Classification of psychosis spectrum disorders using graph convolutional networks with structurally constrained functional connectomes.

This article considers the problem of classifying individuals in a dataset of diverse psychosis spectrum conditions, including persons with subsyndromal psychotic-like experiences (PLEs) and healthy controls. This task is more challenging than the traditional problem of distinguishing patients with a diagnosed disorder from controls using brain network features, since the neurobiological differences between PLE individuals and healthy persons are less pronounced. Further, examining a transdiagnostic sample compared to controls is concordant with contemporary approaches to understanding the full spectrum of neurobiology of psychoses.

Identifying trajectories of change in sleep disturbance during psychological treatment for depression.

Background: Sleep disturbance may impact response to psychological treatment for depression. Understanding how sleep disturbance changes during the course of psychological treatment, and identifying the risk factors for sleep disturbance response may inform clinical decision-making.

Method: This analysis included 18,915 patients receiving high-intensity psychological therapy for depression from one of eight London-based Improving Access to Psychological Therapies (IAPT) services between 2011 and 2020.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study.

Objective: Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters.

Understanding the psychological therapy treatment outcomes for young adults who are not in education, employment, or training (NEET), moderators of outcomes, and what might be done to improve them.

Background: To determine: whether young adults (aged 18-24) not in education, employment or training (NEET) have different psychological treatment outcomes to other young adults; any socio-demographic or treatment-related moderators of differential outcomes; and whether service-level changes are associated with better outcomes for those who are NEET.

Methods: A cohort was formed of 20 293 young adults treated with psychological therapies in eight Improving Access to Psychological Therapies services. Pre-treatment characteristics, outcomes, and moderators of differential outcomes were compared for those who were and were not NEET.