Abstract
One of the most intriguing questions in neuroscience is to understand the mechanism of information transfer between different brain areas. Recently, network theory has gained traction and is at the forefront of providing a possible explanation to not only the mechanism of information transfer but also in the identification of different neuropathologies. The perception of a phantom ringing in the ear called tinnitus, similar to other neuropathologies, has been shown to be accompanied by aberrant functional connectivity between different brain areas. Although, there have been independent studies showing that specific groups of areas encode individual symptoms of tinnitus, there has not been one study to show that tinnitus is the unified percept of distinguishable sub-networks encoding different behavioral aspects. This study combines resting-state functional connectivity obtained from the source-localized electroencephalography of 311 tinnitus patients and 264 controls, and a k-fold cross-validation machine learning algorithm to develop a predictive model that verifies the presence of behaviorally specific, spatiotemporally overlapping sub-networks in tinnitus. This reorganization is found to be exclusive to tinnitus, even when compared to physiologically similar disorders such as chronic pain, with each behavioral symptom having a unique oscillatory signature. This frequency-specific transmission of information, called multiplexing, enables different types of information to be carried between two brain regions through the same anatomical connection. In addition to understanding the efficient compensation mechanism of the brain in the presence of multisymptom disorders, the exclusivity of the prediction model presents an encouraging possibility for an objective neural marker for tinnitus.