Depression

Plasma neurotransmitter-related metabolite alterations in adolescent major depressive disorder with and without psychotic features.

BACKGROUND: Whether adolescent major depressive disorder (MDD) with psychotic features has a distinct peripheral metabolite signature remains uncertain. We compared targeted plasma neurotransmitter-related metabolites in psychotic MDD, non-psychotic MDD, and healthy controls. METHODS: We enrolled 220 adolescents (53 psychotic MDD, 67 non-psychotic MDD, 100 controls) and quantified 14 metabolites s...

Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology.

The accuracy of grey-matter predictors of depression has remained limited. In this study, brain-based predictors of major depressive disorder (MDD) were trained using machine-learning (Best Linear Unbiased Predictors [BLUP]) and deep-learning (ResNet3D) techniques applied to high-dimensional (voxel-wise) grey-matter structure extracted from T1-weighted structural MRI. The training sample comprised...

Predictive machine learning algorithms for depression and anxiety disorders in six cancer types: a comprehensive multi-center population-based study.

OBJECTIVE: Machine learning (ML) has advanced predictive modeling in medical diagnosis and risk assessment through large clinical datasets, yet applic...

Peripheral Metabolic-Redox Signaling as a Core Mechanism of Major Depressive Disorder: Evidence from Deep Metabolomic Phenotyping.

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

Language-based detection of depression with machine learning: systematic review and meta-analysis.

Early detection of depression is critical for timely intervention. Natural language processing (NLP) and machine learning (ML) approaches have increas...

A systematic review on dysconnectivity in face and emotion processing networks in schizophrenia.

Schizophrenia is a complex psychiatric disorder that affects approximately 20 million people worldwide. Patients show face-processing deficits that si...

Role of lactic acid-mediated ALKBH5 in depression induced by blue light exposure at night.

Artificial light at night (ALAN) has been classified as a significant environmental endocrine disruptor. Excessive exposure to ALAN has associated wit...

ChatGPT Health performance in a structured test of triage recommendations.

ChatGPT Health launched in January 2026 as OpenAI's consumer health tool, reaching millions of users. Here, we conducted a structured stress test of t...

Could sleep engineering be used to combat PTSD and depression?

Sleep engineering could be developed to provide a drug-free, non-invasive avenue to treat depression and post-traumatic stress disorder. Such an inter...

Co-Designed Mental Health Screening App (Here for You) for University Students: Pilot Feasibility Mixed Methods Study.

BACKGROUND: Mental health disorders are a growing public health concern among university students globally and in India, exacerbated by stigma and lim...

On the importance of white-box machine learning models in psychiatry: An example predicting suicidality in depression.

Identifying Transdiagnostic Predictors of Depression Across Psychoses: Informing Stratified Antidepressant Treatments.

INTRODUCTION: Depression frequently co-occurs with psychosis and is associated with poor outcomes. Early identification of patients at risk of persist...

Predicting Adolescent Response to School-Based Mindfulness: A Secondary Analysis of the MYRIAD Trial.

IMPORTANCE: Depression most commonly first emerges during adolescence, making early prevention critical. While school-based mindfulness training (SBMT...

Identifying past-year self-reported suicidality in outpatients with somatic symptom disorder using an interpretable machine-learning model: a multicenter study with an online calculator.

BACKGROUND: Somatic symptom disorder (SSD) is associated with an elevated risk of suicidality. However, clinically implementable tools to identify out...

Prediction models for suicide reattempts by lasso regression through machine learning models: Single versus multiple suicide attempters.

The best predictor of a suicide attempt is a previous attempt, apart from psychiatric diagnoses also associated. Some studies found other indicators o...

Differentiation between psychotic and non-psychotic major depression by the tabular prior-data fitted network.

BACKGROUND: Misdiagnosing psychotic major depression (PMD) as non-psychotic major depression (NPMD) can lead to poor treatment outcomes. This study ai...

Peripheral blood transcriptomic biomarkers for predicting antidepressant response in major depressive disorder.

Bioelectronic interface for real-time brain biomarker analysis.

The brain is a highly complex organ, exhibiting a highly dynamic chemical environment, playing crucial role in coordinating pathophysiological process...

MDD-thinker: A reasoning-enhanced large language model for diagnosis of major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of global disability and poses a substantial public health burden. However, current dia...

Discriminating between major depressive disorder and bipolar depression: Aberrant EEG microstate dynamics and machine learning classification.

BACKGROUND: Major depressive disorder (MDD) and bipolar depression (BD) are common mood disorders with overlapping clinical features, posing significa...