Schizophrenia

Imaging features of affective and non-affective psychosis.

Affective and non-affective psychoses exhibit heterogeneous clinical presentation, neurobiological u...

Evaluating large language models for assessment of psychosis risk.

Psychosis prevention relies on early detection of individuals at clinical high risk for psychosis (C...

Computational Analysis of Semantic and Sentiment Features in Natural Language for Schizophrenia.

BACKGROUND: Schizophrenia (SZ) patients exhibit abnormalities in language expression, including sema...

Decoding neuronal gene expression: integrative insights from omics and AI.

Neuronal functional diversity and pathological vulnerability are governed by multi-layered regulator...

A bibliometric analysis of artificial intelligence applications to schizophrenia research (2005 - 2025).

BACKGROUND: Artificial intelligence (AI) has been increasingly applied in schizophrenia research, ye...

Enhanced multimodal MRI classification of schizophrenia through cross-attention graph neural networks.

We propose TB-GCAN, a tri-branch cross-attention graph neural network for schizophrenia classificati...

Letter to the editor: Attitudes toward artificial intelligence among individuals with anxiety, depression, bipolar disorders, and schizophrenia.

First-episode psychosis and mood disorder onset in individuals with epilepsy.

BACKGROUND: Psychiatric disorders represent a major burden for patients with epilepsy (PwE). This st...

Off-target effects of DREADD ligands revealed by an anesthesia emergence paradigm in mice.

Designer receptors exclusively activated by designer drugs (DREADDs) enable reversible control of sp...

Using smartphone-based assessment of social experience to predict relapse in first-episode psychosis.

Social-affective changes are early indicators of psychosis relapse, yet their dynamic and subjective...

Guideline-anchored retrieval-augmented generation outperforms baseline and literature-only configurations in gynecologic oncology decision support: A pre-integration benchmark.

INTRODUCTION: Large language models (LLMs) are being studied as oncology decision-support tools but ...

Real-time hallucination detection and intervention in medical LLMs via calibrated hidden-state probes.

OBJECTIVE: Most hallucination mitigation for large language models (LLMs) operates post-hoc, leaving...

Integrative multi-omics reveals MHC class II-mediated neuroinflammation and systemic metabolic dysregulation as transdiagnostic drivers in major brain disorders.

Psychiatric, neurodevelopmental, and neurodegenerative disorders, including Alzheimer's disease (AD)...

Generative large language models in the clinical management of Alzheimer's disease and mild cognitive impairment.

BACKGROUND: Dementia affects over 55 million people worldwide. Mild cognitive impairment (MCI) often...

Baseline metabolic signature predicts cognitive improvement and longitudinal glycerophospholipid shifts track recovery in first-episode drug-naïve schizophrenia.

BACKGROUND: Cognitive impairment is a core yet treatment-resistant feature of schizophrenia (SZ), an...

Predicting Psychiatric Readmission of Children and Adolescents Using Machine Learning.

INTRODUCTION: Recurrent psychiatric hospitalizations in youth are costly and burdensome to patients ...

Neurological roots of emotional ambivalence: from brain networks to clinical implications.

Introduction: Emotional ambivalence - the coexistence of conflicting positive and negative feelings ...

HALO-GNN: hallucination-resistant temporal graph neural networks for dynamic community detection.

Temporal graph learning is inherently subject to illusory structural dynamics, which is a phenomenon...

Dissecting first-episode psychosis heterogeneity with clustering analyses: a systematic review.

INTRODUCTION: First episode psychosis (FEP) refers to a variable clinical presentation of the onset ...

FL-SDGIN: A federated graph learning approach for schizophrenia diagnosis integrating static and dynamic brain functional networks.

Recent magnetic resonance imaging (MRI) studies have revealed connectivity abnormalities in brain ne...

Hippocampal subfield radiomics improves identification of in-hospital aggressive behavior in schizophrenia: an interpretable machine-learning study.

OBJECTIVE: To evaluate hippocampal subfield radiomics for identifying aggressive behavior in hospita...