Theta–Beta/Gamma Coupling Identifies Bothersome Tinnitus Induced by Thalamocortical Dysrhythmia
ABSTRACT Background The phantom sound of tinnitus can be an extremely debilitating condition. The thalamocortical dysrhythmia (TCD) hypothesis is a feature of subjective tinnitus; however, its consistency in characterizing different types of tinnitus remains unclear. Method We compared theta–beta/ga...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2025-06-01
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| Series: | Brain and Behavior |
| Online Access: | https://doi.org/10.1002/brb3.70437 |
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| Summary: | ABSTRACT Background The phantom sound of tinnitus can be an extremely debilitating condition. The thalamocortical dysrhythmia (TCD) hypothesis is a feature of subjective tinnitus; however, its consistency in characterizing different types of tinnitus remains unclear. Method We compared theta–beta/gamma coupling in multichannel EEG recordings from subjects with bothersome tinnitus (BT), non‐bothersome tinnitus (NBT), and healthy controls (HC). Additionally, we used these EEG features to distinguish BT from NBT by employing the k‐nearest neighbor (KNN) model. Results Theta–beta/gamma phase‐amplitude coupling (PAC) was enhanced in the auditory cortex of both BT and NBT groups compared to HC. In contrast, theta–beta/gamma PAC was specifically enhanced in the cingulate gyrus and parahippocampal gyrus in the BT group. Notably, theta–gamma PAC in the orbitofrontal cortex was attenuated in the BT group and showed a negative correlation with their THI scores. By integrating theta–beta/gamma PAC into a machine learning algorithm, up to 92% of BT patients were accurately identified. Conclusion TCD in BT patients was characterized by enhanced theta–beta/gamma PAC in the auditory cortex, cingulate gyrus, and parahippocampal gyrus and attenuated theta–gamma PAC in the orbitofrontal cortex. The latter was significantly negatively correlated with THI scores. These PAC features, which could objectively distinguish BT patients by machine learning, may support the specificity of PAC features in tinnitus characterization. |
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| ISSN: | 2162-3279 |