1. Dr I. Bojak Section 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

2. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 1 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 2 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 3 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.

5. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 4 Mean fields – sources for non-invasive imaging “in phase” neurons contribute, “out of phase” 10 5 neurons, 1% “in phase”: 32x stronger signal – seen only. Imaging behaviour neuronal mass action averagingmean field theories

6. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 5 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 6 145 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 0 frames disk 10 noise 12 456 3 789 Linux: CentOS 5, queue: PBS, manager: Torque, scheduler: Moab, compilers: Intel 9.1. Green Machine

8. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 7 PSP response under Isoflurane : Mean fields – works well for EEG, e.g., anesthesia Banks & Pearce, MacIver et al. frequency [Hz] 0 6.0 12.0 18.0 24.0 0 0.13 0.27 0.40 wavelength -1 [cm -1 ] MAC EEG ~ mean excitatory soma membrane potential

9. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 8 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:

10. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 9 Isotropic, homogeneous, exp. connectivity: But there’s also specific one: Mean fields – how to implement connectivity? # synapses Felleman & Van Essen

11. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 10 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

12. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 11 BRAINSPECS – working packages projection software main field programs network programs data integration connectivity database WP3 Model fitting WP8 Advanced modeling tools WP4 Local and detailed models WP2 Connectivity WP10 Data acquisition and visualization WP9 Data management and ontology WP7 Epilepsy WP6 Drug effects WP5 Lesions and dementia data interface connectivity constraints visualisation interface data access clinical data functional connectivity WP1 Forward and inverse modeling computational mean field parameters experimental main field parameters 10 scientific Working Packages WP12 Project management WP11 Dissemination clinics experiment theory computing public relations

13. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 12 experi- ment clinics theory computing clinics computing experiment theory storage I / O crunch hardware storage hardware crunch I / O database GUI code software technology center internet portal compute server P P P P P P R R R R R R

14. Dr I. Bojak Section Neurophysiology and Neuroinformatics Computational brain models of EEG/MEG and fMRI signals in health and disease 13 Conclusions – assume it’s Loxodonta africana mean field theory EEG MEG fMRI PET SPECT anatomy