Connectome-based predictive modeling of early and chronic psychosis symptoms
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Core beliefs in psychosis: a systematic review and meta-analysis
Increasing interest is growing for the identification of psychological mechanisms to account for the influence of trauma on psychosis, with core beliefs being proposed as a putative mediator to account for this relationship. A systematic review (n = 79 studies) was conducted to summarise the existing evidence base regarding the role of core beliefs/schemas in psychosis, Clinical High-Risk (CHR), and non-clinical samples with Psychotic-Like Experiences (PLEs). Compared to Healthy Controls (HCs), individuals with psychosis experiencing Auditory Hallucinations or Persecutory Delusions had significantly higher scores for negative self and negative other-beliefs and significantly lower scores for positive self and positive other-beliefs. This pattern of core beliefs was also observed for CHR individuals. In contrast, the core belief profile for grandiose delusions was in the opposite direction: higher positive self and positive other-beliefs and lower negative self-beliefs. In non-clinical samples, several factors mediated the relationship between Traumatic Life Events (TLEs) and PLEs, such as greater perceived stress, dissociation, external locus of control, and negative self and negative other-beliefs. Compared to HCs, meta-analyses revealed statistically significant large effects for negative self and negative other-beliefs in Schizophrenia. In CHR, statistically significant large and moderate effects were found for negative self and negative other-beliefs, respectively, along with a moderate negative effect for positive self-beliefs. Core beliefs were found to play a significant role in the development and maintenance of positive symptoms of psychosis. The development of psychosocial interventions that explicitly target negative self and other-beliefs, whilst also enhancing positive self-beliefs are warranted and would innovate CBTp practices.
Clinical prediction model for transition to psychosis in individuals meeting At Risk Mental State criteria
The At Risk Mental State (ARMS) (also known as the Ultra or Clinical High Risk) criteria identify individuals at high risk for psychotic disorder. However, there is a need to improve prediction as only about 18% of individuals meeting these criteria develop a psychosis with 12-months. We have developed and internally validated a prediction model using characteristics that could be used in routine practice.
Functional brain network dynamics mediate the relationship between female reproductive aging and interpersonal adversity
Premature reproductive aging is linked to heightened stress sensitivity and psychological maladjustment across the life course. However, the brain dynamics underlying this relationship are poorly understood. Here, to address this issue, we analyzed multimodal data from female participants in the Adolescent Brain and Cognitive Development (longitudinal, N = 441; aged 9–12 years) and Human Connectome-Aging (cross-sectional, N = 130; aged 36–60 years) studies. Age-specific intrinsic functional brain network dynamics mediated the link between reproductive aging and perceptions of greater interpersonal adversity. The adolescent profile overlapped areas of greater glutamatergic and dopaminergic receptor density, and the middle-aged profile was concentrated in visual, attentional and default mode networks. The two profiles showed opposite relationships with patterns of functional neural network variability and cortical atrophy observed in psychosis versus major depressive disorder. Our findings underscore the divergent patterns of brain aging linked to reproductive maturation versus senescence, which may explain developmentally specific vulnerabilities to distinct disorders.
Preventing psychosis in people at clinical high risk: an updated meta-analysis by the World Psychiatric Association Preventive Psychiatry section
Recently published large-scale randomised controlled trials (RCTs) have questioned the efficacy of preventive interventions in individuals at clinical high risk for psychosis (CHR-P). We conducted a systematic review and meta-analysis to include this new evidence and provide future directions for the field. We followed the PRISMA guidelines and a pre-registered protocol, with a literature search conducted from inception to November 2023. We included RCTs that collected data on psychosis transition (the primary outcome) in CHR-P. Secondary outcomes were symptoms severity and functioning. Investigated time points were 6,12,24,36, and +36 months. We used odd ratios (ORs) and standardised mean differences (SMD) as summary outcomes. Heterogeneity was estimated with the Higgins I2. Twenty-four RCTs, involving 3236 CHR-P individuals, were included. Active interventions were Cognitive Behavioural Therapy (CBT), family-focused therapy, Integrated Psychological Therapy, antipsychotics, omega-3 fatty acids, CBT plus risperidone, minocycline, and other non-pharmacological approaches (cognitive remediation, sleep-targeted therapy, brain stimulation). Results showed no evidence that any of the investigated active interventions had a sustained and robust effect on any of the investigated outcomes in CHR-P, when compared to control interventions, including CBT on transition to psychosis at 12 months (9 RCTs; OR: 0.64; 95% CI: 0.39–1.06; I2: 21%; P = 0.08). These results highlight the need for novel treatment approaches in CHR-P. Future studies should consider the heterogeneity of this clinical population and prioritise stratification strategies and bespoke treatments.
Probabilistic machine learning for battery health diagnostics and prognostics—review and perspectives
Diagnosing lithium-ion battery health and predicting future degradation is essential for driving design improvements in the laboratory and ensuring safe and reliable operation over a product’s expected lifetime. However, accurate battery health diagnostics and prognostics is challenging due to the unavoidable influence of cell-to-cell manufacturing variability and time-varying operating circumstances experienced in the field. Machine learning approaches informed by simulation, experiment, and field data show enormous promise to predict the evolution of battery health with use; however, until recently, the research community has focused on deterministic modeling methods, largely ignoring the cell-to-cell performance and aging variability inherent to all batteries. To truly make informed decisions regarding battery design in the lab or control strategies for the field, it is critical to characterize the uncertainty in a model’s predictions. After providing an overview of lithium-ion battery degradation, this paper reviews the current state-of-the-art probabilistic machine learning models for health diagnostics and prognostics. Details of the various methods, their advantages, and limitations are discussed in detail with a primary focus on probabilistic machine learning and uncertainty quantification. Last, future trends and opportunities for research and development are discussed.
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