This document summarizes research on analyzing EEG and MEG data to build dynamic functional brain networks. It discusses Broadmann brain areas and their relation to function, different brain imaging techniques like fMRI and their pros and cons, methods for measuring neuronal synchrony and connectivity, experimental paradigms used with MEG to study transient networks involving word processing, and challenges in fully characterizing complex brain network dynamics from neuroimaging data.

1. Analyis of EEG and MEG data
for building dynamic functional
connectome.
Alexei Ossadtchi, Ph.D.
Higher School of Economics
St. Petersburg State University

2. 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

3. ●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

4. 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

5. 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

6. 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

7. T
T
Non-invasive
Invasive
Comparative analysis of functional
brain imaging modalities

8. 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

9. +
Intracranial EEG (iEEG)

10. Variety of tools to measure
relation between signal
Coherence Phase Locking Value
Phase Assymetry Index
Directed transfer function
Granger 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

11. 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

12. 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

13. X
Y
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)

14. EEG is now lightweight,
mobile, cheap and high density
specialneedsdigest.com
advancedbrainmonitoring.com

15. Magnetoencephalography (MEG)

16. 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)

18. Experimental paradigm
ᵦ vs ᵧ
Reference voxel Relative coupling
strength
distribution

19. ● 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

20. 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

21. 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
●

22. Hand movement-related
word vs noun (beta band)
Total synchrony Residual synchrony
time
time
Synchrony source timeseries
time

23. ● 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

24. ● 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

25. 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

26. 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.