Evidence for Behaviorally Segregated, Spatiotemporally Overlapping Subnetworks in Phantom Sound Perception

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 subnetworks 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 subnetworks 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.

Distress‐dependent temporal variability of regions encoding domain‐specific and domain‐general behavioral manifestations of phantom percepts

“…In support of this hypothesis, we first observe that the PHC exhibits a multimodal character by dynamically encoding the loudness and the distress components in the alpha frequency bands in diametrically opposing patterns possibly suggesting a dynamic association between the two networks in highly distressed patients. This finding adds a dynamic aspect to the existing evidence that loudness and distress may be encoded by independent, overlapping subnetworks (De Ridder, Vanneste, Weisz, et al, 2014;Mohan, Moreno, Song, De Ridder, & Vanneste, 2017).…”

“…This may be attributed to increased flexibility of switching connectivity between different functional modules, thus increasing the number of possible routes for information transfer (Deco & Kringelbach, ; Zhang et al., ). With respect to pathology, this can be interpreted as a maladaptive compensation by the brain looking for more information in its environment to make up for the loss of sensory input (i.e., auditory deafferentation with or without clinical hearing loss) by rapidly binding with different brain regions (Mohan & Vanneste, ).…”

Cross-Subject Tinnitus Diagnosis Based on Multi-Band EEG Contrastive Representation Learning

“…Next, evident artifacts are removed from the raw EEG data manually after visual inspection, and the independent component analysis (ICA) is utilized to remove muscle artifacts, eye movement, and heartbeats in the raw data. Then, the EEG data is segmented into 2s slices and a basic finite impulse response (FIR) filter of EEGLAB toolbox is applied to transform the slices to 8 frequency bands which are significant for tinnitus diagnosis, including delta (2-3.5 Hz), theta (4-7.5 Hz), alpha1 (8-10 Hz), alpha2 (10-12 Hz), beta1 (13-18 Hz), beta2 (18.5-21 Hz), beta3 (21.5-30 Hz) and gamma (30.5-44 Hz) [32]- [36]. The process of extracting each frequency band of the EEG signals is shown in Fig.…”

Neurofeedback for Tinnitus Treatment – Review and Current Concepts

“…Apart from investigations comparing brain networks of tinnitus patients and healthy controls based on analyses with graph theory or machine learning algorithms ( Mohan et al, 2016a , b , 2017a , b ), a multitude of recent electrophysiological studies attempt to find specific correlates in neural networks for the different aspects of tinnitus ( Adjamian, 2014 ; De Ridder et al, 2015 ; Eggermont, 2015 ; Elgoyhen et al, 2015 ). These studies mainly investigated tinnitus-related distress or loudness, but also covered tinnitus type, pitch, location/laterality, duration, age of onset, day-time awareness, or related problems such as hearing loss, hyperacusis, depression, or general quality of life (a detailed summary is provided in the Supplementary Materials).…”