Mood changes reflect the adaptive psychological functioning of the individual in most cases but they may reflect serious psychopathological conditions when extreme (i.e. mood disorders). Despite advances in pharmaco- and psychosocial therapies, depression and bipolar disorders are still common, severe and chronic mental disorders with a high economic social impact. Moreover, the identification of risk factors and high rates of relapse and recurrence are still major problems. A way to face these issues concerns the identification of patterns of signals and factors (from biological to psychosocial) that best differentiate normal mood changes from pathological ones. Beyond mild symptoms, possible candidates of these variables are variations in the levels of cognitive biases, self-regulatory emotional processes and heart rate variability. From a biological point of view new evidences support the role of small non-coding RNAs (miRNAs) as causal factors of neuropsychiatric disorders. In particular, miR-212 and miR-29c, which are differentially expressed in salivary exosomes from schizophrenic and bipolar patients, may represent non invasive, early markers of neuroplastic changes underlying mood alterations.
The present project will attempt to individuate factors involved in mood alterations in normal, depressed and bipolar subjects. To this aim, by longitudinal statistical models, we will study the temporal relation among mood changes, emotion regulation, cognition, heart rate variability, and levels of microRNAs. Subjective data will be collected through an internet-based ecological momentary assessment system, already developed and in use by our group, which can be remotely employed by patients to monitor their own mood and provide relevant clues for predicting mood changes. Physiological and biological measures will be collected during ambulatory sessions and immediately following the exceeding of symptoms cut-off.
