Bipolar Disorder

Linear and Nonlinear Oscillatory Functional Brain Networks in Euthymic Bipolar Disorder Classification

Functional brain network (FBN) dysconnectivity has been repeatedly reported in bipolar disorder (BD). However, it remains unclear how this dysconnectivity manifests from the perspective of oscillatory FBNs, that is, which network measures and frequency bands most reliably capture this alteration. Moreover, it is unknown whether this dysconnection is predominantly expressed through linear or nonlin...

Circuit-Specific Resting-State fMRI Signatures for Stratifying First-Episode Major Depressive Disorder and Predicting Recurrence Risk

Background: Depression is biologically heterogeneous, and first-episode depression (FED) carries a high risk of recurrence that is poorly captured by symptom-based assessment. Early identification of patients likely to relapse, as well as reliable identification of those unlikely to relapse, is needed to support personalized intervention and efficient allocation of care. Methods: We developed a ne...

A Non-Invasive 3D Gait Analysis Framework for Quantifying Psychomotor Retardation in Major Depressive Disorder

Predicting the status of Major Depressive Disorder (MDD) from objective, non-invasive methods is an active research field. Yet, extracting automatical...

Regulatory Hub Discovery in MDD Methylome: Hypotheses for Molecular Subtypes via Computational Analysis

Major Depressive Disorder (MDD) is a clinically heterogeneous syndrome with diverse etiological pathways. Traditional Epigenome-Wide Association Studi...

NIMETOX-informed Precision Nomothetic Models of Major Depressive Disorder: Group, Phenome, and Individual Signatures

Background: Major depressive disorder (MDD) is a neuro-immune-metabolic-oxidative (NIMETOX) disorder. Nevertheless, the effects of alterations in immu...

Depression Detection Based on Electroencephalography Using a Hybrid Deep Neural Network CNN-GRU and MRMR Feature Selection

This study investigates the detection and classification of depressive and non-depressive states using deep learning approaches. Depression is a preva...

Machine learning-based predictive modeling of depressive symptoms in Chinese adolescents.

BACKGROUND: The aim is to develop prediction models by lifestyles indicators as well as socioeconomic status to predict the risk of depressive symptom...

Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data.

Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This st...

Prediction of remission of pharmacologically treated psychotic depression: A machine learning approach.

BACKGROUND: The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic feat...

Machine learning-driven risk prediction and feature identification for major depressive disorder and its progression: an exploratory study based on five years of longitudinal data from the US national health survey.

BACKGROUND: Major depressive disorder (MDD) presents significant public health challenges due to its increasing prevalence and complex risk factors. T...

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy

We investigate Machine-Learned Force Fields (MLFFs) trained on approximate Density Functional Theory (DFT) and Coupled Cluster (CC) level potential ...

STEM Diffraction Pattern Analysis with Deep Learning Networks

Accurate grain orientation mapping is essential for understanding and optimizing the performance of polycrystalline materials, particularly in energ...

Machine learning approaches for classifying major depressive disorder using biological and neuropsychological markers: A meta-analysis.

Traditional diagnostic methods for major depressive disorder (MDD), which rely on subjective assessments, may compromise diagnostic accuracy. In contr...

The Application of Large Language Models on Major Depressive Disorder Support Based on African Natural Products

Major depressive disorder represents one of the most significant global health challenges of the 21st century, affecting millions of people worldwid...

Specific heat anomalies and local symmetry breaking in (anti-)fluorite materials: A machine learning molecular dynamics study.

Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In th...

PVDF-based solid polymer electrolytes for lithium-ion batteries: strategies in composites, blends, dielectric engineering, and machine learning approaches.

Solid polymer electrolytes (SPEs) present a viable alternative to organic carbonates typically used as liquid electrolytes in lithium-ion batteries (L...

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

BACKGROUND: Early diagnosis and treatment of mental illnesses is hampered by the lack of reliable markers. This study used machine learning models to ...

RBA-FE: A Robust Brain-Inspired Audio Feature Extractor for Depression Diagnosis

This article proposes a robust brain-inspired audio feature extractor (RBA-FE) model for depression diagnosis, using an improved hierarchical networ...

Interpretable Depression Detection from Social Media Text Using LLM-Derived Embeddings

Accurate and interpretable detection of depressive language in social media is useful for early interventions of mental health conditions, and has i...

Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments

Major depressive disorder (MDD), a leading cause of disability and mortality, is associated with reward-processing abnormalities and concentration i...