Altered Resting-State EEG Microstate in Idiopathic Sudden Sensorineural Hearing Loss Patients With Tinnitus

A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

The Functional Aspects of Resting EEG Microstates: A Systematic Review

A growing body of clinical and cognitive neuroscience studies have adapted a broadband EEG microstate approach to evaluate the electrical activity of large-scale cortical networks. However, the functional aspects of these microstates have not yet been systematically reviewed. Here, we present an overview of the existing literature and systematize the results to provide hints on the functional role of electrical brain microstates. Studies that evaluated and manipulated the temporal properties of resting-state microstates and utilized questionnaires, task-initiated thoughts, specific tasks before or between EEG session(s), pharmacological interventions, neuromodulation approaches, or localized sources of the extracted microstates were selected. Fifty studies that met the inclusion criteria were included. A new microstate labeling system has been proposed for a comprehensible comparison between the studies, where four classical microstates are referred to as A-D, and the others are labeled by the frequency of their appearance. Microstate A was associated with both auditory and visual processing and links to subjects' arousal/arousability. Microstate B showed associations with visual processing related to self, self-visualization, and autobiographical memory. Microstate C was related to processing personally significant information, self-reflection, and self-referential internal mentation rather than autonomic information processing. In contrast, microstate E was related to processing interoceptive and emotional information and to the salience network. Microstate D was associated with executive functioning. Microstate F is suggested to be a part of the Default Mode Network and plays a role in personally significant information processing, mental simulations, and theory of mind. Microstate G is potentially linked to the somatosensory network.

Normative Temporal Dynamics of Resting EEG Microstates

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

Objective Neurophysiological Indices for the Assessment of Chronic Tinnitus Based on EEG Microstate Parameters

Chronic tinnitus is highly prevalent but lacks precise diagnostic or effective therapeutic standards. Its onset and treatment mechanisms remain unclear, and there is a shortage of objective assessment methods. We aim to identify abnormal neural activity and reorganization in tinnitus patients and reveal potential neurophysiological markers for objectively evaluating tinnitus. By way of analyzing EEG microstates, comparing metrics under three resting states (OE, CE, and OECEm) between tinnitus sufferers and controls, and correlating them with tinnitus symptoms. This study reflected specific changes in the EEG microstates of tinnitus patients across multiple resting states, as well as inconsistent correlations with tinnitus symptoms. Microstate parameters were significantly different when patients were in OE and CE states. Specifically, the occurrence of Microstate A and the transition probabilities (TP) from other Microstates to A increased significantly, particularly in the CE state (32-37%, p ≤ 0.05 ); and both correlated positively with the tinnitus intensity. Nevertheless, under the OECEm state, increases were mainly observed in the duration, coverage, and occurrence of Microstate B (15-47%, ), which negatively correlated with intensity ( [Formula: see text]-0.513, ). Additionally, TPx between Microstates C and D were significantly reduced and positively correlated with HDAS levels ( [Formula: see text] 0.548, ). Furthermore, parameters of Microstate D also correlated with THI grades ( [Formula: see text]-0.576, ). The findings of this study could offer compelling evidence for central neural reorganization associated with chronic tinnitus. EEG microstate parameters that correlate with tinnitus symptoms could serve as neurophysiological markers, contributing to future research on the objective assessment of tinnitus.

EEG spectral and microstate analysis originating residual inhibition of tinnitus induced by tailor-made notched music training

Tailor-made notched music training (TMNMT) is a promising therapy for tinnitus. Residual inhibition (RI) is one of the few interventions that can temporarily inhibit tinnitus, which is a useful technique that can be applied to tinnitus research and explore tinnitus mechanisms. In this study, RI effect of TMNMT in tinnitus was investigated mainly using behavioral tests, EEG spectral and microstate analysis. To our knowledge, this study is the first to investigate RI effect of TMNMT. A total of 44 participants with tinnitus were divided into TMNMT group (22 participants; ECnm, NMnm, RInm represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by TMNMT music, respectively) and Placebo control group (22 participants; ECpb, PBpb, RIpb represent that EEG recordings with eyes closed stimuli-pre, stimuli-ing, stimuli-post by Placebo music, respectively) in a single-blind manner. Behavioral tests, EEG spectral analysis (covering delta, theta, alpha, beta, gamma frequency bands) and microstate analysis (involving four microstate classes, A to D) were employed to evaluate RI effect of TMNMT. The results of the study showed that TMNMT had a stronger inhibition ability and longer inhibition time according to the behavioral tests compared to Placebo. Spectral analysis showed that RI effect of TMNMT increased significantly the power spectral density (PSD) of delta, theta bands and decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of TMNMT had shorter duration (microstate B, microstate C), higher Occurrence (microstate A, microstate C, microstate D), Coverage (microstate A) and transition probabilities (microstate A to microstate B, microstate A to microstate D and microstate D to microstate A). Meanwhile, RI effect of Placebo decreased significantly the PSD of alpha2 band, and microstate analysis showed that RI effect of Placebo had shorter duration (microstate C, microstate D), higher occurrence (microstate B, microstate C), lower coverage (microstate C, microstate D), higher transition probabilities (microstate A to microstate B, microstate B to microstate A). It was also found that the intensity of tinnitus symptoms was significant positively correlated with the duration of microstate B in five subgroups (ECnm, NMnm, RInm, ECpb, PBpb). Our study provided valuable experimental evidence and practical applications for the effectiveness of TMNMT as a novel music therapy for tinnitus. The observed stronger residual inhibition (RI) ability of TMNMT supported its potential applications in tinnitus treatment. Furthermore, the temporal dynamics of EEG microstates serve as novel functional and trait markers of synchronous brain activity that contribute to a deep understanding of the neural mechanism underlying TMNMT treatment for tinnitus.

