How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models? - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue NeuroImage Année : 2016

How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?

Résumé

Recent efforts to model human brain activity on the scale of the whole brain rest on connectivity estimates of large-scale networks derived from diffusion magnetic resonance imaging (dMRI). This type of connectivity describes white matter fiber tracts. The number of short-range cortico-cortical white-matter connections is, however, underrepresented in such large-scale brain models. It is still unclear on the one hand, which scale of representation of white matter fibers is optimal to describe brain activity on a large-scale such as recorded with magneto-or electroencephalography (M/EEG) or functional magnetic resonance imaging (fMRI), and on the other hand, to which extent short-range connections that are typically local should be taken into account. In this article we quantified the effect of connectivity upon large-scale brain network dynamics by (i) systematically varying the number of brain regions before computing the connectivity matrix, and by (ii) adding generic short-range connections. We used dMRI data from the Human Connec-tome Project. We developed a suite of preprocessing modules called SCRIPTS to prepare these imaging data for The Virtual Brain, a neuroinformatics platform for large-scale brain modeling and simulations. We performed simulations under different connectivity conditions and quantified the spatiotemporal dynamics in terms of Shannon Entropy, dwell time and Principal Component Analysis. For the reconstructed connectivity, our results show that the major white matter fiber bundles play an important role in shaping slow dynamics in large-scale brain networks (e.g. in fMRI). Faster dynamics such as gamma oscillations (around 40 Hz) are sensitive to the short-range connectivity if transmission delays are considered. (C) 2016 Elsevier Inc. All rights reserved.

Domaines

Neurosciences
Fichier non déposé

Dates et versions

hal-01431312 , version 1 (10-01-2017)

Identifiants

Citer

Timothée Proix, Andreas Spiegler, Michael Schirner, Simon Rothmeier, Petra Ritter, et al.. How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?. NeuroImage, 2016, 142, pp.135-149. ⟨10.1016/j.neuroimage.2016.06.016⟩. ⟨hal-01431312⟩

Collections

UNIV-AMU INS
137 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More