Depression

Symptom trajectories and their predictive factors in psychiatric inpatients with depression.

BACKGROUND: Depression presents with heterogeneity in symptom trajectories, complicating individualized treatment. Identifying distinct classes of symptom trajectories and their predictors may enable earlier intervention for those at greatest risk of poor outcomes. METHODS: We analyzed 620 inpatients with depression drawn from two independent naturalistic samples. Using Growth Mixture Modeling, la...

Development of explainable machine learning-based prediction models of depression with mild and severe insomnia in middle-aged and elderly Chinese people.

BACKGROUND: The risk of depression is significantly elevated in middle-aged and older adults with insomnia; however, the pathways between mild and severe insomnia are different. Most existing prediction tools treat insomnia as a single construct, ignoring its heterogeneity. It is crucial to develop an interpretable, severity-specific modeling framework to quantify depression risk, identify shared ...

Resting-state functional connectivity of the fronto-limbic and default mode network as neural correlates of antidepressant response in major depressive disorder.

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet antidepressant response remains highly variable, with many...

Unconscious elevated bottom-up processing in depression: Insights from dynamic causal modeling with EEG and fMRI.

INTRODUCTION: MRI compatible EEG systems enable simultaneous EEG-fMRI data assessment, which provides high spatial and high temporal resolution of neu...

Medication-based mortality prediction in COPD using machine learning and conventional statistical methods.

BACKGROUND: Predicting mortality in chronic obstructive pulmonary disease (COPD) patients supports clinical decision-making and resource allocation. W...

Acoustic and machine learning methods for speech-based suicide risk assessment: A systematic review.

Suicide remains a public health challenge, necessitating improved detection methods to facilitate timely intervention and treatment. This systematic r...

Machine learning prediction of suicide attempts in major depression: Feature selection and model development using national survey data.

BACKGROUND: The study aimed to explore the risk factors of suicidal attempts (SA) in patients with major depressive disorders (MDD). METHODS: Cross-se...

Demonstrating the potential of untargeted hair proteomics for personalized biomarkers in stress-associated disorders.

Biomarker research in psychopathology increasingly employs high-dimensional Omics approaches. Yet, proteomics based on human hair remain largely unexp...

From Likert Scales to Large Language Models: Validating a Computational Approach to Psychological Assessment of Future Self-Continuity.

Recent advances in Large Language Models (LLMs) offer new assessment approaches that can help overcome the limitations of traditional Likert-item scal...

Navigating artificial intelligence's role in suicide prevention: balancing innovation with ethical vigilance.

Integrating structural and functional brain features to classify major depressive disorder: a multi modal approach.

Major Depressive Disorder (MDD) is a common mental disorder that markedly impairs psychosocial functioning and quality of life. Multi-modal fusion met...

Screening for depression risk in asthma patients: Development and external validation of a machine learning-based predictive model.

BACKGROUND: Depression is prevalent among asthma patients, negatively impacting their quality of life, treatment adherence, and prognosis. This study ...

Biased minds, sensitive hearts: Divergent neural signatures of interpersonal sensitivity in atypical and non-atypical depression.

BACKGROUND: Atypical depression (AD) is a distinct subtype of depression, with interpersonal sensitivity as one of its core characteristics. However, ...

Association between triglyceride-glucose index and depression and the mediating role of hypertension: a cross-sectional study of obese adults.

BACKGROUND: Recent findings indicate a positive correlation between the TyG (triglyceride-glucose) index and the incidence of depression. However, the...

Interpretable machine learning models for predicting depression status and remission three years later in older adults in China: A longitudinal study.

BACKGROUND: Depression significantly impacts older adults, making it valuable to use machine learning to predict their future depressive status and as...

Primary-care-focused interpretable machine learning model for depression screening in geriatrics: A comparative study of multiple algorithms.

BACKGROUND: The prevalence of depression among older adults places a considerable strain on healthcare systems due to a shortage of psychiatrists for ...

Development and validation of a machine learning model for predicting the risk of current depression in physically inactive adults in the United States.

Depression is a major global public health concern, with physical inactivity recognized as a key modifiable risk factor. However, tools for predicting...

Modeling homosynaptic and heterosynaptic plasticity with a single neuromemristive synapse.

INTRODUCTION: Efforts are being made to design a brain-like intelligence due to its robustness, synaptic modification (i.e., learning and memory), ana...

Neurophysiological mechanisms and predictive modeling of SSRI treatment response in depression disorder based on multidimensional EEG features.

BACKGROUND: Depression exhibits significant heterogeneity in antidepressant treatment response. This study aimed to develop an Electroencephalography ...

Sleep disturbance recorded via wearable sensors predicts depression severity 9 years later.

BACKGROUND: Major depressive disorder (MDD) is prevalent and poses major public health implications. Autonomic nervous system (ANS) dysregulation and ...