kitajo, in preparation

Pote

ntia

l energ

y

Pote

ntial energ

y

Tra

nsitio

ns

am

on

g 3

tori

Metastable state0 100 200 300 400 500 600 700 800

Time (s)

This study crosses over 3 stageshttps://en.wikipedia.org/wiki/Kuramoto_model

Lehmann, Scholarpedia, 2009

))()(sin()(

))()(sin(1

tθtψtKrω

tθtθN

Kω

dt

dθ

ii

N

j

ijii

N

j

tθtψ

tψ

j

Ntr

trtEEG

1

)(i)(i

)(i

e1

)e(where

,)e(Re)(

　

Freeston et al., Front. Neurosci., 2014

Jansen & Rit, Biol. Cybern., 1995

Drive!

Phases ofNeural mass model (NMM)

Drive!

1st

2nd

3rd

Research Strategy

NMM can produce alpha wave (8-12 Hz)

Jansen & Rit, Biol. Cybern., 1995 Grimbert & Faugeras, Neural Comput., 2006

Disturbance

1st stage

Results:1st stage

Drive!

Freeston et al., 2014 https://en.wikipedia.org/wiki

/Kuramoto_model

Master EEG

Slave EEG

Var = 3.92

Var = 0.0412

Var = 8.17

Var = 0.962

Grimbert & Faugeras,

2006

Grimbert & Faugeras,

2006

Disturbance Disturbance

<

<

Results:

R = 0.603 (P < 0.001)

Phases Drive!

2nd stage

PLF

K

https://en.wikipedia.org/wiki

/Kuramoto_modelLehmann, 2009

Functional connectivity

Master EEG phase

Slave EEG phase

Connection of previous study to DA3rd stage

2-tori driven by fluctuations

Limit cycles driven by fluctuations

10 Hz

0.3 HzE

EG

EE

G

Po

we

rP

ow

er

Frequency (Hz)

Frequency (Hz)

Setting for DA3rd stage

)(

1

));();(sin(sN

j

ijijiji βstθstθJω

dt

dθ

)(

1

);(i);(i

);(i

e)(

1)e;(where

,)e;(Re);(

sN

j

stθstψ

stψ

j

sNstr

strstEEG

　jiijiijiijii βββJJJ ,0,,0

N(s):

s: update index

2 3 4 50

Constraints

{Jij}

&

{βij}

s=s+1 s=s+1:s=0

Results: Drive!

3rd stage

{Jij}

{βij}

N(s)=2

N(s)=50

https://en.wikipedia.org/wiki

/Kuramoto_modelLehmann, 2009

Master EEG

Slave EEG

EE

Gs

EE

Gs

EE

Gs

EE

Gs

Results:3rd stage

Time (s)

https://en.wikipedia.org/wiki/Kuramoto_modelS

imula

ted E

EG

Sim

ula

ted E

EG

Sim

ula

ted E

EG

Sim

ula

ted E

EG

Sim

ula

ted E

EG

Discussion3rd stage

Chaotic attractor

……

Summary

• The Kuramoto model (KM) driven by the Neural mass model (NMM)suggests that the KM, considering mesoscopic oscillators underlyingEEG differently from the NMM, considering only macroscopicelements, may be suitable for DA study.

• The KM driven by the phase of EEG suggests that the estimatedcoupling strength, K, clearly reflects functional connectivity on EEGdynamics.

• The KM driven by EEG suggests that metastable dynamics, revealedfrom our previous study, may be ideally phase chaos, which cannotbe observed from the human brain.

Conclusions

Future workConclusions

Vareta et al., Nat. Rev. Neurosci., 2001

https://en.wikipedia.org/wiki/Kuramoto_model

…

Individual 1

…

Individual 2

{Jij(1)} {Jij

(2)}

Jij