Inhibition of Brain Area and Functional Connectivity in Idiopathic Sudden Sensorineural Hearing Loss With Tinnitus, Based on Resting-State EEG

Abnormal Functional Connectivity Within Default Mode Network and Salience Network Related to Tinnitus Severity

BACKGROUND

Previous studies have demonstrated that tinnitus is associated with neural changes in the cerebral cortex. This study is aimed at investigating the central nervous characteristics of tinnitus patients with different severity by using a rs-EEG.

PARTICIPANTS AND METHODS

rs-EEG was recorded in fifty-seven patients with chronic tinnitus and twenty-seven healthy controls. Tinnitus patients were divided into moderate-to-severe tinnitus group and slight-to-mild tinnitus group based on their Tinnitus Handicap Inventory (THI) scores. Source localization and functional connectivity analyses were used to measure the changes in central levels and examine the altered network patterns. The correlation between functional connectivity and tinnitus severity was analyzed.

RESULT

Compared to the healthy controls, all tinnitus patients showed significant activation in the auditory cortex (middle temporal lobe, BA 21), while moderate-to-severe tinnitus group showed enhanced connectivity between the parahippocampus and posterior cingulate gyrus. Moreover, the moderate-to-severe tinnitus group had enhanced functional connectivity between auditory cortex and insula compared to the slight-to-mild tinnitus group. The connections between the insula and the parahippocampal and posterior cingulate gyrus were positively correlated with THI scores.

CONCLUSION

The current study reveals that patients with moderate-to-severe tinnitus demonstrate greater changes in the central brain areas, including the auditory cortex, insula, parahippocampus and posterior cingulate gyrus. In addition, enhanced connections were found between the insula and the auditory cortex, as well as the posterior cingulate gyrus and the parahippocampus, which suggests abnormality in the auditory network, salience network, and default mode network. Specifically, the insula is the core region of the neural pathway that is composed of the auditory cortex, insula, and parahippocampus/posterior cingulate gyrus. This suggests that the severity of tinnitus is affected by multiple brain regions.

Prognostic Factors Influencing the Tinnitus Improvement After Idiopathic Sudden Sensorineural Hearing Loss Treatment

PURPOSE

Logistic regression analysis was used to explore the factors that influence tinnitus improvement after idiopathic sudden sensorineural hearing loss (ISSNHL) treatment.

MATERIALS AND METHODS

In this retrospective study, 137 ISSNHL patients with tinnitus were recruited at the Sun Yatsen Memorial Hospital of Sun Yat-sen University, China. They underwent audiological examinations, vestibular assessments, tinnitus examinations, a Tinnitus Handicap Inventory (THI) assessment and ISSNHL treatments. Logistic regression analysis was performed to investigate factors that affected tinnitus improvement.

RESULTS

Participants were divided into an effective group (73 patients) and noneffective group (64 patients) according to THI scores before and after treatment. The effective group had better averaged hearing threshold than the noneffective group (effective group vs. noneffective group: 74.47 vs. 87.66 dB HL; t = 3.03, p < 0.05). Additionally, before intervention there were significant difference in profound audiogram configuration (effective group vs. noneffective group: 17.81% vs. 46.88%, x2 = 23.63; p < 0.001), mid tinnitus pitch (effective group vs. noneffective group: 19.18% vs. 35.94%, x2 = 6.58; p = 0.037) and mean THI scores (effective group vs. noneffective group: 57.07 ± 22.27 vs. 36.78 ± 24.41, t = -5.09, p < 0.001) between the effective and noneffective tinnitus groups. Logistic regression analysis showed that audiogram configurations (profound audiogram: OR = 0.10, 95% CI 0.01-0.72, p = 0.022), tinnitus pitch (mid tinnitus pitch: OR = 0.16, 95% CI 0.05-0.57, p = 0.004) and THI scores (OR = 1.05, 95% CI 1.03-1.07, p < 0.001) were independent factors associated with tinnitus improvement.

