Emerging Topics in the Behavioral Neuroscience of Tinnitus

Searchfield, Grant D.; Zhang, Jinsheng; Biswas, Roshni; Ridder, Dirk De; Deutsch, Brian C.; Hall, Deborah A.; Hébert, Sylvie; Kleinjung, Tobias; Kleinstäuber, Maria; Langguth, Berthold; López-Escámez, José A.; Maslin, Michael; Mazurek, Birgit; Piccirillo, Jay F.; Salvi, Richard; Schlee, Winfried; Shulman, Abraham; Shore, Susan E.; Szczepek, Agnieszka J.; Smith, Paul F.; Theodoroff, Sarah M.; Vajsakovic, Dunja; Weise, Cornelia; Zheng, Yiwen

doi:10.1007/7854_2020_217

Cited by 4 publications

(7 citation statements)

References 137 publications

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“…Tinnitus patients have been surveyed about their preferences in apps ( 113 ), but aside from a small trials ( 119 ) evidence of benefit from apps for tinnitus is absent. Sereda et al ( 113 ) found the five most commonly used apps in descending order from highest were: ( 1 ) White Noise Free, ( 2 ) Oticon Tinnitus Sound, ( 3 ) Relax Melodies–Sleep Sounds, ( 4 ) myNoise, and ( 5 ) Tinnitus Therapy Lite. Due to the fast development and release of new apps on various platforms, the list of apps is ever-growing and changing.…”

Section: Resultsmentioning

confidence: 99%

“…Tinnitus is commonly referred to as “ringing in the ears;” it is the perception of a sound in the absence of a sound source. Tinnitus is the result of a complex cascade of changes within the auditory and emotional networks of the brain that occur following ear or head injury ( 1 ). Tinnitus can have mild through to catastrophic effect on life-quality; it can disrupt hearing, attention and sleep, result in anxiety, and depression ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

“…The 3rd generation of digital tinnitus technologies emerged from a plethora of new therapy concepts based on specific, putative, neurophysiological mechanisms ( 1 ). Third generation therapies attempt to personalize to a characteristic of the users' tinnitus ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

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A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Searchfield

Sanders

Doborjeh

et al. 2021

Front. Digit. Health

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Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies.Aim: To review technologies that will enable the next generations of digital therapies for tinnitus.Methods: A “state-of-the-art” review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011–2021. The search strategy used the following key words: “tinnitus” and [“HA,” “personalized therapy,” “AI” (and “methods” or “applications”), “Virtual reality,” “Games,” “Sensors” and “Transducers”], and “Hearables.” Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes.Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined.Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Searchfield

Sanders

Doborjeh

et al. 2021

Front. Digit. Health

Self Cite

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show abstract

“…Data-driven tinnitus research requires either large datasets to account for heterogeneity or in-depth repeated measures in individuals. Artificial intelligence can be applied to these datasets such that it can extract patterns in big-data or it learns personal preferences for decision making in individuals (Song et al, 2017;Schlee et al, 2021;Searchfield et al, 2021b). Complex relationships between genetic, demographic, lifestyle, and other environmental factors create the variance in tinnitus datasets (Lopez-Escamez et al, 2016).…”

Section: Improve Research Quality and Diversitymentioning

confidence: 99%

“…With the aid of machine learning algorithms we may soon be able to accurately distinguish tinnitus from a non-tinnitus neural activity at an individual level that can help inform treatment (Han et al, 2019;Durai et al, 2020). A very promising avenue for tinnitus assessment is ecological momentary analysis using questionnaires (Deutsch and Piccirillo, 2021) or behavioral measures coupled with wearable sensors measuring physiological function (Searchfield et al, 2021b). Ecological Momentary Analysis samples subjective states regularly while biosensors for example within smartwatches can objectively monitor physiology (Schlee et al, 2016;Goldberg et al, 2017).…”

Section: Develop Smart Assessments To Inform Treatmentmentioning

confidence: 99%