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The rapid rise and now widespread distribution of handheld and wearable devices, such as smartphones, fitness trackers, or smartwatches, has opened a new universe of possibilities for monitoring emotion and cognition in everyday-life context, and for applying experience- and context-specific interventions in psychosis. These devices are equipped with multiple sensors, recording channels, and app-based opportunities for assessment using experience sampling methodology (ESM), which enables to collect vast amounts of temporally highly resolved and ecologically valid personal data from various domains in daily life. In psychosis, this allows to elucidate intermediate and clinical phenotypes, psychological processes and mechanisms, and their interplay with socioenvironmental factors, as well as to evaluate the effects of treatments for psychosis on important clinical and social outcomes. Although these data offer immense opportunities, they also pose tremendous challenges for data analysis. These challenges include the sheer amount of time series data generated and the many different data modalities and their specific properties and sampling rates. After a brief review of studies and approaches to ESM and ecological momentary interventions in psychosis, we will discuss recurrent neural networks (RNNs) as a powerful statistical machine learning approach for time series analysis and prediction in this context. RNNs can be trained on multiple data modalities simultaneously to learn a dynamical model that could be used to forecast individual trajectories and schedule online feedback and intervention accordingly. Future research using this approach is likely going to offer new avenues to further our understanding and treatments of psychosis. [ABSTRACT FROM AUTHOR]

An intense fear of abandonment or rejection is a central feature of social relationships for individuals with borderline personality disorder (BPD). A total of 20 unmedicated BPD patients and 20 healthy participants (HC, matched for age and education) played a virtual ball-tossing game including the three conditions: exclusion, inclusion and a control condition with predefined game rules, whereas cerebral activity was assessed using functional magnetic resonance imaging. Subjective experiences of exclusion were assessed after each blocked condition. Both groups felt similarly excluded during the exclusion condition; however, BPD subjects felt more excluded than HC during the inclusion and control conditions. In all three conditions, BPD patients showed a stronger engagement of the dorsal anterior cingulate and medial prefrontal cortex. For HC, activation in several cerebral regions such as the insula and the precuneus differed depending on the interaction situation, whereas for BPD subjects activation in these regions was not modulated by experimental conditions. Subjects with BPD differed from HC in both their subjective reactions to and their neural processing of social interaction situations. Our data suggest that individuals with BPD have difficulty in discriminating between social situations, and tend to hypermentalize during social encounters that are not determined by the intentions of others. [ABSTRACT FROM AUTHOR]