Depression

An exploratory randomized controlled trial of an AI-enabled mental health intervention for generalized anxiety.

Generalized Anxiety Disorder (GAD) is prevalent and often co-occurs with depression, contributing to significant disability and healthcare burden. Although treatments such as CBT and SSRIs are effective, access remains limited. This exploratory, randomized controlled trial evaluated the effectiveness of an AI-powered mental health app (PATH) in reducing symptoms of anxiety and depression. A total ...

Predicting Depression Risk Among Chinese College Students From Childhood Adversity and Recent Stress Exposure Using Interpretable Machine Learning.

Previous research has shown that both early-life stressors (e.g., adverse childhood experiences) and recent stress exposure (e.g., recent life events) may contribute to the onset of depressive symptoms. However, their combined predictive effect on depression remains unclear. Using data from 2440 Chinese college students, the present study employed nine machine learning algorithms to evaluate the j...

Predicting suicide attempts in a high-risk clinical cohort of adolescents using machine-learning.

TMN: Learning multi-timescale functional connectivity for identifying brain disorders.

BACKGROUND: Functional connectivity (FC) has been used to identify brain disorders. The present study aimed to identify brain disorders by FC across m...

From non-specific biomarker to targeted action: transdiagnostic and sex-specific drivers of high-CRP status in severe mental illness across the FondaMental Advanced Centers of Expertise (FACE) cohorts.

BACKGROUND AND OBJECTIVES: Low-grade systemic inflammation contributes to the pathophysiology of severe mental illness (SMI) in a substantial subset o...

Gut microbiome signatures associated with depression and obesity.

UNLABELLED: Depression and obesity are highly comorbid and likely involve common risk factors and pathophysiological mechanisms, which could crosslink...

Voice-based machine learning for rapid screening of bipolar disorder and major depressive disorder in children and adolescents: a robust and low-complexity diagnostic model.

BACKGROUND: Major depressive disorder (MDD) and bipolar disorder (BD) are psychiatric disorders that seriously impact physical and mental health. They...

Deep learning enhanced ALPS reveals genetic and environmental factors of brain glymphatic function.

BACKGROUND: The glymphatic system plays a critical role in brain waste clearance and health. Diffusion tensor imaging along the perivascular space (DT...

A multilayered integrated analysis of insomnia-related genes ATG7 and JAK2 in the autophagy-inflammation mechanism and clinical implications in major depressive disorder.

OBJECTIVE: Insomnia is widely recognized as a key risk factor for major depressive disorder (MDD). However, the potential molecular mechanisms and the...

Generating actionable insights to support point-of-care suicide risk decision-making in a safety-net healthcare system: a machine learning approach to predicting dynamic risk of intentional self-harm.

BACKGROUND: Suicide rates have increased over the last couple of decades globally, particularly in the United States and among populations with lower ...

Neuroimmune Cross-Talk and Multilevel Cascades in Fentanyl Toxicity: Interplay of Hypoxic Stress, Glial Activation, and Synaptic Dysregulation in Systems-Level Neurodegeneration.

Fentanyl, an ultra-potent synthetic opioid, has traditionally been characterized by its acute toxic effects, particularly respiratory depression. Howe...

Spatiotemporal patterns and environmental correlates of suicide seasonality in Brazil.

Suicide exhibits a consistent seasonal pattern worldwide, peaking in late spring and early summer in both hemispheres. Yet, the biological mechanisms ...

Machine learning and SHAP-based interpretation of dietary antioxidants' impact on comorbid MDD and suicidal ideation.

OBJECTIVE: To develop and validate a machine learning model for predicting major depressive disorder (MDD) with suicidal ideation (SI) by incorporatin...

Lymphocyte-based inflammatory biomarkers during the postpartum period and postpartum depression symptoms in U.S. women.

BACKGROUND: Postpartum depression (PPD) has multiple cascading negative effects on maternal and infant health. Inflammation is a potential factor for ...

Passive data do not improve prediction or detection of alcohol consumption beyond temporal patterns in major depression: A 90-day cross-validated study.

Major Depressive Disorder (MDD) is one of the most prevalent psychological disorders and frequently co-occurs with alcohol use disorders, increasing t...

Tegmental kappa-opioid receptor neurons modulate opioid withdrawal via the periaqueductal gray.

Opioid withdrawal is a common and distressing manifestation of opioid dependence which, if left untreated, frequently results in relapse, accidental o...

Prediction of 12-Week Remission in Patients With Depressive Disorder Using Reasoning-Based Large Language Models: Model Development and Validation Study.

BACKGROUND: Depressive disorder affects over 300 million people globally, with only 30% to 40% of patients achieving remission with initial antidepres...

Triaging Casual From Critical-Leveraging Machine Learning to Detect Self-Harm and Suicide Risks for Youth on Social Media: Algorithm Development and Validation Study.

BACKGROUND: This study aims to detect self-harm or suicide (SH-S) ideation language used by youth (aged 13-21 y) in their private Instagram (Meta) con...

Age matters: a narrative review and machine learning analysis on shared and separate multidimensional risk domains for early and late onset suicidal behavior.

There is considerable heterogeneity among late-life suicide attempters who can present stark differences in their suicidal trajectories. This work pro...

A Multimodal Neuro-Demographic Signature for Immuno-Metabolic Depression.

BACKGROUND: The underlying neurobiology of a recently described immuno-metabolic depression (IMD) subtype of major depressive disorder (MDD), characte...