Neural oscillations after acute large artery atherosclerotic cerebral infarction during resting state and sleep spindles

Electroencephalogram-microstate analysis was conducted using low-resolution electromagnetic tomography (LORETA)-KEY to evaluate dynamic brain network changes in patients with acute large artery atherosclerotic cerebral infarction (LAACI) during the rest and sleep stages. This study included 35 age- and sex-matched healthy controls and 34 patients with acute LAACI. Each participant performed a 3-h, 19-channel video electroencephalogram test. Subsequently, 20 epochs of 2-s sleep spindles during stage N2 sleep and five epochs of 10-s electroencephalogram data in the resting state for each participant were obtained. In both the resting state and sleep spindles, patients with LAACI displayed altered neural oscillations. The parameters of microstate A (coverage, occurrence, and duration) increased during the resting state in the patients with LAACI compared with healthy controls. The coverage and occurrence of microstate B and D were reduced in the LAACI group compared with the healthy controls (p < 0.05). Moreover, during sleep spindles, the duration of microstate A and the transition probability from microstate A and B to C decreased, but the coverage of microstate B and the transition rate from microstate B to D increased (p < 0.05) in the LAACI group compared with the healthy controls. These results enable better understanding of how neural oscillations are modified in patients with LAACI during the resting state and sleep spindles. Following LAACI, the dynamic brain network undergoes changes during sleep spindles and the resting state. Continued long-term investigations are required to determine how well these changes in brain dynamics reflect the clinical characteristics of patients with LAACI.

Study on tinnitus-related electroencephalogram microstates in patients with vestibular schwannomas

Tinnitus is closely associated with cognition functioning. In order to clarify the central reorganization of tinnitus in patients with vestibular schwannoma (VS), this study explored the aberrant dynamics of electroencephalogram (EEG) microstates and their correlations with tinnitus features in VS patients. Clinical and EEG data were collected from 98 VS patients, including 76 with tinnitus and 22 without tinnitus. Microstates were clustered into four categories. Our EEG microstate analysis revealed that VS patients with tinnitus exhibited an increased frequency of microstate C compared to those without tinnitus. Furthermore, correlation analysis demonstrated that the Tinnitus Handicap Inventory (THI) score was negatively associated with the duration of microstate A and positively associated with the frequency of microstate C. These findings suggest that the time series and syntax characteristics of EEG microstates differ significantly between VS patients with and without tinnitus, potentially reflecting abnormal allocation of neural resources and transition of functional brain activity. Our results provide a foundation for developing diverse treatments for tinnitus in VS patients.

Efficacy of Tailor-Made Notched Music Training Versus Tinnitus Retraining Therapy in Adults With Chronic Subjective Tinnitus: A Randomized Controlled Clinical Trial

OBJECTIVES

Chronic subjective tinnitus can have a serious effect on daily life, even causing serious psychological disorders. Currently there are no specific effective solutions or cures. Tailor-made notched music training (TMNMT) is a recently proposed sound therapy that has simpler processes and a higher compliance rate than tinnitus retraining therapy (TRT), a widely used treatment for chronic subjective tinnitus. This study explores the therapeutic effect of TMNMT in comparison to TRT to highlight its clinical value.

DESIGN

The study was a randomized controlled, single-blinded clinical trial. One hundred twenty eligible participants were randomly assigned to receive TMNMT (n = 60) or TRT (n = 60) for 3 mo with concurrent follow-up. It should be noted that the duration of sound treatment in TRT was modified to 2 hr per day for better feasibility in practice. The primary outcome was mean change in tinnitus handicap inventory (THI) measured at baseline ( T0 ), 1 mo ( T1 ) and 3 mo ( T2 ) after intervention. Change in visual analog scale (VAS) was measured as a secondary outcome. A comparison of therapeutic effectiveness between TMNMT and TRT was evaluated by repeated measure analysis of variance.

