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Continuous dynamical systems as state transition networks: a new perspective on brain signal analysis

Young Researchers Team Grant: PN-III-P1-1.1-TE-2016-1457

contract number: 48

Sponsored by the Romanian National Authority for Scientific Research CNCS-UEFISCDI

Project director: Dr. Mária Ercsey-Ravasz

Period: 02 May 2018- 30 April 2020


Research topic:

Nonlinear dynamics and network science are two powerful research areas with many interdisciplinary applications recently including neuroscience, where understanding brain dynamics and functional connectivity is still in its infancy. Functional networks extracted from EEG and fMRI signals have been frequently studied in the last years for understanding the neurophysiological mechanisms underlying normal and disturbed brain functions, but the low sampling rate of fMRI and the small number of electrodes in the case of EEG limits the power of this approach. In this project we aim to use state transition networks (STN) defined on continuous dynamical systems to combine the network science approach with nonlinear dynamics theory in order to study brain signals. This will lay the foundations of a method that will unleash the power of the network approach also for low dimensional data sets or even for a single time series (via time delay embedding). First, we aim to develop the methodology of extracting STNs from continuous dynamics. Studying well-known dynamical systems (Henon map, Lorenz system etc.) we want to understand how dynamical properties, such as chaotic behavior, are reflected in its structure. Second, we will apply this methodology to extract STNs from sleep EEG brain signals recorded in different clinical conditions: healthy control group, pre-manifest and manifest Huntington’s disease (a genetically inherited progressive neurodegenerative disease). Disrupted sleep being one of the earliest markers of neurodegeneration, comparing different clinical groups we want to identify potential biomarkers/predictors for cognitive decline. Our project will introduce new concepts and methods in continuous dynamics theory, will open a new direction in neuronal signal processing and can have a direct impact in clinical neuroscience, where biomarkers distinguishing normal and disturbed brain functions play a crucial role.


Aim I: To develop the methodology of extracting state transition networks (STN) from continuous dynamics and understand how dynamical properties (such as chaotic behavior) are reflected in its structure. As a start we will work on well-known dynamical systems: Henon map, logistic map, Lorenz system etc. We will study the STN on two levels: 1) the network of states: nodes are defined as small regions of the state space divided along a geometrical grid; 2) and on a higher level, the network of modules (clusters or strongly connected subgraphs) detected in the lower-level network of states.

Aim II: To study state transition networks extracted from sleep EEG brain signals recorded in different clinical conditions. Measures offered by quantitative EEG analysis contribute essentially to the understanding of sleep physiology. We will use sleep data collected in pre-manifest and manifest Huntington's disease (a genetically inherited neurodegenerative disease). Using this methodology our goal is to identify specific patterns and discriminative features for different conditions such as different phases of sleep and health vs disease.


Phase I (2018 May-December): Budget: 123,625 RON

- Activity I.1: Studying the role of the space resolution parameter

- Activity I.2: Studying the role of the time resolution parameter

- Activity I.3: Studying the length and number of trajectories needed to build the STN.

- Activity I.4: Studying the network at a higher hierarchical level: the network of modules.

Summary of Report I soon to be seen.

ISI Journal articles published during the project:

1. B Molnár, F. Molnar, M Varga, Z Toroczkai, M Ercsey-Ravasz, „A continuous-time max-SAT solver with high analog performance”, under review in Nature Communications.

Other publications:

- M. Ercsey-Ravasz, "Agyi halozaok modellezese egy tavolsagszabaly alapjan" ("Modeling structural brain networks based on a distance rule"), FIRKA, invited paper 2017-2018 vol. 4 (

Previous publications relevant to the project:

- Răzvan Gămănuţ, Henry Kennedy, Zoltán Toroczkai, Mária Ercsey-Ravasz, David C Van Essen, Kenneth Knoblauch, Andreas Burkhalter, The Mouse Cortical Connectome, Characterized by an Ultra-Dense Cortical Graph, Maintains Specificity by Distinct Connectivity Profiles, Neuron 97, 698-715. e10 , 2018.

- Xunzhao Yin, Behnam Sedighi, Melinda Varga, Mária Ercsey-Ravasz, Zoltán Toroczkai, Xiaobo Sharon Hu, „Efficient analog circuits for Boolean satisfiability”, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 26 (1), pp. 155-167, 2018.

- Sz. Horvát†, Răzvan Gămănuț†, Mária Ercsey-Ravasz†, Loïc Magrou, Bianca Gămănuț, David C. Van Essen, Andreas Burkhalter, Kenneth Knoblauch, Zoltán Toroczkai, Henry Kennedy,”Spatial embedding and wiring cost constrain the functional layout of cortical networks in rodents and primates”, PLoS Biol., vol. 14, e1002512, 2016. († indicates equal contribution).

- M. Ercsey-Ravasz, N.T. Markov, C. Lamy, D.C. Van Essen, K. Knoblauch, Z. Toroczkai, H. Kennedy, “A predictive network model of cerebral cortical connectivity based on a distance rule.”, Neuron vol. 80, pp. 184-197, 2013

- N.T. Markov, M. Ercsey-Ravasz, D.C. Van Essen, K. Knoblauch, Z. Toroczkai, H. Kennedy, "Cortical High-density Counter-stream Architectures", Science, 342, 1238406, 2013 doi:10.1126/science.1238406

- M. Ercsey-Ravasz, Z. Toroczkai, "The Chaos Within Sudoku", Nature Scientific Reports 2, 755 (2012) doi:10.1038/srep00725

- M. Ercsey-Ravasz, Z. Toroczkai, "Optimization Hardness as Transient Chaos in an Analog Approach to Constraint Satisfaction", Nature Physics 7, 966 (2011) arxiv:1208.0526