Tinnitus: network pathophysiology-network pharmacology

Abstract

Tinnitus, the phantom perception of sound, is a prevalent disorder. One in 10 adults has clinically significant subjective tinnitus, and for one in 100, tinnitus severely affects their quality of life. Despite the significant unmet clinical need for a safe and effective drug targeting tinnitus relief, there is currently not a single Food and Drug Administration (FDA)-approved drug on the market. The search for drugs that target tinnitus is hampered by the lack of a deep knowledge of the underlying neural substrates of this pathology. Recent studies are increasingly demonstrating that, as described for other central nervous system (CNS) disorders, tinnitus is a pathology of brain networks. The application of graph theoretical analysis to brain networks has recently provided new information concerning their topology, their robustness and their vulnerability to attacks. Moreover, the philosophy behind drug design and pharmacotherapy in CNS pathologies is changing from that of "magic bullets" that target individual chemoreceptors or "disease-causing genes" into that of "magic shotguns," "promiscuous" or "dirty drugs" that target "disease-causing networks," also known as network pharmacology. In the present work we provide some insight into how this knowledge could be applied to tinnitus pathophysiology and pharmacotherapy.

Keywords: brain networks; graph analysis; magic bullets; network pharmacology; phantom percept; scale-free; small-world; tinnitus.

Figure 1

Network topologies. (A) An example of a random network with no high degree hubs, where nodes (red circles) are connected by edges (black lines). (B) A scale-free network with high degree hubs (gray circles). (C) A modular network where nodes within a module (i.e., red, green, and blue modules) are highly connected to each other and only sparsely connected to nodes of another module.

Figure 2

Graph analysis to brain networks. Structural (including either gray or white matter measurements using histological or imaging data) or functional data (including resting-state fMRI, fMRI, EEG, or MEG data) is the starting point. Nodes are defined (e.g., anatomically defined regions of histological, MRI or diffusion tensor imaging data in structural networks or EEG electrodes or MEG sensors in functional networks) and an association between nodes is established (coherence, connection probability, or correlations in cortical thickness). The pairwise association between nodes is then computed, and usually thresholded to create a binary (adjacency) matrix. A brain network is then constructed from nodes (brain regions) and edges (pairwise associations that were larger than the chosen threshold).