Project #11491
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#11491 : MOODel: Modeling Mood Disorders
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Name of Applicant : Aroldo A. Dargél
Date of application : 11-04-2018
Unit : Perception and Memory
Location : Fernbach 1st floor
Phone : 01 44 38 95 24
@ Mail :
@ PI-Mail :

Project context and summary :

Mood disorders such as bipolar and major depressive illnesses are among the most severe psychiatric disorders. They have high prevalence and chronic course, and are associated with significant mental and somatic comorbidities and high personal and societal costs (lost productivity and increased medical expenses). Patients with bipolar disorder (BD), for example, exhibit a reduced lifespan compared with the general population, a finding that cannot only be explained by high suicide risk, reduced access to medical care and lifestyle factors. However, the pathophysiological mechanisms of BD are poorly understood, and patients often have incomplete treatment response. Advanced mathematical approaches such as machine learning techniques are increasingly being used to generate predictions based on complex data, and it has been successfully used to detect a number of clinical outcomes and to predict behaviours. In combination with mobile technologies (e.g. smartphones, wearables) to collect behavioural, physiological and environmental data, these big data predictive approaches may provide a much richer and deeper understanding of phenomenology and pathophysiological mechanisms of mood and bipolar disorders. By taking advantage of the high-standard bioinformatics expertise offered by the C3BI, this multidisciplinary, collaborative project aims to explore how clinical and biological factors, may contribute for better characterizing BD patients as well as to identify predictors of treatment response in BD. Our project also aims to explore how daily behavioural and physiological parameters may influence mood and behaviour in individuals at-risk or suffering from mood disorders.

Related team publications :
Dargél AA, Roussel F, Volant S et al. Emotional Hyper-Reactivity and Cardiometabolic Risk in Remitted Bipolar Patients: A Machine Learning Approach. Acta Psychiatrica Scandinavica 2018, in review
Dargél AA, Godin O, Etain B et al. Emotional reactivity, functioning, and C-reactive protein alterations in remitted bipolar patients: Clinical relevance of a dimensional approach. Aust New Zeal J Psychiatry 2017;51:788–798.
Dargél AA, Godin O, Kapczinski F et al. C-reactive protein alterations in bipolar disorder: a meta-analysis. J Clin Psychiatry 2015;76:142–150.
Service Delivery
Project Manager :
Project Type : Long
Status : Closed
Publication : 10.1111/bdi.12927;10.1159/000493690;10.1111/acps.12901

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