Project description
AI-powered protocol to restore emotional stability
Emotion regulation (ER) is crucial for mental well-being. Disruptions in the brain’s ER circuitry are a transdiagnostic mechanism underlying a wide range of affective disorders, significantly contributing to the economic burden of mental health issues in Europe. Supported by the Marie Skłodowska-Curie Actions program, the RESoNATE project focuses on major depressive disorder, which is expected to become the leading cause of global disability by 2030. The project aims to validate the effectiveness of an electroencephalography (EEG) neurofeedback protocol that targets ER circuitry. It will also explore deep learning algorithms for EEG data analysis and investigate industrial potential through collaboration with a startup. RESoNATE seeks to advance mental health interventions and push the boundaries of AI methods.
Objective
Emotion regulation (ER) is the key to mental well-being. Disruptions in the brain ER circuitry is a transdiagnostic mechanism underpinning a wide spectrum of affective disorders that contribute to the mental health economic burden of 600 billion euros annually in Europe. This project focuses on Major Depressive Disorder (MDD), predicted to become the leading cause of global disability by 2030. Conventional treatment is ineffective for approximately 40-60% of patients, underscoring the crucial need for innovative therapeutic approaches. In this context, neurofeedback (NF) on ER with functional magnetic resonance imaging has emerged as a promising tool for the treatment of MDD in research settings. NF is a training that enables people to regulate their brain activity by providing real-time feedback on specific brain patterns, in this case on brain areas involved in ER. To bridge the gap between clinical practice and academic research, RESoNATE will validate the clinical effectiveness of an electroencephalography (EEG) NF protocol targeting ER circuitry. Going beyond the current state of the art, we will conduct a placebo-controlled study involving 72 MDD patients, stratified into NF, sham-NF(placebo), and control groups. A collaboration with the National Kaohsiung Medical University (Taiwan) will add a second NF dataset of 60 subjects. The second goal entails advancing the validation of novel deep learning algorithms for EEG time-series data analysis and testing generalization to different cultural datasets, deploying them to predict the success of NF—a key challenge within the field. By incorporating a startup into the project, we will explore the industrial potential of this research. As such, through RESoNATE and its objectives psychiatrists, psychologists, AI experts, and industrial innovators will come together to advance mental health interventions and explore the boundaries of state-of-the-art AI methods, unlocking new research avenues and market opportunities.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been human-validated.
- natural sciences computer and information sciences data science
- social sciences economics and business business and management innovation management
- medical and health sciences clinical medicine psychiatry
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2023-PF-01
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
67081 Strasbourg
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.