CONCLUSION

Audiogram configuration, tinnitus pitch, and THI scores before intervention appear to be predictive of the effectiveness of acute tinnitus improvement following ISSNHL treatment.

A survey of brain network analysis by electroencephalographic signals

Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features

Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in electroencephalography (EEG) signals can be used as the biomarkers for an efficient and fast diagnosis method for chronic tinnitus.

Methods: In this study, the resting-state EEG signals of tinnitus patients with different tinnitus locations were recorded. Four connectivity features [including the Phase-locking value (PLV), Phase lag index (PLI), Pearson correlation coefficient (PCC), and Transfer entropy (TE)] and two time-frequency domain features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources.

Results: Classification accuracy was highest when the SVM algorithm or the MLP algorithm was applied to the PCC feature sets, achieving final average classification accuracies of 99.42 or 99.1%, respectively. And based on the PLV feature, the classification result was also particularly good. And MLP ran the fastest, with an average computing time of only 4.2 s, which was more suitable than other methods when a real-time diagnosis was required.

Conclusion: Connectivity features of the resting-state EEG signals could characterize the differentiation of tinnitus location. The connectivity features (PCC and PLV) were more suitable as the biomarkers for the objective diagnosing of tinnitus. And the results were helpful for clinicians in the initial diagnosis of tinnitus.

Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques

Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.

Cortical Oscillatory Signatures Reveal the Prerequisites for Tinnitus Perception: A Comparison of Subjects With Sudden Sensorineural Hearing Loss With and Without Tinnitus

Just as the human brain works in a Bayesian manner to minimize uncertainty regarding external stimuli, a deafferented brain due to hearing loss attempts to obtain or "fill in" the missing auditory information, resulting in auditory phantom percepts (i.e., tinnitus). Among various types of hearing loss, sudden sensorineural hearing loss (SSNHL) has been extensively reported to be associated with tinnitus. However, the reason that tinnitus develops selectively in some patients with SSNHL remains elusive, which led us to hypothesize that patients with SSNHL with tinnitus (SSNHL-T) and those without tinnitus (SSNHL-NT) may exhibit different cortical activity patterns. In the current study, we compared resting-state quantitative electroencephalography findings between 13 SSNHL-T and 13 SSNHL-NT subjects strictly matched for demographic characteristics and hearing thresholds. By performing whole-brain source localization analysis complemented by functional connectivity analysis, we aimed to determine the as-yet-unidentified cortical oscillatory signatures that may reveal potential prerequisites for the perception of tinnitus in patients with SSNHL. Compared with the SSNHL-NT group, the SSNHL-T group showed significantly higher cortical activity in Bayesian inferential network areas such as the frontopolar cortex, orbitofrontal cortex (OFC), and pregenual anterior cingulate cortex (pgACC) for the beta 3 and gamma frequency bands. This suggests that tinnitus develops in a brain with sudden auditory deafferentation only if the Bayesian inferential network updates the missing auditory information and the pgACC-based top-down gatekeeper system is actively involved. Additionally, significantly increased connectivity between the OFC and precuneus for the gamma frequency band was observed in the SSNHL-T group, further suggesting that tinnitus derived from Bayesian inference may be linked to the default mode network so that tinnitus is regarded as normal. Taken together, our preliminary results suggest a possible mechanism for the selective development of tinnitus in patients with SSNHL. Also, these areas could serve as the potential targets of neuromodulatory approaches to preventing the development or prolonged perception of tinnitus in subjects with SSNHL.

