Triple network activation causes tinnitus in patients with sudden sensorineural hearing loss: A model-based volume-entropy analysis

Lee, Seung-Jae; Park, Jaemin; Lee, Sang Yeon; Koo, Ja-Won; Vanneste, Sven; Ridder, Dirk De; Lim, Seonhee; Song, Jae-Jin

doi:10.3389/fnins.2022.1028776

Cited by 5 publications

(9 citation statements)

References 99 publications

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“…The involvement of the insula and the rostral anterior cingulate cortex highlights their significance in the salience network or ventral attention network, crucial for detecting and filtering salient external stimuli and internal events, thereby facilitating the transition between activated networks such as the default mode network and the central executive or frontoparietal network. The superior frontal gyrus and the frontal pole, implicated in executive function and attentional control, are key components of the frontoparietal network, underscoring their role in directing attention and managing cognitive resources, which could be particularly relevant in modulating attention towards or away from tinnitus sounds [90]. Additionally, the involvement of sensory integration and executive regions suggests a complex interplay between auditory processing and higher-order cognitive functions, emphasizing the multisensory and multidimensional nature of tinnitus perception within these overarching neural networks.…”

Section: Discussionmentioning

confidence: 99%

Prediction of acoustic tinnitus suppression using resting state EEG: An explainable AI approach

Shabestari,

Schoisswohl,

Wellauer

et al. 2024

Preprint

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Tinnitus, characterized by the perception of sound without an external source, affects a significant portion of the population and can lead to considerable individual suffering, yet understanding of its suppression remains limited. Understanding neural traits of tinnitus suppression may be crucial for developing accurate predictive models in tinnitus research and treatment. This study aims to classify individuals capable of brief acoustic tinnitus suppression (BATS; also known as residual inhibition) based on their independent resting state EEG (n=102), exploring the classification’s robustness on various sample splits, and the relevance of resulting specific EEG features in the spirit of explainable AI. A comprehensive set of EEG features, including band power in standard frequency bands, spectral entropy, aperiodic slope and offset of the power spectrum, and connectivity, was included in both sensor and source space. Binary classification of the BATS status was performed using a comprehensive set of standard classifiers and Pearson correlation for feature selection, which addresses multicollinearity, avoiding complex dimensionality reduction techniques. Feature importance was assessed using Gini impurity metrics, allowing interpretation of the directionality of identified neural features. The Random Forest model showed the most consistent performance, with its majority voting mechanism effectively reducing overfitting and providing reliable predictions, and was therefore chosen for subsequent feature interpretation analysis. Our classification task demonstrated high accuracy across the various BATS split thresholds, suggesting that the choice of threshold does not significantly influence the underlying pattern in the data. We achieved classification accuracies of 98% for sensor and source models and 86% for the connectivity model in the main split. Looking at identified important features, our findings align with and extend existing neuroscience research in tinnitus by discovering highly specific and novel neural features in naive resting-state data predictive of BATS. Gamma power is identified as the most important feature in the sensor model, followed by alpha power, which fits current models of sensory processing, prediction, and updating (gamma) as well as inhibitory (alpha) frameworks. The overall spectral shape of the EEG power spectrum tends to be more normal in +BATS individuals, as reflected in the aperiodic offset and slope features. In the source model, important features are lateralized in that the gamma feature is more prominent in the left core auditory network, whereas the alpha feature is distributed more sparsely over the right hemisphere in line with auditory attention data. Furthermore, we identified several hotspots in the temporal, insular, parietal, parahippocampal, medial prefrontal, and (posterior) cingulate cortex implicated in sensory processing, gating, attention, and memory processes. Relevant network features were found in a hyperconnected bilateral auditory network (within the network), while the full auditory network was hyperconnected to limbic regions (between networks), which may reflect an intact sensory gating mechanism aiding tinnitus suppression. This study’s implications extend to improving the understanding and prediction of tinnitus loudness perception and tinnitus distress as well as its (acoustic) suppression. Furthermore, our approach underscores the importance of careful feature selection, model choice, and validation strategies in analyzing complex neurophysiological data.

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Section: Discussionmentioning

confidence: 99%

Prediction of acoustic tinnitus suppression using resting state EEG: An explainable AI approach

Shabestari,

Schoisswohl,

Wellauer

et al. 2024

Preprint

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“…Previous studies have shown that patients with obesity have reduced total brain and gray matter volume, which is attributed to neuroinflammation. Furthermore, the fronto-temporal regions, which are involved in the noise cancellation pathway, are also associated with obesity 3 , 22 , 23 . Since tinnitus is linked to the noise cancellation pathway 3 , these structural changes in the brain may contribute to the onset and persistence of tinnitus.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the fronto-temporal regions, which are involved in the noise cancellation pathway, are also associated with obesity 3 , 22 , 23 . Since tinnitus is linked to the noise cancellation pathway 3 , these structural changes in the brain may contribute to the onset and persistence of tinnitus.…”

