title: How do parcellation size and short-range connectivity affect dynamics in large-scale brain network models?
abstract: 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 Connectome 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.
authors.affiliation: Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France. Electronic address: timothee.proix@etu.univ-amu.fr.
authors.forename: Timothée
authors.initials: T
authors.lastname: Proix
authors.affiliation: Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
authors.forename: Andreas
authors.initials: A
authors.lastname: Spiegler
authors.affiliation: Department of Neurology, Charité University Medicine Berlin, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Schirner
authors.affiliation: Department of Neurology, Charité University Medicine Berlin, Germany.
authors.forename: Simon
authors.initials: S
authors.lastname: Rothmeier
authors.affiliation: Department of Neurology, Charité University Medicine Berlin, Germany; BrainModes Research Group, Max-Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany.
authors.forename: Petra
authors.initials: P
authors.lastname: Ritter
authors.affiliation: Aix-Marseille Univ, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France. Electronic address: viktor.jirsa@univ-amu.fr.
authors.forename: Viktor K
authors.initials: VK
authors.lastname: Jirsa
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging / Q000502:physiology*; D063132:Connectome / Q000379:methods*; D056324:Diffusion Tensor Imaging / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*
keywords: Diffusion; Functional and structural MRI; Large-scale brain network models; Parcellations; SCRIPTS; Short-range connectivity; The virtual brain