Bipolar Disorder

Artificial intelligence in psychiatry: Current and emerging trends, clinical applications, and research gaps explored through a bibliometric analysis.

OBJECTIVE: The aim of this study is to analyze AI-based scientific publications in psychiatry using bibliometric methods to identify prominent themes, research trends, and future opportunities. METHODS: Original articles published between 1980 and 2025 and indexed in the "Psychiatry" category of the Web of Science were screened. The search strategy included keywords such as "artificial intelligenc...

Plasma metabolomics identifies lipid mediators linking depression and cognitive decline in late-life depression.

BACKGROUND: Late-life depression (LLD) features recurrent episodes and frequently co-exists with cognitive impairment, which predicts worse outcomes and progression to dementia. Evidence indicates a bidirectional depression-cognition relationship, but objective biological tools to capture severity and this interplay are scarce. METHODS: This study compared 110 patients with LLD between depressive ...

Machine learning detecting aggression among mood disorder patients visited in psychiatric emergency departments.

BACKGROUND: Aggression, which is highly prevalent in patients with mood disorders, has been proven valuable in detecting the progression from hypomani...

Identifying diagnostic neuroimaging biomarkers for adolescent major depressive disorder.

BACKGROUND: The increasing incidence of adolescent depression represents a serious public health concern. Despite clear diagnostic criteria, the wide ...

Advanced Machine Learning Interatomic Potential for Accelerated Atomistic Simulations of Lithiation Dynamics in Large-Scale Si@C Core-Shell Anodes.

Designing high-capacity silicon-based anodes for lithium-ion batteries is fundamentally challenged by severe volume expansion during lithiation, which...

Optimizing depression diagnosis: fNIRS and machine learning differentiate unipolar, bipolar, and healthy states.

BACKGROUND: Differentiating Unipolar Depression (UD) from Bipolar Depression (BD) remains challenging due to overlapping symptoms, which can lead to m...

Biotypes of deeply phenotyped depressed patients reflect signatures of adverse childhood experience and depressive cognitive biases.

BACKGROUND: Negative Cognitive Styles (NCS) are key features of depression contributing to severe clinical outcomes by sustaining negative affect. How...

Cation-Ordering-Driven Design of Superionic Lithium Halospinels.

Reducing the material cost of inorganic solid-state electrolytes is crucial to advancing all-solid-state batteries (ASSBs) for next-generation energy ...

Korean longitudinal study on digitally optimized mental healthcare: a cohort profile.

The Korean Longitudinal Study on Digitally Optimized Mental Healthcare is an innovative multicenter trial-ready cohort study. It aims to develop a dig...

Structural alterations related to emotion dysregulation, anxiety and self-harm in adolescents borderline personality disorder. A source-based morphometry study.

OBJECTIVE: Previous research suggests that adolescents with BPD (aBPD) exhibit distinct neuroanatomical alterations, although methodological limitatio...

Causal models and network instability in neurological and psychiatric disorders: Computational and clinical perspectives.

Self-diagnosis-the capacity of a system to detect and correct its own failures-is a defining property of adaptive systems. In the brain, recursive sel...

Combining network pharmacology, machine learning, molecular docking, molecular simulation dynamics and experimental validation to explore the mechanism of Zhenwu decoction in treating major depression through TNF-α pathways.

BACKGROUND: Major depressive disorder (MDD) is a severe psychophysiological condition characterized by cognitive decline, low energy, weight loss, ins...

Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density.

Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investiga...

Neuroinflammation and insulin resistance in major depression and bipolar disorder: Implications for clinical trials evaluating immunometabolic targeted therapies.

Bipolar disorder (BD) and major depressive disorder (MDD) are highly prevalent, disabling psychiatric illnesses marked by substantial heterogeneity an...

A five-year risk prediction model of cardiovascular disease in individuals with bipolar disorder: a nationwide register study from Sweden.

Cardiovascular disease (CVD) risk prediction models for the general population may not provide accurate predictions in individuals with bipolar disord...

Screening for depressive symptoms in primary and secondary school students based on speech features: A one-year longitudinal study from Jiangsu, China.

OBJECTIVE: This study aims to develop and validate an interpretable prediction model for recognizing depression risk in primary and secondary school s...

Functional near-infrared spectroscopy-based computer-aided diagnosis of major depressive disorder using explainable artificial intelligence: Comparison with conventional machine learning.

BACKGROUND AND OBJECTIVE: Only a limited number of explainable artificial intelligence (XAI) models have been developed for the functional near-infrar...

Integrating AI-driven technologies and facial-semantic features for depression detection: A cross-sectional study.

BACKGROUND: Depression is a major global health concern, still individuals with depressive tendencies remain undetected in outpatient settings due to ...

Disease and state dependent neural markers in adolescents with BD. Understanding the neural bases of mania, depression and remission in a data fusion approach.

BACKGROUND: Pediatric bipolar disorder (PBD) is a severe and disabling condition marked by alternating episodes of mania and depression, intermitted w...

Differential factor effects in comorbid depressive and anxiety symptoms (CDAS): A machine learning approach to individualized mental health promotion.

BACKGROUND: Depression and anxiety comorbid presents greater severity than isolated symptoms. Traditional analyses often fail to capture complex inter...