195 research outputs found
A Framework to Control Functional Connectivity in the Human Brain
In this paper, we propose a framework to control brain-wide functional
connectivity by selectively acting on the brain's structure and parameters.
Functional connectivity, which measures the degree of correlation between
neural activities in different brain regions, can be used to distinguish
between healthy and certain diseased brain dynamics and, possibly, as a control
parameter to restore healthy functions. In this work, we use a collection of
interconnected Kuramoto oscillators to model oscillatory neural activity, and
show that functional connectivity is essentially regulated by the degree of
synchronization between different clusters of oscillators. Then, we propose a
minimally invasive method to correct the oscillators' interconnections and
frequencies to enforce arbitrary and stable synchronization patterns among the
oscillators and, consequently, a desired pattern of functional connectivity.
Additionally, we show that our synchronization-based framework is robust to
parameter mismatches and numerical inaccuracies, and validate it using a
realistic neurovascular model to simulate neural activity and functional
connectivity in the human brain.Comment: To appear in the proceedings of the 58th IEEE Conference on Decision
and Contro
Disturbing synchronization: Propagation of perturbations in networks of coupled oscillators
We study the response of an ensemble of synchronized phase oscillators to an
external harmonic perturbation applied to one of the oscillators. Our main goal
is to relate the propagation of the perturbation signal to the structure of the
interaction network underlying the ensemble. The overall response of the system
is resonant, exhibiting a maximum when the perturbation frequency coincides
with the natural frequency of the phase oscillators. The individual response,
on the other hand, can strongly depend on the distance to the place where the
perturbation is applied. For small distances on a random network, the system
behaves as a linear dissipative medium: the perturbation propagates at constant
speed, while its amplitude decreases exponentially with the distance. For
larger distances, the response saturates to an almost constant level. These
different regimes can be analytically explained in terms of the length
distribution of the paths that propagate the perturbation signal. We study the
extension of these results to other interaction patterns, and show that
essentially the same phenomena are observed in networks of chaotic oscillators.Comment: To appear in Eur. Phys. J.
On the stability of the Kuramoto model of coupled nonlinear oscillators
We provide an analysis of the classic Kuramoto model of coupled nonlinear
oscillators that goes beyond the existing results for all-to-all networks of
identical oscillators. Our work is applicable to oscillator networks of
arbitrary interconnection topology with uncertain natural frequencies. Using
tools from spectral graph theory and control theory, we prove that for
couplings above a critical value, the synchronized state is locally
asymptotically stable, resulting in convergence of all phase differences to a
constant value, both in the case of identical natural frequencies as well as
uncertain ones. We further explain the behavior of the system as the number of
oscillators grows to infinity.Comment: 8 Pages. An earlier version appeared in the proceedings of the
American Control Conference, Boston, MA, June 200
Dynamics on networks: the role of local dynamics and global networks on the emergence of hypersynchronous neural activity.
Published onlineJournal ArticleResearch Support, Non-U.S. Gov'tGraph theory has evolved into a useful tool for studying complex brain networks inferred from a variety of measures of neural activity, including fMRI, DTI, MEG and EEG. In the study of neurological disorders, recent work has discovered differences in the structure of graphs inferred from patient and control cohorts. However, most of these studies pursue a purely observational approach; identifying correlations between properties of graphs and the cohort which they describe, without consideration of the underlying mechanisms. To move beyond this necessitates the development of computational modeling approaches to appropriately interpret network interactions and the alterations in brain dynamics they permit, which in the field of complexity sciences is known as dynamics on networks. In this study we describe the development and application of this framework using modular networks of Kuramoto oscillators. We use this framework to understand functional networks inferred from resting state EEG recordings of a cohort of 35 adults with heterogeneous idiopathic generalized epilepsies and 40 healthy adult controls. Taking emergent synchrony across the global network as a proxy for seizures, our study finds that the critical strength of coupling required to synchronize the global network is significantly decreased for the epilepsy cohort for functional networks inferred from both theta (3-6 Hz) and low-alpha (6-9 Hz) bands. We further identify left frontal regions as a potential driver of seizure activity within these networks. We also explore the ability of our method to identify individuals with epilepsy, observing up to 80% predictive power through use of receiver operating characteristic analysis. Collectively these findings demonstrate that a computer model based analysis of routine clinical EEG provides significant additional information beyond standard clinical interpretation, which should ultimately enable a more appropriate mechanistic stratification of people with epilepsy leading to improved diagnostics and therapeutics.Funding was from Epilepsy Research UK (http://www.epilepsyresearch.org.uk) via grant number A1007 and the Medical Research Council (http://www.mrc.ac.uk) via grants (MR/K013998/1 and G0701310)
Stochastic Stability of Discrete-time Phase-coupled Oscillators over Uncertain and Random Networks
This paper studies stochastic stability of a class of discrete-time
phase-coupled oscillators. We introduce the two new notions of stochastic and
ultimate stochastic phase-cohesiveness using the concepts of Harris and
positive Harris recurrent Markov chains. Stochastic phase-cohesiveness of
oscillators in two types of networks are studied. First, oscillators in a
network with an underlying connected topology subject to both multiplicative
and additive stochastic uncertainties are considered. Second, we study a
special case of the former problem by assuming that the multiplicative
uncertainties are governed by the Bernoulli process representing the well known
Erd{\H o}s R\'enyi network
The Kuramoto model in complex networks
181 pages, 48 figures. In Press, Accepted Manuscript, Physics Reports 2015 Acknowledgments We are indebted with B. Sonnenschein, E. R. dos Santos, P. Schultz, C. Grabow, M. Ha and C. Choi for insightful and helpful discussions. T.P. acknowledges FAPESP (No. 2012/22160-7 and No. 2015/02486-3) and IRTG 1740. P.J. thanks founding from the China Scholarship Council (CSC). F.A.R. acknowledges CNPq (Grant No. 305940/2010-4) and FAPESP (Grants No. 2011/50761-2 and No. 2013/26416-9) for financial support. J.K. would like to acknowledge IRTG 1740 (DFG and FAPESP).Peer reviewedPreprin
Effects of Delay on the Functionality of Large-scale Networks
Networked systems are common across engineering and the physical sciences. Examples include the Internet, coordinated motion of multi-agent systems, synchronization phenomena in nature etc. Their robust functionality is important to ensure smooth operation in the presence of uncertainty and unmodelled dynamics. Many such networked systems can be viewed under a unified optimization framework and several approaches to assess their nominal behaviour have been developed. In this paper, we consider what effect multiple, non-commensurate (heterogeneous) communication delays can have on the functionality of large-scale networked systems with nonlinear dynamics. We show that for some networked systems, the structure of the delayed dynamics allows functionality to be retained for arbitrary communication delays, even for switching topologies under certain connectivity conditions; whereas in other cases the loop gains have to be compensated for by the delay size, in order to render functionality delay-independent for arbitrary network sizes. Consensus reaching in multi-agent systems and stability of network congestion control for the Internet are used as examples. The differences and similarities of the two cases are explained in detail, and the application of the methodology to other technological and physical networks is discussed
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