Dr I. BojakSection Neurophysiology and Neuroinformatics

http://www.neuropi.org

Computational brain models of EEG / MEG and fMRI signals in health and disease

#slides• Multimodal• Mean fields• BRAINSPECS• Borromean Rings• Conclusions

• 3• 6• 2• 1• 1

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

Multimodal – the six blind men and the elephant

Pics - EEG: Brain Sci. Institute Swinburne; MEG: Dept. of Psychology NYU; fMRI: Dept. Cog. Neurology, MPI Leipzig; SPECT: C. Studholme UCSF; PET: N.D. Volkov et al.; Anatomy: NTVH MRI Lab; Poem: Wordinfo.

SPECT

EEG

MEG

fMRI

PET

anatomy

3

Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

Multimodal – why EEG / MEG and fMRI first?

• EEG/MEG and fMRI are complementary modalities:

• EEG and fMRI can be recorded simultaneously• EEG and MEG can be recorded simultaneously• MEG and fMRI are however technologically incompatible

4

Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

• Activity regions for different modalities are not identical• Correlation picks out regions not prominent in single modalities• Correlated activity regions are much more localized

Multimodal – correlations are not enough

M. Schulz, W. Chau, S.J. Graham, A.R. McIntosh, B. Ross, R. Ishii, and C. Pantev, “An Integrative MEG-fMRI study of the primary somatosensory cortex using cross-modal correspondence analysis” , NeuroImage 22 (2004) 120-133.

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

Mean fields – sources for non-invasive imaging

• “in phase” neurons contribute , “out of phase” • 105 neurons, 1% “in phase”: 32x stronger signal – seen only.• Imaging behaviour neuronal mass action

averaging mean field theories

6

Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

Mean fields – our model

flattened

simplified

averaged spatially

7

Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

• 145 Dell Power Edge 1950 blades: 2 x quad-core 2.33 GHz Clovertown, 16 GB RAM

• 145 TB blade hard drives, 100 TB Raid 5 disks, 77 TB robot tape

• Cisco 6509 gigabit ethernet about to be upgraded to 20Gb/s infiniband

• 4 -128 nodes run in parallel using MPI Fortran

Mean fields – whole cortex computing

0framesdisk

10noise

1 2

4 5 6

3

7 8 9Linux: CentOS 5,queue: PBS,manager: Torque,scheduler: Moab,compilers: Intel 9.1.

Green Machine

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

• PSP response under Isoflurane :

Mean fields – works well for EEG, e.g., anesthesia

Banks & Pearce, MacIver et al.

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req

ue

ncy

[H

z]

0

6

.0

12

.0

1

8.0

24

.0

0 0.13 0.27 0.40

wavelength-1 [cm-1]

MAC

EEG ~ mean excitatory soma membrane potential

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

Mean fields – how to get fMRI BOLD contrast

• Assume that neurovascular coupling is due to the uptake of intracellular glutamate from excitatory synapses (plus sodium) into astrocytes, resulting eventually in the glycolysis of ATP.

• Hence the root cause of the Blood Oxygen Level-Dependent signal is proportional to excitatory synaptic activity.

• Excitatory pulses:

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

• Isotropic, homogeneous, exp. connectivity:

• But there’s also specific one:

Mean fields – how to implement connectivity?

# synapses

Felleman & Van Essen

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

BRAINSPECS – the proposal

• BRain Activity Imaging and Network Simulations for the Prediction and Evaluation of Clinical Syndromes - a personalizable brain model of EEG/MEG and fMRI signals in health and disease (Integrating Project for FP7-ICT-2007-2)

• 40 principal researchers, budget € 9.5 million, 5 years runtime

Nijmegen Amsterdam London Cambridge Barcelona Warsaw Sankt Augustin Ås Lausanne

iel Stockholm

1

SME Localite 1

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

BRAINSPECS – working packages

projection software

main fieldprograms

networkprograms

data integration

connectivity database

WP3Model fitting

WP8Advanced modeling tools

WP4Local and detailed models

WP2Connectivity

WP10Data acquisition and visualization

WP9Data management and ontology

WP7Epilepsy

WP6Drug effects

WP5Lesions and dementia

data interface

connectivity constraints

visualisation interface

data access

clinical data

func

tion

al c

onne

ctiv

ity

WP1Forward and inverse modeling

com

puta

tion

al

mea

n fi

eld

para

met

ers

 experimental main field parameters

10 scientific Working Packages

WP12Project management

WP11Dissemination

clinics

experiment

theory

computing

public relations

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

experi-ment

clinics

theory

computing

clinics

computing

experiment theory

storage

I / O

crunch

hardware storage

hardware

crunch

I / O

database

GUI

code

software

technologycenter

internetportal

computeserver

P

P

P

PP

P

R

R

R

RRR

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Dr I. Bojak

Section Neurophysiology and Neuroinformatics

Computational brain models of EEG/MEG and fMRI signals in health and disease

Conclusions – assume it’s Loxodonta africana

mean field theory

EEG

MEG

fMRI

PET

SPECT

anatomy