Assessment of the treatment needs of community recovery Services in Spain: From the perspective of service users, families, and mental health professionals.

Profile of Community Recovery Services users has changed over the years and has become more diverse. To explore the evolution of treatment needs, this study aimed to identify users' needs, from the point of view of different agents implicated in the recovery process. We explored the consistency between the agents using the focus group technique. We defined four groups (n = 58): service users, family members, professionals, and referring professionals. We pre-identified topics related to recovery, such as illness-related losses, imaginary of CRS, expectations, activities, and life goals. All agents recognised losses related to the mental illness, the need for carrying activities out of the Community Recovery Services, and for including families in the recovery process. The groups differed in some areas, such as the identification of activities that should be encouraged, or the importance of promoting vital expectations. Our findings suggest that it is important to identify the needs of different agents involved in the recovery process. There is consistency in the service users' needs, but there are some differences that need to be considered. Interventions should be personalised, covering functional, cognitive, and relational losses related to the mental illness.

Early prediction of developing spontaneous activity in cultured neuronal networks.

Synchronization and bursting activity are intrinsic electrophysiological properties of in vivo and in vitro neural networks. During early development, cortical cultures exhibit a wide repertoire of synchronous bursting dynamics whose characterization may help to understand the parameters governing the transition from immature to mature networks. Here we used machine learning techniques to characterize and predict the developing spontaneous activity in mouse cortical neurons on microelectrode arrays (MEAs) during the first three weeks in vitro. Network activity at three stages of early development was defined by 18 electrophysiological features of spikes, bursts, synchrony, and connectivity. The variability of neuronal network activity during early development was investigated by applying k-means and self-organizing map (SOM) clustering analysis to features of bursts and synchrony. These electrophysiological features were predicted at the third week in vitro with high accuracy from those at earlier times using three machine learning models: Multivariate Adaptive Regression Splines, Support Vector Machines, and Random Forest. Our results indicate that initial patterns of electrical activity during the first week in vitro may already predetermine the final development of the neuronal network activity. The methodological approach used here may be applied to explore the biological mechanisms underlying the complex dynamics of spontaneous activity in developing neuronal cultures.