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Background: Disruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown., Methods: We acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15-25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning., Results: Individuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error-corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error-corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes., Conclusions: Brainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objective: The current guidelines recommend continuation of antipsychotic medication for a minimum of at least 1 year following a first episode of psychosis (FEP). There have been several trials investigating whether early dose reduction or cessation leads to improved functional outcomes. The aim of this study was to explore the experience of consenting to and participating in a randomized controlled trial (RCT) of antipsychotic medication cessation., Method: Five participants in the Reduce trial-an RCT evaluating early antipsychotic medication dose reduction/cessation following FEP-aged 22-24 years completed a semistructured qualitative interview following the RCT. Interpretive phenomenological analysis was undertaken to understand the key themes., Results: A superordinate theme was derived from interviews: the Liminal Space of FEP and treatment. Themes within the Liminal Space included: rejection versus identification with psychosis, medication as symbolic of illness versus wellness, embodiment of wellness and illness with medication, medication as symbolic of independence versus dependence, discovery of independence when autonomously choosing medication, the Reduce trial offered safety to navigate the liminal space, and self-exploration versus altruism., Conclusions and Implications for Practice: The experience and treatment of FEP involves young people feeling torn between multiple, competing perspectives, demands, and priorities. Participation in an RCT exploring dose reduction provided additional supports contributing to the perception of greater safety to navigate their own experiences of treatment that was appropriate for them. When treatment is experienced as collaborative, involves shared decision making and support, other than medication, young people feel more equipped to navigate the liminal space. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The detection of seismic activity precursors as part of an alarm system will provide opportunities for minimization of the social and economic impact caused by earthquakes. It has long been envisaged, and a growing body of empirical evidence suggests that the Earth's electromagnetic field could contain precursors to seismic events. The ability to capture and monitor electromagnetic field activity has increased in the past years as more sensors and methodologies emerge. Missions such as Swarm have enabled researchers to access near-continuous observations of electromagnetic activity at second intervals, allowing for more detailed studies on weather and earthquakes. In this paper, we present an approach designed to detect anomalies in electromagnetic field data from Swarm satellites. This works towards developing a continuous and effective monitoring system of seismic activities based on SWARM measurements. We develop an enhanced form of a probabilistic model based on the Martingale theories that allow for testing the null hypothesis to indicate abnormal changes in electromagnetic field activity. We evaluate this enhanced approach in two experiments. Firstly, we perform a quantitative comparison on well-understood and popular benchmark datasets alongside the conventional approach. We find that the enhanced version produces more accurate anomaly detection overall. Secondly, we use three case studies of seismic activity (namely, earthquakes in Mexico, Greece, and Croatia) to assess our approach and the results show that our method can detect anomalous phenomena in the electromagnetic data.

Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks 1 . Accurate and timely warnings are critical for mitigating flood risks 2 , but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that artificial intelligence-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a five-day lead time that is similar to or better than the reliability of nowcasts (zero-day lead time) from a current state-of-the-art global modelling system (the Copernicus Emergency Management Service Global Flood Awareness System). In addition, we achieve accuracies over five-year return period events that are similar to or better than current accuracies over one-year return period events. This means that artificial intelligence can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed here was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings., (© 2024. The Author(s).)