The experience sampling methodology in psychosis risk states: A systematic review.

The experience sampling method (ESM) is a structured diary technique, which is used to assess thoughts, mood and appraise subjective experiences in daily life. It has been recognized as a useful tool for understanding the characteristics, dynamics, and underlying mechanisms of prodromal symptoms of psychosis. The present systematic review aimed to provide a qualitative synthesis of findings provided by the ESM studies conducted in people with psychosis risk states. A systematic review of the MEDLINE, ERIC, Academic Search Ultimate, and Health Source: Nursing/Academic Edition databases, utilizing search terms related to the ESM and the risk of psychosis was conducted. Out of 1069 publication records identified, 77 studies met the inclusion criteria for the review. Data were synthesized around the following topics: 1) assessment of symptoms dynamics and social functioning; 2) assessment of the mechanisms contributing to the emergence of psychotic experiences and 3) assessment of stress sensitivity. The studies have shown that negative emotions are associated with subsequent development of paranoia. The tendency to draw hasty conclusions, aberrant salience, self-esteem, and emotion regulation were the most frequently reported mechanisms associated with the emergence of psychotic experiences. Studies using the ESM also provided evidence for the role of stress sensitivity, in the development of psychotic symptoms. The ESM has widely been applied to studies investigating psychosis risk states, using a variety of protocols. Findings from this systematic review might inform future studies and indicate potential targets for interventions.

Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models.

The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.