RESULTS

One hundred and twelve (93%) of participants took part in the study, of which 64 were men and 48 women. Mean (SD) age was 42.80 (12.91) years. Fifty-eight were allocated to receive TMNMT and 54 to receive TRT. The between-group difference in primary outcome was -6.90 points (95% confidence interval [CI], -13.53 to -0.27) at T1 and -6.17 points (95% CI, -13.04 to 0.71) at T2 . These results closely reached to clinical significance of tinnitus-related effective relief. For the secondary outcome, the mean value in the TMNMT group was 0.83 points (95% CI, 0.12 to 1.54), significantly lower than the mean value of the TRT group. The differences in THI and VAS between the two groups were statistically significant after intervention. Further analysis showed that age and baseline THI and VAS scores were associated with change in THI and VAS scores after interventions.

CONCLUSIONS

Both TMNMT and TRT were able to alleviate chronic subjective tinnitus effectively after a 3 month intervention. When the two forms of therapy were compared TMNMT appeared to be more effective and consequently potentially superior to TRT for reducing tinnitus loudness and functional and emotional disturbance associated with chronic subjective tinnitus.

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.

Altered resting-state electroencephalography microstate characteristics in young male smokers

The development of nicotine addiction was associated with the abnormalities of intrinsic functional networks during the resting state in young adult smokers. As a whole-brain imaging approach, EEG microstate analysis treated multichannel EEG recordings as a series of quasi-steady microscopic states which were related to the resting-state networks (RSNs) found by fMRI. The aim of this study was to examine whether the resting-state EEG microstate analysis may provide novel insights into the abnormal temporal properties of intrinsic brain activities in young smokers. We used 64-channel resting-state EEG datasets to investigate alterations in microstate characteristics between twenty-five young smokers and 25 age- and gender-matched non-smoking controls. Four classic EEG microstates (microstate A, B, C, and D) were obtained, and the four temporal parameters of each microstate were extracted, i.e., duration, occurrence, coverage, and transition probabilities. Compared with non-smoking controls, young smokers showed decreased occurrence of microstate C and increased duration of microstate D. Furthermore, both the duration and coverage of microstate D were significantly negatively correlated with Fagerstrom Test of Nicotine Dependence (FTND) in young smoker group. The complex changes in the microstate time-domain parameters might correspond to the abnormalities of RSNs in analyses of FC measured with fMRI in the previous studies and indicate the altered specific brain functions in young smokers. Microstate D could be potentially represented as a selective biomarker for predicting the dependence degree of adolescent smokers on cigarettes. These results suggested that EEG microstate analysis might detect the deviant functions of large-scale cortical activities in young smokers and provide a new perspective for the study of brain networks of adolescent smokers.

Altered Resting-State Electroencephalography Microstates in Idiopathic Generalized Epilepsy: A Prospective Case-Control Study

Objective: Idiopathic generalized epilepsy (IGE) involves aberrant organization and functioning of large-scale brain networks. This study aims to investigate whether the resting-state EEG microstate analysis could provide novel insights into the abnormal temporal and spatial properties of intrinsic brain activities in patients with IGE.

Methods: Three groups of participants were chosen for this study (namely IGE-Seizure, IGE-Seizure Free, and Healthy Controls). EEG microstate analysis on the resting-state EEG datasets was conducted for all participants. The average duration (“Duration”), the average number of microstates per second (“Frequency”), as well as the percentage of total analysis time occupied in that state (“Coverage”) of the EEG microstate were compared among the three groups.

Results: For microstate classes B and D, the differences in Duration, Frequency, and Coverage among the three groups were not statistically significant. Both Frequency and Coverage of microstate class A were statistically significantly larger in the IGE-Seizure group than in the other two groups. The Duration and Coverage of microstate class C were statistically significantly smaller in the IGE-Seizure group than those in the other two groups.

Conclusions: The Microstate class A was regarded as a sensorimotor network and Microstate class C was mainly related to the salience network, this study indicated an altered sensorimotor and salience network in patients with IGE, especially in those who had experienced seizures in the past 2 years, while the visual and attention networks seemed to be intact.

Significance: The temporal dynamics of resting-state networks were studied through EEG microstate analysis in patients with IGE, which is expected to generate indices that could be utilized in clinical researches of epilepsy.

