English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Modeling functional connectivity on empirical and randomized structural brain networks

Bayrak, S., Hövel, P., & Vuksanović, V. (2017). Modeling functional connectivity on empirical and randomized structural brain networks. Differential Equations and Dynamical Systems: DEDS, 1-17. doi:10.1007/s12591-017-0354-x.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/21.11116/0000-0002-B8F4-B Version Permalink: http://hdl.handle.net/21.11116/0000-0004-3A9A-E
Genre: Journal Article

Files

show Files

Locators

show
hide
Description:
-

Creators

show
hide
 Creators:
Bayrak, Seyma1, Author              
Hövel, Philipp 2, 3, Author
Vuksanović, Vesna 2, 3, 4, Author
Affiliations:
1Max Planck Research Group Neuroanatomy and Connectivity, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_1356546              
2Institute of Theoretical Physics, TU Berlin, Germany, ou_persistent22              
3Bernstein Center for Computational Neuroscience Berlin, Humboldt University Berlin, Germany, ou_persistent22              
4Aberdeen Biomedical Imaging Centre, University of Aberdeen, United Kingdom, ou_persistent22              

Content

show
hide
Free keywords: Brain networks; Functional and anatomical connectivity; Hemodynamic mode;l Resting state; Time-delayed oscillations
 Abstract: This study combines modeling of neuronal activity and networks derived from neuroimaging data in order to investigate how the structural organization of the human brain affects the temporal dynamics of interacting brain areas. The dynamics of the neuronal activity is modeled with FitzHugh–Nagumo oscillators and the blood-oxygen-level-dependent (BOLD) time series is inferred via the Balloon–Windkessel hemodynamic model. The simulations are based on anatomical probability maps between considered brain regions of interest. These maps were derived from diffusion-weighted magnetic resonance imaging measurements. In addition, the length of the fiber tracks allows for inference of coupling delays due to finite signal propagation velocities. We aim to investigate (i) graph-theoretical properties of the network topology derived from neuroimaging data and (ii) how randomization of structural connections influences the dynamics of neuronal activity. The network characteristics of the structural connectivity data are compared to density-matched Erdős–Rényi random graphs. Furthermore, the neuronal and BOLD activity are modeled on both empirical and random (Erdős–Rényi type) graphs. The simulated temporal dynamics on both graphs are compared statistically to capture whether the spatial organization of these network affects the modeled time series. Results support previous findings that key topological network properties such as small-worldness of our neuroimaging data are distinguishable from random networks. We also show that simulated BOLD activity is affected by the underlying network topology and the strength of connections between the network nodes. The difference of the modeled temporal dynamics of brain networks from the dynamics on randomized graphs suggests that anatomical connections in the human brain together with dynamical self-organization are crucial for the temporal evolution of the resting-state activity.

Details

show
hide
Language(s): eng - English
 Dates: 2017-03-11
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1007/s12591-017-0354-x
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : -
Grant ID : 01Q1001B
Funding program : -
Funding organization : German Federal Ministry of Education and Research (BMBF)

Source 1

show
hide
Title: Differential Equations and Dynamical Systems: DEDS
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Hyderabad : Research Square Publ.
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1 - 17 Identifier: ISSN: 0971-3514
CoNE: https://pure.mpg.de/cone/journals/resource/0971-3514