title: Stratification of first episode psychosis based on clinical and neurobiological features: from single-center studies to big data
Creators: name: Pigoni, Alessandro
Publisher: name: IMT School for Advanced Studies Lucca
publisherIdentifier: https://ror.org/035gh3a49
publisherIdentifierScheme: ROR
Year: 2024

description: Psychosis is a common and functionally disruptive clinical syndrome that might be present in many psychiatric, neurodevelopmental, neurologic, and medical conditions. Rather than a nosological entity, psychosis is a syndrome characterized by different symptoms and domains.
Therefore, an increasing amount of pointed out the importance of recognising and treating a first episode as soon as possible. For these reasons, first episode psychosis (FEP) rapidly became a very important population of study and assessment. More than just the first symptomatic presentation of a disease, FEP often shows already some of the features of the advanced psychiatric illnesses, although to a minor extent. On the other hand, great efforts are being made in order to establish an effective intervention, given the fact that early treatment has been proved to ameliorate the course of the disease, ranging from symptoms, relapse, and number of hospitalisations to quality-of-life measures such as involvement in school or work and global functioning.
Given the multifactorial nature of FEP and the different trajectories it can follow (e.g., affective vs. non-affective psychosis), the possibility of predicting future trajectories and
to obtain clear and valid biomarkers is becoming of paramount importance.
Prediction modelling has the potential to revolutionize medicine by predicting individual patient outcome. Early identification of those with good and poor outcomes would allow for a more personalised approach to care, matching interventions and resources to those most at need.
Through a series of studies, we explored: 1) the possibility to stratify FEP patients based on neuroimaging and biological measures; 2) the possibility of use cutting edge machine learning techniques to improve classification and cluster subtypes of FEP patients; 3) the presence of autoimmune features in FEP in a multi-site study I had the opportunity to coordinate as Co-PI (namely the PHLAMES study).
Specifically, in single-site studies we showed that neuroimaging and biological variables can be predictive of the course of the disease. Moreover, in large multi-site bid data analyses we presented how machine learning can improve the prediction of the disorder and help in stratify the risk, using both clinical and neuroimaging data. Finally, in the first results of PHLAMES study emerged that a subsample of FEP with
autoimmune characteristics might be defined. This subsample shows some unique features in terms of neurological symptoms, cognitive deficits, and brain imaging alterations.
The studies presented in this dissertation point out that it is possible to dissect the clinical and biological heterogeneity of psychosis at the beginning of its disease course, by defining meaningful groups of patients and therefore tailor personalized management. In conclusion, these data foster the research for subtypes of FEP and the definition of disease trajectories. These advances might have a great impact on patients’ lives, by defining specific subgroups or progression that benefit of tailored interventions.