Depression and bipolar disorder are frequent, debilitating conditions. Both are thought to be primarily caused by an impaired regulation of mood, which is why they are sometimes referred to as "mood disorders". However, the biological basis of mood remains poorly understood, which is a major limitation for the development of new treatments. Recent work that combines neuroscience with mathematical models are promising to better understand mood and to link it to its biological basis, but they don't have any medical application yet. Can these models describe mood in a way that is relevant to mood disorders, and help doctors and psychologists predict subsequent clinical evolution? With the objective of extending this framework to real-life fluctuations and to assess its clinical relevance, this study will combine a neuroimaging session with a smartphone-based, longitudinal follow-up. Three groups of 96 subjects each will be recruited: depressive disorder, bipolar disorder and healthy controls. They will have their mood fluctuations assessed first in the lab (in the neuroimaging experiment), then in their daily lives (by providing a few ratings and choices every day on the smartphone app). This study will allow to better understand the differences in how patients' mood reacts to daily events, as compared to people who don't suffer from depression or bipolar disorder. The combination of the two steps will allow to assess whether a short neuroimaging evaluation can be useful to predict subsequent clinical evolution during the following months. The investigators wanted to add two optional ancillary studies. The first uses a mobile application for implicit, passive, and longitudinal mood assessments through emotion tracking. Indeed, it seems relevant to add this type of evaluation alongside explicit assessments to more accurately detect mood fluctuations. The second study uses a mobile application that allows voice recordings. The analysis of these vocal parameters will help to characterize a specific linguistic and vocal profile within the three groups, as well as to identify specific symptoms of conditions such as depression and bipolar disorder. These ancillary studies will be offered to both patients and the control group.
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Computational phenotypes of daily mood fluctuations in the 3 groups
Timeframe: From enrolment to Month 12