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Neuroimage. 2017 Feb 1;146:188-196. doi: 10.1016/j.neuroimage.2016.11.034. Epub 2016 Nov 16.

Temporally correlated fluctuations drive epileptiform dynamics.

Author information

1
Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Terrassa, Spain; Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain. Electronic address: maciej.jedynak@protonmail.com.
2
Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Terrassa, Spain.
3
Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Parc de Recerca Biomèdica de Barcelona, Barcelona, Spain.
4
College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.

Abstract

Macroscopic models of brain networks typically incorporate assumptions regarding the characteristics of afferent noise, which is used to represent input from distal brain regions or ongoing fluctuations in non-modelled parts of the brain. Such inputs are often modelled by Gaussian white noise which has a flat power spectrum. In contrast, macroscopic fluctuations in the brain typically follow a 1/fb spectrum. It is therefore important to understand the effect on brain dynamics of deviations from the assumption of white noise. In particular, we wish to understand the role that noise might play in eliciting aberrant rhythms in the epileptic brain. To address this question we study the response of a neural mass model to driving by stochastic, temporally correlated input. We characterise the model in terms of whether it generates "healthy" or "epileptiform" dynamics and observe which of these dynamics predominate under different choices of temporal correlation and amplitude of an Ornstein-Uhlenbeck process. We find that certain temporal correlations are prone to eliciting epileptiform dynamics, and that these correlations produce noise with maximal power in the δ and θ bands. Crucially, these are rhythms that are found to be enhanced prior to seizures in humans and animal models of epilepsy. In order to understand why these rhythms can generate epileptiform dynamics, we analyse the response of the model to sinusoidal driving and explain how the bifurcation structure of the model gives rise to these findings. Our results provide insight into how ongoing fluctuations in brain dynamics can facilitate the onset and propagation of epileptiform rhythms in brain networks. Furthermore, we highlight the need to combine large-scale models with noise of a variety of different types in order to understand brain (dys-)function.

KEYWORDS:

Epilepsy; Ictogenesis; Jansen-Rit model; Neural mass models; Nonlinear dynamics; Ornstein‐Uhlenbeck noise; Stochastic effects

PMID:
27865920
PMCID:
PMC5353705
DOI:
10.1016/j.neuroimage.2016.11.034
[Indexed for MEDLINE]
Free PMC Article

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