Summary
"Tinnitus disorder, often referred to as ""ringing in the ears,"" is a disabling medical condition characterized by a subjective perception of sound without any external acoustic stimulus. It is linked with a range of psycho-social disturbances, including anxiety, depression, stress, irritability, concentration difficulties, and sleep disorders, resulting in behavioral changes and functional disability. Given the lack of effective drug treatments, comprehensive public health actions are needed. First, prevention requires the identification of risk factors predicting tinnitus onset and associated distress. Secondly, improving tinnitus clinical management depends on identifying factors predicting the worsening, stability, or improvement of tinnitus distress over time. Finally, understanding the mechanisms underlying tinnitus by integrating socio-psychological and cerebral aspects is crucial to better address this pathology and provide leads to find treatments. We will address the methodological limitations typically associated with small datasets using two complementary datasets. The UK Biobank provides information on the socio-economic background, the physical and mental health of 500,000 participants with longitudinal assessments. The Audicog dataset ensures high precision in hearing and cognitive evaluations on 300 participants with or without tinnitus. We will use machine learning technics on the UK Biobank data to identify i) the socio-demographic, psychological, cognitive and health related factors predicting tinnitus apparition and evolution over time. The validity of those risk factors will be tested during a clinical study performed in a tinnitus clinic. We will also determine ii) the cerebral patterns associated with tinnitus using machine learning algorithm. We will explore the links between the cerebral patterns and the sociopsychological, auditory, emotional and cognitive factors with the Audicog dataset to establish an integrative model of tinnitus."
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| Web resources: | https://cordis.europa.eu/project/id/101146406 |
| Start date: | 01-06-2024 |
| End date: | 31-05-2027 |
| Total budget - Public funding: | - 268 025,00 Euro |
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Original description
"Tinnitus disorder, often referred to as ""ringing in the ears,"" is a disabling medical condition characterized by a subjective perception of sound without any external acoustic stimulus. It is linked with a range of psycho-social disturbances, including anxiety, depression, stress, irritability, concentration difficulties, and sleep disorders, resulting in behavioral changes and functional disability. Given the lack of effective drug treatments, comprehensive public health actions are needed. First, prevention requires the identification of risk factors predicting tinnitus onset and associated distress. Secondly, improving tinnitus clinical management depends on identifying factors predicting the worsening, stability, or improvement of tinnitus distress over time. Finally, understanding the mechanisms underlying tinnitus by integrating socio-psychological and cerebral aspects is crucial to better address this pathology and provide leads to find treatments. We will address the methodological limitations typically associated with small datasets using two complementary datasets. The UK Biobank provides information on the socio-economic background, the physical and mental health of 500,000 participants with longitudinal assessments. The Audicog dataset ensures high precision in hearing and cognitive evaluations on 300 participants with or without tinnitus. We will use machine learning technics on the UK Biobank data to identify i) the socio-demographic, psychological, cognitive and health related factors predicting tinnitus apparition and evolution over time. The validity of those risk factors will be tested during a clinical study performed in a tinnitus clinic. We will also determine ii) the cerebral patterns associated with tinnitus using machine learning algorithm. We will explore the links between the cerebral patterns and the sociopsychological, auditory, emotional and cognitive factors with the Audicog dataset to establish an integrative model of tinnitus."Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
06-10-2024
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