Section: Discussionmentioning

confidence: 99%

“…In such cases, research has found correlations between tinnitus and various physical conditions, including pain, infection, and sleep quality, as well as mental health issues like anxiety and depression 1 , 2 . Furthermore, subjective tinnitus has a significant relationship with brain metabolism and structure 3 . Therefore, some diseases that cause changes in the brain network through chronic inflammatory responses or are associated with structural and functional changes in the brain might be connected to subjective tinnitus 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

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Associations between tinnitus and body composition: a cross-sectional study

Han,

Lee,

Suh

et al. 2024

Sci Rep

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The relationship between tinnitus and body composition in specific regions has not been extensively investigated. This study aimed to identify associations between tinnitus and body composition. Individuals with data on physical and otological examination findings, and bioelectrical impedance analysis were included from the ninth Korea National Health and Nutritional Examination Survey. They were divided into a tinnitus group and a non-tinnitus group. Participants with tinnitus were further classified into acute or chronic tinnitus group. The tinnitus group showed significantly higher body fat percentages in each region (arms: P = 0.014; legs: P = 0.029; trunk: P = 0.008; whole body: P = 0.010) and waist circumference (P = 0.007) than the non-tinnitus group, and exhibited lower leg muscle percentage (P = 0.038), total body fluid percentage (P = 0.010), and intracellular fluid percentage (P = 0.009) than the non-tinnitus group in men. Furthermore, men with chronic tinnitus showed a significantly higher trunk fat percentage (P = 0.015) and waist circumference (P = 0.043), and lower intracellular fluid percentage (P = 0.042) than their counterparts without tinnitus. No significant differences in body composition were observed among the groups in the female population. In men, body composition may be associated with tinnitus.

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“… 6 Previous studies of the causes of tinnitus, especially in neuroscience, have revealed possible mechanisms for tinnitus. 1 7 8 9 10 However, these mechanisms are still not sufficient to comprehensively define tinnitus owing to its highly heterogenous physiopathology.…”

Section: Introductionmentioning

confidence: 99%

Consensus Statements on the Definition, Classification, and Diagnostic Tests for Tinnitus: A Delphi Study Conducted by the Korean Tinnitus Study Group

Choo,

Kim,

Lee

et al. 2024

J Korean Med Sci

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Background Tinnitus is a bothersome condition associated with various symptoms. However, the mechanisms of tinnitus are still uncertain, and a standardized assessment of the diagnostic criteria for tinnitus is required. We aimed to reach a consensus on diagnosing tinnitus with professional experts by conducting a Delphi study with systematic review of the literature. Methods Twenty-six experts in managing tinnitus in Korea were recruited, and a two-round modified Delphi study was performed online. The experts evaluated the level of agreement of potential criteria for tinnitus using a scale of 1–9. After the survey, a consensus meeting was held to establish agreement on the results obtained from the Delphi process. Consensus was defined when over 70% of the participants scored 7–9 (agreement) and fewer than 15% scored 1–3 (disagreement). To analyze the responses of the Delphi survey, the content validity ratio and Kendall’s coefficient of concordance were evaluated. Results Consensus was reached for 22 of the 38 statements. For the definition of tinnitus, 10 out of 17 statements reached consensus, with three statements achieving complete agreement including; 1) Tinnitus is a conscious perception of an auditory sensation in the absence of a corresponding external stimulus, 2) Tinnitus can affect one’s quality of life, and 3) Tinnitus can be associated with hearing disorders including sensorineural hearing loss, vestibular schwannoma, Meniere’s disease, otosclerosis, and others. For the classification of tinnitus, 11 out of 18 statements reached consensus. The participants highly agreed with statements such as; 1) Vascular origin is expected in pulse-synchronous tinnitus, and 2) Tinnitus can be divided into acute or chronic tinnitus. Among three statements on the diagnostic tests for tinnitus only Statement 3, “There are no reliable biomarkers for sensory or emotional factors of tinnitus.” reached consensus. All participants agreed to perform pure-tone audiometry and tinnitus questionnaires, including the Tinnitus Handicap Inventory and Tinnitus Questionnaire. Conclusion We used a modified Delphi method to establish a consensus-based definition, a classification, and diagnostic tests for tinnitus. The expert panel reached agreement for several statements, with a high level of consensus. This may provide practical information for clinicians in managing tinnitus.

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Triple network activation causes tinnitus in patients with sudden sensorineural hearing loss: A model-based volume-entropy analysis

Cited by 5 publications

References 99 publications

Prediction of acoustic tinnitus suppression using resting state EEG: An explainable AI approach

Prediction of acoustic tinnitus suppression using resting state EEG: An explainable AI approach

Associations between tinnitus and body composition: a cross-sectional study

Consensus Statements on the Definition, Classification, and Diagnostic Tests for Tinnitus: A Delphi Study Conducted by the Korean Tinnitus Study Group

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