Biological Psychiatry最新文献

The devastating effects of the COVID-19 pandemic have underscored the significant threat infectious diseases pose to our society. Pregnancy represents a particularly vulnerable period for infections, which can compromise maternal health and increase the risk of neurodevelopmental disorders in offspring. Preclinical and clinical investigations suggest a potential association between maternal immune activation (MIA), triggered by viral or bacterial infections, and the increased risk for neurodevelopmental disorders such as autism and schizophrenia. Genetic and environmental factors might contribute to the overall risk. Hence, the two-hit hypothesis of schizophrenia suggests that MIA could act as a first trigger, with subsequent factors, such as stress or drug abuse, exacerbating latent abnormalities. A growing body of research focuses on the interaction between MIA and cannabis use during adolescence, considering the role of the endocannabinoid system in neurodevelopment and in neurodevelopmental disorders. The endocannabinoid system, crucial for fetal brain development, may be disrupted by MIA, leading to adverse outcomes in adulthood. Recent research indicates the endocannabinoid system's significant role in the pathophysiology of neurodevelopmental disorders in preclinical models. However, findings on adolescent cannabinoid exposure in MIA-exposed animals reveal unexpected complexities, with several studies failing to support the exacerbation of MIA-related abnormalities. This review delves into the functional implications of the endocannabinoid system in MIA models, emphasizing 2-arachidonoylglycerol (2-AG) signaling's role in synaptic plasticity and neuroinflammation, and its relevance to the two-hit model of schizophrenia.

Background: Opioid addiction is a worldwide public health crisis. In the United States, for example, opioids cause more drug overdose deaths than any other substance. Yet, opioid addiction treatments have limited efficacy, meaning that additional treatments are needed.

Methods: To help address this problem, we used network-based machine learning techniques to integrate results from genome-wide association studies (GWAS) of opioid use disorder (OUD) and problematic prescription opioid misuse with transcriptomic, proteomic, and epigenetic data from the dorsolateral prefrontal cortex (dlPFC) of opioid overdose victims and controls.

Results: We identified 211 highly interrelated genes identified by GWAS or dysregulation in the dlPFC of opioid overdose victims that implicated the Akt, BDNF, and ERK pathways, identifying 414 drugs targeting 48 of these opioid addiction-associated genes. Some of the identified drugs are approved to treat other substance use disorders (SUDs) or depression.

Conclusions: Our synthesis of multi-omics using a systems biology approach revealed key gene targets that could contribute to drug repurposing, genetics-informed addiction treatment, and future discovery.

Background: The computational mechanisms underlying psychiatric disorders are hotly debated. One hypothesis, grounded in the Bayesian predictive coding framework, proposes that schizophrenia patients have abnormalities in encoding prior beliefs about the environment, resulting in abnormal sensory inference, which can explain core aspects of the psychopathology, such as some of its symptoms.

Methods: Here, we tested this hypothesis by identifying oscillatory traveling waves as neural signatures of predictive coding. By analyzing an EEG dataset comprising 146 schizophrenia patients and 96 age-matched healthy controls, during resting states and a visual backward masking task.

Results: We found that schizophrenia patients have stronger top-down alpha-band traveling waves compared to healthy controls during resting state, supposedly reflecting overly precise priors at higher levels of the predictive processing hierarchy. We also found stronger bottom-up alpha-band waves in schizophrenia patients during a visual task, in line with the notion of enhanced signaling of sensory precision errors.

Conclusions: Our results yield a novel spatial-based characterization of oscillatory dynamics in schizophrenia, considering brain rhythms as traveling waves and providing a unique framework to study the different components involved in a predictive coding scheme. Altogether, our findings significantly advance our understanding of the mechanisms involved in fundamental pathophysiological aspects of schizophrenia, promoting a more comprehensive and hypothesis-driven approach to psychiatric disorders.