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.
Mood disorders are characterized by abnormal mood fluctuations, in which individual¿s functioning is significantly deteriorated. Although psychiatric, neuropsychological, neurobiological and psychological research has clearly highlighted the differences between normal, depressed and (hypo-)maniacal subjects, both in acute and remission phases, results on mechanisms that regulate the transition from a normal mood alteration to a pathological condition are scarce and clinically irrelevant. This project attempts to fill this gap relying on the general hypothesis that mood disorders are, at least to some extent, the consequence of dysfunctional self-regulatory processes leading to mood worsening or elevation (Aldao et al., 2010; Strauman, 2017). For example, in depression the fatigue-passivity vicious circle is typically observed: the more the person feels tired, the more his active behavior decreases, as an attempt to solve the tiredness itself. Passivity, in turn, increases the sense of fatigue, resulting in a dysfunctional solution attempt. In contrast, in bipolar disorders, it can be hypothesized that tiredness is perceived as a threat to goal achievements. This leads the person to find solutions that counteract fatigue (e.g. stimulant consumption) and that as a consequence alter sleep-wake cycle. Although these mechanisms have been investigated using experimental paradigms (Harvey, 2004) in the lab, longitudinal ecological studies that might guarantee generalizability are still lacking.
A second factor of innovation is the attempt to relate levels of analysis that are traditionally considered separate (biological vs psychophysiological vs psychological). To our knowledge, the project proposes the first study that, by means of an EMA paradigm and longitudinal models, attempt to investigate at the same time molecular, psychophysiological and cognitive-behavioral variables.
Finally, at present there is no possibility to evaluate reliably the preclinical indicators of relapse such as biological markers or physiological signals (Vieta et al., 2011; Andreazza et al., 2008). For instance, previous studies showed changes on several parameters regarding sleep (Iverson et al., 2002) circadian heart rate rhythms (Taillard et al., 1993) and the hormonal system (Glassman et al., 1998). These parameters may be considered predictors of clinical changes according to the clinical status but none of these studies have reached an acceptable level of accuracy for clinical use. Therefore, in the current scientific literature, no validated mood recognition system using long-term monitoring of mood and physiological changes of bipolar patients has ever been proposed. By contrast our project will correlate the longitudinal monitorings of single subjects for their mood status and of fluctuations of possible biomarkers of mood changes. The system can be used at home by the patient and provide both patients and clinicians with helpful clues and feedback (for a potential relapse, remission and, in general, mood change). It will permit to adapt therapy and to implement coping strategies where conditions could induce a relapse (before the relapse) and also allow to create awareness about patient's condition. Moreover, the longitudinal monitoring method allows to regularly collect clinical data in a more natural setting, thus enabling to apply the results to everyday clinical practice. In our opinion, this is a significant improvement over artificial lab settings and qualitative studies that do not permit to reach appropriate levels of external validity.
Our EMA system could be efficient in cost-benefit terms, making the clinical and scienti¿c bene¿ts available for everydays clinical practice and could be a useful addition to the clinical questionnaires. On the other hand, identification of biomarkers reflecting mood disorders-specific pathophysiologic processes, will predict the onset of mood changes and provide novel biological targets for the development of personalized treatments (Culpepper et al., 2014).