Communication dynamics in complex brain networks

Abstract

Neuronal signalling and communication underpin virtually all aspects of brain activity and function. Network science approaches to modelling and analysing the dynamics of communication on networks have proved useful for simulating functional brain connectivity and predicting emergent network states. This Review surveys important aspects of communication dynamics in brain networks. We begin by sketching a conceptual framework that views communication dynamics as a necessary link between the empirical domains of structural and functional connectivity. We then consider how different local and global topological attributes of structural networks support potential patterns of network communication, and how the interactions between network topology and dynamic models can provide additional insights and constraints. We end by proposing that communication dynamics may act as potential generative models of effective connectivity and can offer insight into the mechanisms by which brain networks transform and process information.

Weighted Network Density Predicts Range of Latent Variable Model Accuracy

“…The method can be extended in three ways 1) via different weight matrix quantification methods 2) with more graph-theoretic measures 3) testing with multiple types of network models. A directed graph quantification can be attained through use of measures such as transfer entropy, Granger causality, or more sophisticated methods of effective connectivity [1]. Investigation of various network measures could elucidate even stronger relationships between graph-theoretic measures and certain classes of network models.…”

“…For example, in the study of sensory perception, this confluence of techniques allows for a change from pairwise associations between individual sensory neurons and sensory stimuli, to network-level measures of sensory representation. Conventional graph-theoretic measures have often evaluated the explicit network where nodes and edges correspond to neuronal units recorded and statistical dependences between these neurons respectively [1]. Yet, the explicit neuronal population dynamics could be modulated by latent variables or implicit network with unobservable nodes and edges, where the implicit activities were not recorded [5].…”

“…Applying graph theoretic tools on these high-volume simultaneous recordings allows for novel analyses of brain networks. Graph theory can efficiently characterize network types and elucidate important network properties [1]. For example, in the study of sensory perception, this confluence of techniques allows for a change from pairwise associations between individual sensory neurons and sensory stimuli, to network-level measures of sensory representation.…”

Altered inter‐ and intrahemispheric functional connectivity dynamics in autistic children

“…Functional connectivity density (FCD) mapping is a voxel‐wise data‐driven graph theory approach, which allows for identification of the distribution of highly connected hubs in brain networks (Tomasi & Volkow, , ). Network hubs facilitate efficient functional integration of information processing both within and between particular neural systems (Avena‐Koenigsberger, Misic, & Sporns, ). Subsequent FCD studies confirmed its sensitivity in detecting abnormalities of the functional connectivity hubs in psychiatric and neurological diseases (Lee et al, ; Li et al, ; Tomasi & Volkow, ).…”

Synthetic Biology: Engineering Mammalian Cells To Control Cell‐to‐Cell Communication at Will

“…For example, bacteria control their own proliferation activity by quorum sensing and fungi communicate with each other through pheromones . Mammalian cells also communicate with each other both with diffusible factors (paracrine/endocrine signalling) and by direct cell contact (juxtacrine signalling) to regulate development, immune responses, neurotransmission, and cancer development . Thus, the ability to precisely control these cell‐to‐cell communication processes would provide new insights into the biology of complex systems, as well as opportunities to treat intractable diseases associated with dysfunctions of cell‐to‐cell communication.…”