Alterations of brain activity and functional connectivity in transition from acute to chronic tinnitus

The objective of this study was to investigate alterations to brain activity and functional connectivity in patients with tinnitus, exploring neural features in the transition from acute to chronic phantom perception. Twenty‐four patients with acute tinnitus, 23 patients with chronic tinnitus, and 32 healthy controls were recruited. High‐density electroencephalography (EEG) was used to explore changes in brain areas and functional connectivity in different groups. When compared with healthy subjects, acute tinnitus patients had a significant reduction in superior frontal cortex activity across all frequency bands, whereas chronic tinnitus patients had a significant reduction in the superior frontal cortex at beta 3 and gamma frequency bands as well as a significant increase in the inferior frontal cortex at delta‐band and superior temporal cortex at alpha 1 frequency band. When compared to the chronic tinnitus group, the acute tinnitus group activity was significantly increased in the middle frontal and parietal gyrus at the gamma‐band. Functional connectivity analysis showed that the chronic tinnitus group had increased connections between the parahippocampus gyrus, posterior cingulate cortex, and precuneus when compared with the healthy group. Alterations of local brain activity and connections between the parahippocampus gyrus and other nonauditory areas appeared in the transition from acute to chronic tinnitus. This indicates that the appearance and development of tinnitus is a dynamic process involving aberrant local neural activity and abnormal connectivity in multifunctional brain networks.

Altered Brain Activity and Functional Connectivity in Unilateral Sudden Sensorineural Hearing Loss

Background

Sudden sensorineural hearing loss (SSNHL) is an otologic emergency and could lead to social difficulties and mental disorders in some patients. Although many studies have analyzed altered brain function in populations with hearing loss, little information is available about patients with idiopathic SSNHL. This study is aimed at investigating brain functional changes in SSNHL via functional magnetic resonance imaging (fMRI).

Methods

Thirty-six patients with SSNHL and thirty well-matched normal hearing individuals underwent resting-state fMRI. Amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and functional connectivity (FC) values were calculated.

Results

In the SSNHL patients, ALFF and fALFF were significantly increased in the bilateral putamen but decreased in the right calcarine cortex, right middle temporal gyrus (MTG), and right precentral gyrus. Widespread increases in FC were observed between brain regions, mainly including the bilateral auditory cortex, bilateral visual cortex, left striatum, left angular gyrus (AG), bilateral precuneus, and bilateral limbic lobes in patients with SSNHL. No decreased FC was observed.

Conclusion

SSNHL causes functional alterations in brain regions, mainly in the striatum, auditory cortex, visual cortex, MTG, AG, precuneus, and limbic lobes within the acute period of hearing loss.

Aberrant Functional and Causal Connectivity in Acute Tinnitus With Sensorineural Hearing Loss

Purpose

The neural bases in acute tinnitus remains largely undetected. The objective of this study was to identify the alteration of the brain network involved in patients with acute tinnitus and hearing loss.

Methods

Acute tinnitus patients (n = 24) with hearing loss and age-, sex-, education-matched healthy controls (n = 21) participated in the current study and underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Regional homogeneity and amplitude of low-frequency fluctuation were used to investigate the local spontaneous neural activity and functional connectivity (FC), and Granger causality analysis (GCA) was used to analyze the undirected and directed connectivity of brain regions.

Results

Compared with healthy subjects, acute tinnitus patients had a general reduction in FC between auditory and non-auditory brain regions. Based on FC analysis, the superior temporal gyrus (STG) revealed reduced undirected connectivity with non-auditory brain regions including the amygdala (AMYG), nucleus accumbens (NAc), the cerebellum, and postcentral gyrus (PoCG). Using the GCA algorithm, increased effective connectivity from the right AMYG to the right STG, and reduced connectivity from the right PoCG to the left NAc was observed in acute tinnitus patients with hearing loss. The pure-tone threshold was positively correlated with FC between the AMYG and STG, and negatively correlated with FC between the left NAc and the right PoCG. In addition, a negative association between the GCA value from the right PoCG to the left NAc and the THI scores was observed.

Conclusion

Acute tinnitus patients have aberrant FC strength and causal connectivity in both the auditory and non-auditory cortex, especially in the STG, AMYG, and NAc. The current findings will provide a new perspective for understanding the neuropathophysiological mechanism in acute tinnitus.