Eavesdropping on Tinnitus Using MEG: Lessons Learned and Future Perspectives

Aberrant auditory prediction patterns robustly characterize tinnitus

Phantom perceptions like tinnitus occur without any identifiable environmental or bodily source. The mechanisms and key drivers behind tinnitus are poorly understood. The dominant framework, suggesting that tinnitus results from neural hyperactivity in the auditory pathway following hearing damage, has been difficult to investigate in humans and has reached explanatory limits. As a result, researchers have tried to explain perceptual and potential neural aberrations in tinnitus within a more parsimonious predictive-coding framework. In two independent magnetoencephalography studies, participants passively listened to sequences of pure tones with varying levels of regularity (i.e. predictability) ranging from random to ordered. Aside from being a replication of the first study, the pre-registered second study, including 80 participants, ensured rigorous matching of hearing status, as well as age, sex, and hearing loss, between individuals with and without tinnitus. Despite some changes in the details of the paradigm, both studies equivalently reveal a group difference in neural representation, based on multivariate pattern analysis, of upcoming stimuli before their onset. These data strongly suggest that individuals with tinnitus engage anticipatory auditory predictions differently to controls. While the observation of different predictive processes is robust and replicable, the precise neurocognitive mechanism underlying it calls for further, ideally longitudinal, studies to establish its role as a potential contributor to, and/or consequence of, tinnitus.

Astrocytes and Tinnitus

Tinnitus is correlated with anomalies of neural plasticity and has been found to be affected by inflammatory status. The current theories on tinnitus, although still somewhat incomplete, are based on maladaptive plasticity mechanisms. Astrocytes play a major role in both neural responses to inflammation and plasticity regulation; moreover, they have recently been discovered to encode "context" for neuronal circuits, which is similar to the "expectation" of Bayesian brain models. Therefore, this narrative review explores the possible and likely roles of astrocytes in the neural mechanisms leading to acute and chronic tinnitus.

Chronic tinnitus is associated with aging but not dementia

AIM

Aging is related to deterioration of bodily and neural functions, leading to various disorders and symptoms, including the development of dementia, hearing loss, or tinnitus. Understanding how these phenomena are intertwined and how aging affects those is crucial for prevention and the future development of interventions.

METHODS

We utilized the UK Biobank which includes a total of 502,382 participants between 40 and 70 years old. We used logistic regression models and cox proportional hazard models and compared hazard ratios.

RESULTS

The odds of reporting tinnitus in the older age group (i.e., older than 58 years) were increased by 53.6 % and a one decibel increase in the speech-reception thresholds enhanced the odds for tinnitus by 13.0 %. For our second analysis regarding hearing loss, the risk of dementia increased by 14.0 % with an increase by one decibel in the speech-reception threshold score. In terms of aging, each additional year increased the risk by 17.3 %. Tinnitus alone showed a significant influence with a hazard ratio of 52.1 %, however, when adding hearing loss, age and various covariates, the effect vanished.

CONCLUSION

Findings confirm that tinnitus is indeed related to aging, but presumably independent of the aging processes accompanying the development of dementia. This highlights the urge to further investigate the impact of aging on neural processes that are relevant for alterations in the auditory systems (e.g., leading to the development of tinnitus or hearing loss) as well as for increased vulnerability in terms of neurodegenerative diseases.

Tinnitus classification based on resting-state functional connectivity using a convolutional neural network architecture

OBJECTIVES

Many studies have investigated aberrant functional connectivity (FC) using resting-state functional MRI (rs-fMRI) in subjective tinnitus patients. However, no studies have verified the efficacy of resting-state FC as a diagnostic imaging marker. We established a convolutional neural network (CNN) model based on rs-fMRI FC to distinguish tinnitus patients from healthy controls, providing guidance and fast diagnostic tools for the clinical diagnosis of subjective tinnitus.

METHODS

A CNN architecture was trained on rs-fMRI data from 100 tinnitus patients and 100 healthy controls using an asymmetric convolutional layer. Additionally, a traditional machine learning model and a transfer learning model were included for comparison with the CNN, and each of the three models was tested on three different brain atlases.

RESULTS

Of the three models, the CNN model outperformed the other two models with the highest area under the curve, especially on the Dos_160 atlas (AUC = 0.944). Meanwhile, the model with the best classification performance highlights the crucial role of the default mode network, salience network, and sensorimotor network in distinguishing between normal controls and patients with subjective tinnitus.

CONCLUSION

Our CNN model could appropriately tackle the diagnosis of tinnitus patients using rs-fMRI and confirmed the diagnostic value of FC as measured by rs-fMRI.