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.

Highlights

  • In the present work we provide some insight into how this knowledge could be applied to tinnitus pathophysiology and pharmacotherapy

  • The rationale behind the use of them is to treat the co-morbidities that come along with tinnitus, like depression and anxiety (Johnson et al, 1993; Sullivan et al, 1993; Bahmad et al, 2006). It is derived from the use of drugs which are effective in disorders thought to share some commonalities with tinnitus, like anticonvulsants used in epilepsy (Hoekstra et al, 2011) and the calcium antagonist gabapentin used in neuropathic pain (Bauer and Brozoski, 2007)

  • In the present work we review some recent trends in the discovery of central nervous system (CNS) acting drugs, describe new ways of analyzing brain networks

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Summary

Introduction

Graph analysis of structural and functional connectivity is consistently showing characteristic non-random properties of brain networks (Reijneveld et al, 2007; Bullmore and Sporns, 2009; Bullmore and Bassett, 2011; Sporns, 2011c). Graph analysis from fMRI data has described small-world topology of brain networks, with a truncated power law distribution (Salvador et al, 2005; Achard et al, 2006).

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