Bipolar Disorder

A Zero-Burden Sleep Foundation Model Built on Cardiorespiratory Signals from 800,000+ Hours of Multi-Ethnic Sleep Recordings

Sleep disorders pose a major global health burden and are associated with a wide range of adverse health outcomes. Polysomnography (PSG) is the gold standard for sleep assessment, but it is impractical for home-based or long-term monitoring. We investigated whether zero-burden cardiorespiratory signals, when harnessed through foundation model approaches, can enable accurate sleep assessment at sca...

Bridging Brain Signals and Self-Reported Symptoms: An AI-Driven, High-Sensitivity Model for Detecting Suicidality in Major Depressive Disorder

Major depressive disorder (MDD) with suicidality represents a significant public health concern, as suicide ranks among the leading causes of death worldwide. While electroencephalography (EEG) has shown promise in depression diagnosis, its utility in identifying suicidal risk remains underexplored. This study aims to develop and validate a Suicidal Risk Index (SR Index) using EEG biomarkers and m...

Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls

Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain ...

Development and Validation of Machine Learning-Based Prediction of Depression Progression Using EHR Data: A Multi-Institutional Retrospective Cohort Study

Depression is a leading cause of global disability. Timely identification of patients at risk for clinical worsening remains a major challenge. Electr...

Mapping heterogeneity in the neuroanatomical correlates of depression

Major depressive disorder (MDD) affects millions worldwide, yet its neurobiological underpinnings remain elusive. Neuroimaging studies have yielded in...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...

A Chatbot for the Management of Bipolar Disorder: Using Retrieval-Augmented Generation with an Open-Weight Large Language Model to Answer Clinical Questions Based on the CANMAT and ISBD 2018 Guidelines

Clinical practice guidelines support evidence-based care but are often underused due to complexity, time constraints, and navigation challenges. We in...

Peripheral Metabolic–Redox Signaling as a Core Mechanism of Major Depressive Disorder: Evidence From Deep Metabolomic Phenotyping

Major depressive disorder (MDD) is a neuro-immune, oxidative, and nitrosative stress (NIMETOX) disorder, in which peripheral immune-redox pathways int...

The Hypothalamic Medial Preoptic Area-Paraventricular Nucleus Circuit Modulates Depressive-Like Behaviors in a Mouse Model of Postpartum Depression.

Estrogen fluctuations have been implicated in various mood disorders, including perimenopausal and postpartum depression (PPD), likely through complex...

A Clinical Risk Prediction Model for Depressive Disorders Based on Seven Machine Learning Algorithms.

OBJECTIVE: To develop a clinical risk prediction model for depressive disorders using seven machine learning algorithms based on routine blood test in...

Predicting Suicidal Ideation Among Youths With Autism Spectrum Disorder: An Advanced Machine Learning Study.

This study aimed to predict suicidal ideation among youth with autism spectrum disorder (ASD) by applying machine learning techniques. A cross-section...

From the -Factor to Cognitive Content: Detection and Discrimination of Psychopathologies Based on Explainable Artificial Intelligence.

Differentiating psychopathologies is challenging due to shared underlying mechanisms, such as the -factor. Nevertheless, recent methodological advanc...

A lithium-ion batteries SOH estimation method based on extracting new features during the constant voltage charging stage and improving BPNN.

Existing state of health (SOH) estimation methods for lithium-ion batteries predominantly extract health features (HF) from constant current (CC) and ...

Developing a machine learning algorithm to predict psychotropic drugs-induced weight gain and the effectiveness of anti-obesity drugs in patients with severe mental illness: Protocol for a prospective cohort study.

Obesity is a global public health concern, often co-occurring in patients with severe mental illnesses. The impact of psychotropic drugs-induced weigh...

Predicting depression severity using machine learning models: Insights from mitochondrial peptides and clinical factors.

Depression presents a significant challenge to global mental health, often intertwined with factors including oxidative stress. Although the precise r...

Predicting Antidepressant Treatment Response From Cortical Structure on MRI: A Mega-Analysis From the ENIGMA-MDD Working Group.

Accurately predicting individual antidepressant treatment response could expedite the lengthy trial-and-error process of finding an effective treatmen...

Comparison of Different Machine Learning Methodologies for Predicting the Non-Specific Treatment Response in Placebo Controlled Major Depressive Disorder Clinical Trials.

Placebo effect represents a serious confounder for the assessment of treatment effect to the extent that it has become increasingly difficult to devel...

Semantic abnormalities in schizophrenia and bipolar disorder: A natural language processing approach.

INTRODUCTION: The diagnostic boundaries between schizophrenia and bipolar disorder are controversial due to the ambiguity of psychiatric nosology. Fro...

Accuracy and robust early detection of short-circuit faults in single-cell lithium battery

Effective early-stage detection of internal short circuit in lithium-ion batteries is crucial to preventing thermal runaway. This report proposes an...

Refining CNN-based Heatmap Regression with Gradient-based Corner Points for Electrode Localization

We propose a method for detecting the electrode positions in lithium-ion batteries. The process begins by identifying the region of interest (ROI) i...