Analyis of EEG and MEG data for building dynamic functional

connectome.Alexei Ossadtchi, Ph.D.

Higher School of EconomicsSt. Petersburg State University

Broadmann brain areas

Economo, C. von, Koskinas, G.N. (1925) Die Cytoarchitektonik der Hirnrinde des erwachsenen Menschen. Julius Springer, Vienna

●Originally identified by Korbinian Broadmann as 52(44+8) distinct regions with specific histilogical characteristics

●Refined and much argued over

●Less detailed maps before (Alfred Campbell, Grafton Smith) and more detailed maps later Constatin von Economo and George Koskinas were published

Broadmann brain areas

●Broadmann areas originally discovered based on microstructural features were later linked to functions

●Clinical post-mortem studies, animal studies, unfortunate cases of trauma and for the last quarter of a century functional imaging associated cognitive tasks with engagement of certain brain regions

Primary auditory cortex

Primary somatosensory cortex

Broca's area

http://thebrain.mcgill.ca/flash/capsules/outil_jaune05.html; J.T. Cacioppo,G.G. Berntson, and H.C. Nusbaum, Neuroimaging as a New Tool in the Toolbox of Psychological , Current directions in psychological science, 2008

Relation to function

Scopus, 2013, search query: TITLE-ABS-KEY((neuronal AND synchrony) OR (neuronal AND coupling) OR (neuronal AND connectivity)) AND PUBYEAR > 2001

relative to

TITLE-ABS-KEY(neuronal)

«Just one word, Ben:» Networks

Brain network structures● Invasive (cellular level, population level ) studies yield fantastic insights into how the networks of neurons are organized on the microscale level

● MRI based DTI (diffusion tensor imaging) technique allows to track axonal connections linking distal neuronal populations into a small-world architecture by means of a limited set of hubs forming so called rich club.

● fMRI (registers Blood Oxygenation Level Dependent ) is one of the most frequently used techniques for non-invasive functional brain imaging

fMRI for causal dynamics● Dynamic causal modeling (DCM , K. Friston, UCL) is model based and hypotheses driven method for assessing the information flow structure.

● Originally developed for fMRI

● fMRI lacks needed temporal resolution to study fine timing details of neuronal communications

● DCM philosophy does not allow exploratory analysis - you've got to have models to compare

T

T

Non-invasive

Invasive

Comparative analysis of functional brain imaging modalities

Transient networks

Go-no-Go task in a behaving cat waiting for a visual pattern to change

Varela et al., Brainweb, Nature Review Neuroscience, 2001,2,229-239

+

Intracranial EEG (iEEG)

Variety of tools to measure relation between signal

Coherence Phase Locking ValuePhase Assymetry Index

Directed transfer functionGranger Causality

Mutual Information

Transfer entropy

Bi-coherence

Bi-Phase Locking Value

Phase Slope Index

R.E. Greenblatt, M.E. Pflieger, A. Ossadtchi, Connectivity measures applied to human brain electrophysiological data, Journal of Neuroscience Methods, 207 (2012) 1– 16

Concurently active networks at seizure onset

A. Ossadtchi, R.E. Greenblatt, V.L. Towle, M.H. Kohrman, K. Kamada, Inferring Spatiotemporal Network Patterns from Intracranial EEG Data, Clin, Neurophysiology, 2010

Degree of synchrony

time

N1

N2

N3

Golgi section of cat's cortex

● LFP that can be registered with EEG/MEG comes from synchronous stimulation of large populations (~ 1-10 cm2 of cortex) of the pyramidal neurons with long apical dendrites

● The dynamic process of PSP propagation along the long dendrite can be approximated by the current dipole

Okada Y (1983): Neurogenesis of evoked magnetic fields. In: Williamson SH, Romani GL, Kaufman L, Modena I, editors. Biomagnetism: an Interdisciplinary Approach, New York: Plenum Press, pp 399-408

Extracellular space polarization

Equivalent current dipole MODEL

XY

Z

Okada Y : Neurogenesis of evoked magnetic fields. In: Williamson SH, Romani GL, Kaufman L, Modena I, editors. Biomagnetism: an Interdisciplinary Approach, New York: Plenum Press, pp 399-408, 1983

`

C.H. Wolters, A. Anwander, D. Weinstein, M. Koch, X. Tricoche, and R.S. MacLeod, Influence of tissue conductivity anisotropy on EEG/MEG field and return current computation in a realistic head model: A simulation and visualization study using high-resolution finite element modeling,NeuroImage, 30:3 (2006), 813–826; 4.

The other side of the mirror(forward and inverse problems)

EEG is now lightweight, mobile, cheap and high density

specialneedsdigest.com

advancedbrainmonitoring.com

Magnetoencephalography (MEG)

Pros & Cons of EEG/MEG

● Completely noninvasive● Seeing the whole head● Measures electircal activity (more directly

related to neuronal activation than BOLD or Glucometabolic signal)

● High temporal resolution● Low spatial resolution (0.5 cm MEG, 1.5 cm

EEG)● Insensitivity to deep structures (primarily EEG)

Experimental paradigm

ᵦ vs ᵧ

Reference voxel Relative coupling strength

distribution

● Growing evidence that cortical activity consists of an interpaly between constantly active specific networks (Glasser et al., 2012, fMRI evidence)

● Baker at al., 2014 illustrated that with resting state MEG using HMM. Mutually exclusive states.

Switching Networks

Interaction space RAP-MUSIC● A new method to recover transient networks● The standard first source signals then synchrony

measures approach is prone to errors, results into a huge multiple comparisons problems that affects the sensitivity

● Use linear generative models and perform inference in a standard Multiple Signal Classification framework

● Global approach, allows to measure the amount of explained synchrony

● Naturally accommodates temporal evolution of synchrony

Experimental Setting

● Odd-ball, movement related words (randomized design )● Brosym, Brosym, …, Brosai, Brosym, …, Brosok

● 120 of odd balls of each type

● Neuromag Vectorview 306 sensor MEG machine

●

Hand movement-related word vs noun (beta band)

Total synchrony Residual synchrony

time

time

Synchrony source timeseries

time

● Straightforward way results into a huge multiple comparisons problem. Sensor data simply don't have this much information and it is unclear how to introduce the priors

● Think your source is an interacting pair of dipoles with unknown coordinates, orientations and activations

● Think Networks not dipoles !

Conclusion

● Understanding how we are wired and and how these wiring patterns condition our behavior.

● Presurgical mapping of brain function to avoid post-surgical deficit

● Diagnosis of complex neurological disorders● Mind reading, brain state classification based

on the network activity● Building dynamic functional connectome

Conclusion

Big Data?

One subject dataset fMRI + MRI+MEG/EEG data, DTI is on the order of 50 GB

In one study usually 20 subjects

Source Space Synchrony tensor : 15K x 15k x 1000

Collaborators● Tatiana Stroganova, Moscow MEG Centre● Richard Greeblatt, ex-president, Source Signal

Imaging Inc, San Diego, CA● John Mosher, MEG Centre, Cleveland Clinic● Syvain Baillett, McGill University, Montreal,

Canada

Partial support

● RFBR grant #140....,Novel instrumental-mathematical paradigm for presurgical mapping of language cortex, P.I. Ossadtchi,A.