Depression

Life-course comorbidity patterns and integrated prediction of postpartum depression, multimorbidity, and symptom progression

Perinatal depression (PD) is common and disabling, yet its longitudinal comorbidity patterns and predictability remain poorly understood. This study leveraged 8,804 women with delivery records in the All of Us cohort, including 438 with clinically diagnosed postpartum depression (PPD), to characterize multimorbidity trajectories and develop integrated prediction models. Comorbidities were grouped ...

Reproducible symptom subtypes of depression identified using unsupervised machine learning

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, individuals may present with markedly different symptom profiles, potentially reflecting distinct underlying mechanisms. Identifying common patterns of symptoms using data-driven approaches could help clarify the heterogeneity of depression. Furthermore, examining the sociodemographic and lifestyle cha...

Early Pregnancy DNA Methylation Signatures as Predictors of Antenatal Depressive Symptoms: A longitudinal study of DNA methylation changes

Background. Antenatal depressive symptoms (ADS) are common and underdiagnosed, particularly in low and middle income countries, and are associated wit...

Deep Lipidomic Phenotyping Identifies Ceramide-Centered Lipotoxicity and Depletion of Plasmalogen-Carnitine Pathways in Major Depressive Disorder: Implications for Precision Medicine

Background: Major depressive disorder (MDD) severely impairs individual health and creates heavy societal burdens. Diagnostic and therapeutic research...

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 ...

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...

Student Mental Health Screening via Fitbit Data Collected During the COVID-19 Pandemic

College students experience many stressors, resulting in high levels of anxiety and depression. Wearable technology provides unobtrusive sensor data t...

Adversarial Drift-Aware Predictive Transfer: Toward Durable Clinical AI

Clinical AI systems frequently suffer performance decay post-deployment due to temporal data shifts, such as evolving populations, diagnostic coding u...

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...

Suicide- and crisis-risk detection using large language models in mental-health chatbots

ObjectiveLarge language models (LLMs) are increasingly embedded in mental-health chatbots, yet safe deployment is limited by two unresolved challenges...

Decoding chronic stress: From behavioral-molecular dynamics in mice to clinical implications of cortisol and IL-17 in depression severity.

BACKGROUNDS: The etiology of depression involves chronic stress, a recognized determinant of onset and severity. This study adopts a translational app...

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...

Multimodal depression recognition and analysis: Facial expression and body posture changes via emotional stimuli.

BACKGROUND: Clinical studies have shown that facial expressions and body posture in depressed patients differ significantly from those of healthy indi...

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...

Development and Evaluation of HopeBot: an LLM-based chatbot for structured and interactive PHQ-9 depression screening

Static tools like the Patient Health Questionnaire-9 (PHQ-9) effectively screen depression but lack interactivity and adaptability. We developed Hop...

Prediction of Adolescent Suicide Attempt by Integrating Clinical, Neurocognitive and Geocoded Neighborhood Environment Data.

BACKGROUND AND HYPOTHESIS: Suicide attempt is a complex behavior influenced by a combination of factors including clinical, neurocognitive, and enviro...

Molecular Machine Learning Using Euler Characteristic Transforms

The shape of a molecule determines its physicochemical and biological properties. However, it is often underrepresented in standard molecular repres...