Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/10230/23098
Título: | How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest |
---|---|
Autor/a: | Nakagawa, Tristan T.; Woolrich, Mark; Luckhoo, Henry; Joensson, Morten; Mohseni, Hamid; Kringelbach, Morten L.; Jirsa, Viktor K.; Deco, Gustavo |
Abstract: | In recent years the study of the intrinsic brain dynamics in a relaxed awake state in the absence of any specific/ntask has gained increasing attention, as spontaneous neural activity has been found to be highly structured at a/nlarge scale. This so called resting-state activity has been found to be comprised by nonrandom spatiotemporal/npatterns and fluctuations, and several Resting-State Networks (RSN) have been found in BOLD-fMRI as well as/nin MEG signal power envelope correlations. The underlying anatomical connectivity structure between areas of/nthe brain has been identified as being a key to the observed functional network connectivity, but the mechanisms/nbehind this are still underdetermined. Theoretical large-scale brain models for fMRI data have corroborated the/nimportance of the connectome in shaping network dynamics,while the importance of delays and noise differ between/nstudies and depend on the models' specific dynamics. In the current study, we present a spiking neuron/nnetworkmodel that is able to produce noisy, distributed alpha-oscillations, matching the power peak in the spectrumof/ngroup resting-stateMEG recordings.We studied howwell the model captured the inter-node correlation/nstructure of the alpha-band power envelopes for different delays between brain areas, and found that the model/nperforms best for propagation delays inside the physiological range (5–10 m/s). Delays also shift the transition/nfrom noisy to bursting oscillations to higher global coupling values in the model. Thus, in contrast to the/nasynchronous fMRI state, delays are important to consider in the presence of oscillation. |
Abstract: | TTN was supported by the SUR of the DEC of the Catalan Government/nand by the FSE. GD was supported by the ERC Advanced Grant:/nDYSTRUCTURE (n. 295129), by the Spanish Research Project SAF2010-/n16085 and by the CONSOLIDER-INGENIO 2010 Programme CSD2007-/n00012, and the FP7-ICT BrainScales. The research reported herein was/nsupported by the Brain Network Recovery Group through the James S./nMcDonnell Foundation. |
Materia(s): | -Resting-state model -MEG -Delays -Spontaneous alpha -Alpha-oscillations -SFA -Spike-frequency adaptation |
Derechos: | © 2013 The Authors. Published by Elsevier Inc. Open access under CC BY-NC-SA license.
http://creativecommons.org/licenses/by-nc-sa/3.0/ |
Tipo de documento: | Artículo Artículo - Versión publicada |
Editor: | Elsevier |
Compartir: |