Bipolar Disorder

Magnetic resonance imaging study of major depressive disorder: a 28-year scientometric and visual analysis.

Neuroimaging, particularly magnetic resonance imaging (MRI), has become a cornerstone in elucidating the neural underpinnings of Major Depressive Disorder (MDD). As the volume of related literature grows rapidly, a systematic, quantitative overview of the field's development, intellectual structure, and emerging trends is essential. This study conducted a scientometric and visual analysis of MRI r...

Association between the oxidative balance score and depressive symptoms in adults with metabolic syndrome: a cross-sectional study from NHANES.

OBJECTIVE: This study investigated the association between the Oxidative Balance Score (OBS) and the odds of depressive symptoms in adults with metabolic syndrome (MetS) using National Health and Nutrition Examination Survey data (2007-2018), and employed machine learning to enhance predictive insights. METHODS: We analyzed 6,244 U.S. adults with MetS. Multivariable logistic regression assessed th...

Integrative analysis via bioinformatics and machine learning identifies SERPING1 as a biomarker candidate for major depressive disorder.

Beyond acoustic features: Incorporating linguistic variables in automatic speech analysis for depression detection.

BACKGROUND: Most research on automatic speech analysis (ASA) has focused on acoustic features, while the potential of linguistic markers remains under...

The association between psychotropic medications and cognitive functioning in bipolar disorder.

BACKGROUND: Many individuals with bipolar disorder have decreased levels of cognitive functioning even when in a euthymic mood state. Persons with bip...

From pharmacokinetic genes to polygenic scores and multi-omic integration: advances towards precision psychiatry.

INTRODUCTION: . Pharmacological treatment is the mainstay in the acute and long-term management of severe mental disorders such as major depressive di...

Transcriptomic analysis and machine learning have identified shared diagnostic genes and a possible mechanism linking bipolar disorder and epilepsy.

It is well known that bipolar disorder (BD) and epilepsy (EP) are common neurological diseases. The objective of this study was to screen for potentia...

Unveiling state-specific neural dynamics in migraine with and without depressive symptom: a hidden Markov model and interpretable machine learning approach.

Neuroimaging insights into the neurophysiological subtypes of major depressive disorder.

Major depressive disorder (MDD) is a highly heterogeneous condition that limits the reliability of symptom-based diagnosis and treatment selection. In...

Classification of suicide attempt history in adolescents with affective disorders: An exploratory machine learning study.

INTRODUCTION: Diagnosis of affective disorders among adolescent population links with the high risk of suicide attempt. The use of clinical psychologi...

Evaluating the potential of applying artificial intelligence in the diagnosis of major depressive disorder: Neuroimaging and clinical behavioral markers.

BACKGROUND: Consensus exists that point-of-care in scalable capabilities are required to improve the timeliness and accuracy of Major Depressive Disor...

Using machine learning to reveal two distinct neuroanatomical subtypes of first-episode, drug-naïve major depressive disorder: Evidence from the REST-meta-MDD project.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous condition, complicating biomarker discovery and precision medicine. Identifying ...

A universal machine learning model for the electronic density of states.

In the last few years several "universal" interatomic potentials have appeared, using machine-learning approaches to predict energy and forces of atom...

A Pulse-Test-Based Method for Predicting Lithium Battery DRT Curves and State Estimation.

Distribution of relaxation times (DRT) curves for lithium batteries facilitates the analysis of internal dynamics and polarization characteristics, of...

A Hybrid Physics-Driven Neural Network Force Field for Liquid Electrolytes.

Electrolyte design plays an important role in the development of lithium-ion batteries and sodium-ion batteries. Battery electrolytes feature a large ...

Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.

Major depressive disorder (MDD) remains clinically diagnosed based on subjective symptoms rather than objective neurobiological markers, which limits ...

Development of an interpretable machine learning model-based online tool for risk identification of anxiety symptoms in Chinese older adults.

BACKGROUND: As the population aging process has accelerated, anxiety symptoms (AS) among older adults have become a critical concern. In addition to d...

Shift to the core: Abnormal core-periphery global topography in unipolar and bipolar depression.

This study explores the global signal topography of core and periphery brain networks in Major Depressive Disorder (MDD), Bipolar disorder (BD-Dep) an...

Interpretable Feature Selection and Hybrid Deep Learning Models for Depressive Symptoms Prediction from Wearable Device Data.

A hybrid SMOTE and Gaussian mixture model based optimized XGBoost framework for bipolar disorder detection.

The identification of bipolar disorder (BD), a severe psychiatric condition characterized by recurrent mood fluctuations, remains challenging due to s...