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Depressive Disorder, Major

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Development of machine-learning-driven signatures for diagnosing and monitoring therapeutic response in major depressive disorder using integrated immune cell profiles and plasma cytokines.

Theranostics
Diagnosis and treatment efficacy of major depressive disorder (MDD) currently lack stable and reliable biomarkers. Previous research has suggested a potential association between immune cells, cytokines, and the pathophysiology and treatment of MDD....

Altered Blood Oxygen Level-Dependent Signal Stability in the Brain of Patients with Major Depressive Disorder Undergoing Resting-State Functional Magnetic Resonance Imaging.

Neuropsychobiology
INTRODUCTION: Major depressive disorder (MDD) is a common, relapse-prone psychiatric disorder with unknown pathogenesis. Previous studies on resting-state functional magnetic resonance imaging of MDD have mostly focused on the spontaneous activity of...

Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis.

Sensors (Basel, Switzerland)
The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely u...

The efficacy of topological properties of functional brain networks in identifying major depressive disorder.

Scientific reports
Major Depressive Disorder (MDD) is a common mental disorder characterized by cognitive impairment, and its pathophysiology remains to be explored. In this study, we aimed to explore the efficacy of brain network topological properties (TPs) in identi...

Toward molecular diagnosis of major depressive disorder by plasma peptides using a deep learning approach.

Briefings in bioinformatics
Major depressive disorder (MDD) is a severe psychiatric disorder that currently lacks any objective diagnostic markers. Here, we develop a deep learning approach to discover the mass spectrometric features that can discriminate MDD patients from heal...

An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection.

Scientific reports
Major Depressive Disorder (MDD) is an affective disorder that can lead to persistent sadness and a decline in the quality of life, increasing the risk of suicide. Utilizing multimodal data such as electroencephalograms and patient interview audios ca...

The voice of depression: speech features as biomarkers for major depressive disorder.

BMC psychiatry
BACKGROUND: Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project ai...

Machine learning models for diagnosis and risk prediction in eating disorders, depression, and alcohol use disorder.

Journal of affective disorders
BACKGROUND: Early diagnosis and treatment of mental illnesses is hampered by the lack of reliable markers. This study used machine learning models to uncover diagnostic and risk prediction markers for eating disorders (EDs), major depressive disorder...

Model-informed approach to estimate treatment effect in placebo-controlled clinical trials using an artificial intelligence-based propensity weighting methodology to account for non-specific responses to treatment.

Journal of pharmacokinetics and pharmacodynamics
In randomized, placebo controlled clinical trials (RCT) in major depressive disorders (MDD), treatment response (TR) is estimated by the change from baseline at study-end (EOS) of the scores of clinical scales used for assessing disease severity. Tre...

Optimizing the Prediction of Depression Remission: A Longitudinal Machine Learning Approach.

American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics
Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. P...