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Decoding Tinnitus: Unveiling the Socio-Psychological, Cognitive, and Brain Mechanisms

Project description

Listening for answers: new research offers hope for tinnitus

Tinnitus, often referred to ‘ringing in the ears’, is a condition where people hear sounds that are not actually present. This constant noise can lead to serious issues like anxiety, depression, and trouble sleeping, affecting daily life. Despite its impact, there are no effective medications to treat it. Understanding and managing tinnitus is challenging due to its complex causes. With the support of the Marie Skłodowska-Curie Actions programme, the HISSING project aims to address this problem by using large datasets and machine learning. By studying these factors, the project hopes to improve tinnitus prediction, understand its brain mechanisms, and discover better ways to manage and treat the condition.

Objective

"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."

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global Fellowships

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Call for proposal

(opens in new window) HORIZON-MSCA-2023-PF-01

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Coordinator

INSTITUT PASTEUR
Net EU contribution
€ 268 025,28
Address
RUE DU DOCTEUR ROUX 25-28
75724 Paris
France
Region
Ile-de-France Ile-de-France Paris
Activity type
Research Organisations
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Total cost
No data

Partners (2)