Disrupted Topological Organization in White Matter Networks in Unilateral Sudden Sensorineural Hearing Loss

Sudden sensorineural hearing loss (SSNHL) is a sudden-onset hearing impairment that rapidly develops within 72 h and is mostly unilateral. Only a few patients can be identified with a defined cause by routine clinical examinations. Recently, some studies have shown that unilateral SSNHL is associated with alterations in the central nervous system. However, little is known about the topological organization of white matter (WM) networks in unilateral SSNHL patients in the acute phase. In this study, 145 patients with SSNHL and 91 age-, gender-, and education-matched healthy controls were evaluated using diffusion tensor imaging (DTI) and graph theoretical approaches. The topological properties of WM networks, including global and nodal parameters, were investigated. At the global level, SSNHL patients displayed decreased clustering coefficient, local efficiency, global efficiency, normalized clustering coefficient, normalized characteristic path length, and small-worldness and increased characteristic path length (p < 0.05) compared with healthy controls. At the nodal level, altered nodal centralities in brain regions involved the auditory network, visual network, attention network, default mode network (DMN), sensorimotor network, and subcortical network (p < 0.05, Bonferroni corrected). These findings indicate a shift of the WM network topology in SSNHL patients toward randomization, which is characterized by decreased global network integration and segregation and is reflected by decreased global connectivity and altered nodal centralities. This study could help us understand the potential pathophysiology of unilateral SSNHL.

Treatment Effect of Exercise Intervention for Female College Students with Depression: Analysis of Electroencephalogram Microstates and Power Spectrum

This paper aims to assess the effect of exercise intervention on the improvement of college students with depression and to explore the change characteristics of microstates and the power spectrum in their resting-state electroencephalogram (EEG). Forty female college students with moderate depression were screened according to the Beck Depression Inventory-II (BDI-II) and Depression Self-Rating Scale (SDS) scores, and half of them received an exercise intervention for 18 weeks. The study utilized an EEG to define the resting-state networks, and the scores of all the participants were tracked during the intervention. Compared with those in the depression group, the power spectrum values in the θ and α bands were significantly decreased (p < 0.05), and the duration of microstate C increased significantly (p < 0.05), while the frequency of microstate B decreased significantly (p < 0.05) in the exercise intervention group. The transition probabilities showed that the exercise intervention group had a higher probability from B to D than those in the depression group (p < 0.01). In addition, the power of the δ and α bands were negatively correlated with the occurrence of microstate C (r = −0.842, p < 0.05 and r = −0.885, p < 0.01, respectively), and the power of the β band was positively correlated with the duration of microstate C (r = 0.900, p < 0.01) after exercise intervention. Our results suggest that the decreased duration of microstate C and the increased α power in depressed students are associated with reduced cognitive ability, emotional stability, and brain activity. Depression symptoms were notably improved after exercise intervention, thus providing a more scientific index for the research, rehabilitation mechanisms, and treatment of depression.

EEG microstate features for schizophrenia classification

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.

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.

Microstate in resting state: an EEG indicator of tinnitus?

Background: Tinnitus was a subjective auditory phantom phenomenon which could be highly distressing and had no objective indicators for evaluation.Objectives: The aim of this study was to investigate whether and how did electroencephalography (EEG) microstates of subjective tinnitus patients change in resting state and whether could be an objective indicator for tinnitus.Material and Methods: We enrolled chronic subjective tinnitus patients and matched age and gender with healthy controls. EEG recording, microstate analysis and statistical analysis were performed.Results: We finally had 10 male and 8 female age-matched participants in tinnitus group and healthy control group. Statistical differences were found in the microstate A, C and D durations between the two groups (class A, p = .024; class B, p = .018; class D, p = .029). Microstate durations of class A and D had linear correlation with VAS scores in tinnitus patients (microstate A [R spare = 0.43, p = .003*]; microstate D [R spare = 0.46, p = .002*]).Conclusions: Microstates had changed in chronic tinnitus patients and provided an indicator or perspective to explore the mechanisms of tinnitus. The maintenance of chronic subjective tinnitus may be related to changes in cerebral cortex activity.

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

This study aimed to identify the mechanism behind idiopathic sudden sensorineural hearing loss (ISSNHL) in patients with tinnitus by investigating aberrant activity in areas of the brain and functional connectivity. High-density electroencephalography (EEG) was used to investigate central nervous changes in 25 ISSNHL subjects and 27 healthy controls. ISSNHL subjects had significantly reduced activity in the left frontal lobe at the alpha 2 frequency band compared with controls. Linear lagged connectivity and lagged coherence analysis showed significantly reduced functional connectivity between the temporal gyrus and supramarginal gyrus at the gamma 2 frequency band in the ISSNHL group. Additionally, a significantly reduced functional connectivity was found between the central cingulate gyrus and frontal lobe under lagged phase synchronization analysis. These results strongly indicate inhibition of brain area activity and change in functional connectivity in ISSNHL with tinnitus patients.