Schizophrenia

Chatbot psychosis: moving beyond recognition to mechanistic understanding and harm reduction.

The chatbot psychosis phenomenon is no longer just a hypothesis. We call for interdisciplinary frameworks to systematically investigate individual characteristics and artificial intelligence related factors which (on their own or in combination) cause or contribute to this phenomenon, underlying mechanisms and the psychoeducation, ethics, policy and practices needed to reduce harm.

Behavioral dataset for Long-Evans and its schizophrenia-like substrain through several generations.

We present a high-throughput behavioral dataset acquired with Ambitus, an automated reward-based corridor system that records locomotor and exploratory activities and cognitive functions after minimal handling. The collection contains 91 raw and derived variables, each measured across four consecutive trials, for 1,342 Long-Evans rats, including a triple-hit schizophrenia-like substrain (Lisket) b...

Foundations for AI-assisted Adverse Outcome Pathways (AOPs) in radiation research.

PURPOSE: Artificial Intelligence (AI) and Machine Learning (ML) are being explored to improve systematic evidence gathering and to identify patterns a...

Analysis and prediction of schizophrenia patients based on high-order graph attention generative adversarial networks.

Generative Adversarial Networks, a popular deep learning method, have achieved excellent performance in both classification and prediction tasks. Howe...

On Hallucinations in Artificial Intelligence-Generated Content for Nuclear Medicine Imaging (the DREAM Report).

Artificial intelligence-generated content (AIGC) has shown remarkable performance in nuclear medicine imaging (NMI), offering cost-effective software ...

Exploring the intricate interplay between metabolic abnormalities and multidimensional cognitive impairment in stable schizophrenia patients.

Cognitive deficits across multiple domains are prevalent in patients with schizophrenia (PWS), and metabolic syndrome (MetS) may significantly contrib...

From non-specific biomarker to targeted action: transdiagnostic and sex-specific drivers of high-CRP status in severe mental illness across the FondaMental Advanced Centers of Expertise (FACE) cohorts.

BACKGROUND AND OBJECTIVES: Low-grade systemic inflammation contributes to the pathophysiology of severe mental illness (SMI) in a substantial subset o...

Constructing fine-grained subcortical atlases with connectional consensus graph representation learning.

Objective.The complex internal organization of subcortical structures forms the foundation of critical neural circuits that support sensorimotor proce...

Ontology-grounded knowledge graphs for mitigating hallucinations in large language models for clinical question answering.

OBJECTIVE: Large Language Models (LLMs) show strong potential in biomedical informatics but frequently generate hallucinated or factually incorrect re...

Brain aging in bipolar disorder using a neuroimaging and machine learning-derived metric: Findings from the ENIGMA BD Working Group.

BACKGROUND: Bipolar disorder (BD) is associated with clinical and biological markers of premature aging. In this largest study of brain age in BD to d...

Two Decades of Metaphorical Use of Schizophrenia in Brazilian Newspapers: A Machine Learning Approach.

Media representation of mental illness has the potential to reinforce stigma. This phenomenon may arise from the perpetuation of stereotypes, the asso...

Artificial intelligence for schizophrenia: from unimodal prediction to multimodal characterization.

PURPOSE OF REVIEW: Artificial intelligence is increasingly advancing both fundamental research and clinical applications in schizophrenia. This review...

Dual-representation structural MRI classification of psychiatric disorders using deep learning and large language models.

Accurate differentiation among psychiatric disorders such as schizophrenia and bipolar disorder remains a significant clinical challenge due to overla...

Exploring Primary and Interaction Effects of Minor Physical Anomalies: Development and Validation of Prediction Models Using Explainable Machine Learning Algorithms for Early-Onset Schizophrenia.

BACKGROUND AND HYPOTHESIS: Minor physical abnormalities (MPAs) are neurodevelopmental markers that can be traced to prenatal events and may be signifi...

The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia.

BACKGROUND AND HYPOTHESIS: Given the available findings confirming accelerated brain aging in schizophrenia (SZ), we conducted a study aimed at verify...

Mental Health Professionals' Perspectives on Digital Remote Monitoring in Services for People with Psychosis.

BACKGROUND AND HYPOTHESIS: Digital remote monitoring (DRM) captures service users' health-related data remotely using devices such as smartphones and ...

Development and external validation of machine learning approaches for risk prediction of cardiovascular disease in individuals with schizophrenia: a nationwide Swedish and Danish study.

BACKGROUND: Currently available cardiovascular disease (CVD) risk prediction tools may underestimate the risk in individuals with schizophrenia. OBJEC...

Sex and Stress Govern the Function and Innervation of a Basolateral Amygdala to Nucleus Accumbens Corticotropin-Releasing Hormone/GABA-Expressing Projection.

Motivated behaviors are executed by refined brain circuits. Early-life adversity (ELA) is a risk for human affective disorders involving dysregulated ...

Effective prompt design for large language models in clinical practice.

BACKGROUND: Large language models (LLMs) have emerged as transformative healthcare tools for clinical documentation, diagnostic reasoning, and medical...

EEG-based schizophrenia classification using attention-integrated deep convolutional networks.

Schizophrenia is a complex psychiatric disorder marked by cognitive and perceptual disruptions, for which electroencephalography (EEG) provides a valu...