Bipolar Disorder

Predicting Long-Term Depression Progression in Parkinson's Disease: A Machine-Learning Survival Analysis and Risk Score.

BACKGROUND: Depression in Parkinson's disease (dPD) is common and heterogeneous, impairs quality of life, and may accelerate disease progression. Tools that predict long-term dPD progression are lacking. METHODS: We retrospectively analyzed de novo, drug-naïve Parkinson's disease (PD) participants in the Parkinson's Progression Markers Initiative (PPMI; 2011-2024). The primary outcome was depressi...

Perspective: Depression in Persons with Parkinson's Disease.

Depression is a prevalent and disabling syndrome characterized by sustained sadness and/or anhedonia, as well as cognitive and physical symptoms. In Parkinson's disease (PD), depression is both common and clinically challenging due to overlapping symptoms and complex etiologic interactions. Major depressive disorder occurs in approximately 17% of PD patients, while clinically significant depressiv...

Help-Seeking in the Age of AI: Cross-Sectional Survey of the Use and Perceptions of AI-Based Mental Health Support Among US Adults.

BACKGROUND: Anecdotal evidence suggests that an increasing number of people are turning to generative artificial intelligence (GenAI) tools or artific...

Don't predict if you cannot interpret: investigating the clinical viability of facial movements for machine-learning assisted diagnostics of bipolar disorder.

BACKGROUND: Numerous studies have explored the possibility of developing automatic detection pipelines that can seamlessly diagnose patients with bipo...

Convolutional neural network for real‑time localization of ganglionated plexi from bipolar intracardiac electrograms.

BACKGROUND: Precise localization of ganglionated plexi (GP) is critical for effective cardioneuroablation, yet current mapping relies on labour‑intens...

Comparative Mortality Risk of Aripiprazole, Olanzapine, Quetiapine, and Risperidone in Alzheimer's Disease: A Real‑World Retrospective Cohort Study with Treatment Effect Heterogeneity Analysis.

BACKGROUND: Second-generation antipsychotics (SGAs) are frequently used off-label to manage behavioral symptoms in Alzheimer's disease (AD), despite o...

Anticipatory pleasure as a key hedonic component in classifying major depressive disorder: multidimensional behavioral evidence from majority vote algorithm.

Photoelectrocatalytic Chemical Pathways in Photoassisted Lithium-Sulfur Batteries via a Multiscale Graph-Based Machine Learning Framework.

The rapid advancement of photorechargeable batteries is driven by the need for efficient solar energy utilization, with photoassisted lithium-sulfur b...

On the relationships between apathy, depression and anhedonia.

BACKGROUND: Apathy, depression and anhedonia are clinically overlapping constructs, which hinders diagnostic clarity and treatment development. This s...

Bioinformatics and machine learning identify ITIH4 and ZC3H13 as novel mRNA biomarkers for major depressive disorder that promote microglial M1 polarization.

UNLABELLED: Major depressive disorder (MDD), a prevalent mental illness, currently lacks reliable biomarkers and depends predominantly on subjective d...

Prediction of depressive episodes based on clinical features, cognitive characteristics, inflammation-related proteins, and EEG data.

The absence of clinically validated biomarkers and objective diagnostic protocols hinders the accurate and effective diagnosis of depression. Although...

Breaking the rate limiting barrier in lithium||sulfur batteries via spin state engineering.

Elucidating the mechanisms governing sulfur redox reactions is important for the development of high-energy-density Li||S batteries. Despite progress,...

Machine learning models for detecting suicidal ideation in Chinese in-patients with major depressive disorder: A single-centre retrospective study.

UNLABELLED: Suicide claims >720,000 lives annually; major depressive disorder (MDD) carries the highest population-attributable risk. Suicidal ideatio...

Predicting suicidal ideation from depression screening data: A network-augmented machine learning approach.

BACKGROUND: Suicidal ideation is often assessed using a single self-report item in routine screening. We developed a model that combines machine learn...

Altered brain network topology in adolescents with major depressive disorder and bipolar disorder: A resting-state fMRI graph-theoretical and machine learning study.

BACKGROUND: Adolescents with major depressive disorder (MDD) and bipolar disorder (BD) share substantial clinical overlap and elevated suicide risk, y...

Multi-domain ECG feature extraction enable accurate automated recognition of bipolar disorder, depression and schizophrenia.

OBJECTIVE: To develop and validate a multi-lead electrocardiogram (ECG)-based machine learning system for automated classification of major psychiatri...

Deep learning using electroencephalogram (EEG) data for diagnosing and predicting SSRI response in major depressive disorder.

BACKGROUND: Major Depression (MDD) is a potentially life-threatening condition that ranks among the diseases with the highest global burden. Despite i...

Assessing the accuracy of 4 generative AI tools to guide dosing of long-acting injectable antipsychotics.

PURPOSE: The purpose of this study was to review the accuracy of 4 different artificial intelligence (AI) tools in providing dosing recommendations fo...

Differentiating Depression Subtypes through three-electrode EEG and Emotional Stimuli: A Machine Learning Approach.

We propose an EEG-based framework for depression subtype assessment using emotion-modulated neural dynamics elicited by immersive virtual reality (VR)...

Computational delineation and cellular profiling of murine cortical cell layers using multiplex immunofluorescence imaging.

BACKGROUND: The adult mammalian cerebral cortex has a vertical laminar organization consisting of six neuronal layers, with each layer subserving a sp...