Large-Scale Neural Network Model for Functional Networks of the Human Cortex

Vesna Vuksanovic, Philipp Hövel

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Citations (Scopus)

Abstract

We investigate the influence of indirect connections, interregional distance and collective effects on the large-scale functional networks of the human cortex. We study topologies of empirically derived resting state networks (RSNs), extracted from fMRI data, and model dynamics on the obtained networks. The RSNs are calculated from mean time-series of blood-oxygen-level-dependent (BOLD) activity of distinct cortical regions via Pearson correlation coefficients. We compare functional-connectivity networks of simulated BOLD activity as a function of coupling strength and correlation threshold. Neural network dynamics underpinning the BOLD signal fluctuations are modelled as excitable FitzHugh-Nagumo oscillators subject to uncorrelated white Gaussian noise and time-delayed interactions to account for the finite speed of the signal propagation along the axons. We discuss the functional connectivity of simulated BOLD activity in dependence on the signal speed and correlation threshold and compare it to the empirical data.
Original languageEnglish
Title of host publicationSelforganization in Complex Systems
Subtitle of host publicationThe Past, Present, and Future of Synergetics
EditorsGünter Wunner, Axel Pelster
PublisherSpringer
Pages345-352
Number of pages8
ISBN (Electronic)978-3-319-27635-9
ISBN (Print)978-3-319-27633-5
DOIs
Publication statusPublished - 2016

Publication series

NameUnderstanding Complex Systems
PublisherSpringer
ISSN (Electronic)1860-0832

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Vuksanovic, V., & Hövel, P. (2016). Large-Scale Neural Network Model for Functional Networks of the Human Cortex. In G. Wunner, & A. Pelster (Eds.), Selforganization in Complex Systems: The Past, Present, and Future of Synergetics (pp. 345-352). (Understanding Complex Systems). Springer . https://doi.org/10.1007/978-3-319-27635-9_26