AIMC Topic: Depressive Disorder, Major

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Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

Artificial intelligence in medicine
OBJECTIVE: The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining te...

Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression.

Psychiatry research
Standard functional magnetic resonance imaging (fMRI) analyses cannot assess the potential of a neuroimaging signature as a biomarker to predict individual vulnerability to major depression (MD). Here, we use machine learning for the first time to ad...

PsyGeNET: a knowledge platform on psychiatric disorders and their genes.

Bioinformatics (Oxford, England)
UNLABELLED: PsyGeNET (Psychiatric disorders and Genes association NETwork) is a knowledge platform for the exploratory analysis of psychiatric diseases and their associated genes. PsyGeNET is composed of a database and a web interface supporting data...

Feature Selection and Classification of Electroencephalographic Signals: An Artificial Neural Network and Genetic Algorithm Based Approach.

Clinical EEG and neuroscience
Feature selection is an important step in many pattern recognition systems aiming to overcome the so-called curse of dimensionality. In this study, an optimized classification method was tested in 147 patients with major depressive disorder (MDD) tre...

Real-world effectiveness of cariprazine in major depressive disorder and bipolar I disorder in the United States.

Journal of medical economics
AIMS: The efficacy of cariprazine for major depressive disorder (MDD) (adjunctive therapy) and bipolar I (BP-I) depression has been demonstrated in clinical trials. This study evaluated the real-world effectiveness of cariprazine in reducing depressi...

[Psilocybin in the setting of treatment-resistant unipolar depression: A case report].

L'Encephale
BACKGROUND: Current antidepressants have shown certain limitations in the treatment of unipolar depression. Their long onset of action, interactions, and side effects are obstacles to achieving lasting remission of this prevalent, often chronic or re...

Comparative cytokine signatures and cognitive deficits in early-onset schizophrenia and adolescent major depression: Toward refined diagnostic classification frameworks.

Journal of affective disorders
BACKGROUND: This study analyzed plasma cytokine patterns in individuals with schizophrenia (SCZ), major depressive disorder (MDD), and healthy controls, explored the link between cytokine levels and cognitive function, and created machine learning mo...

Predicting rTMS treatment response in depression: use of machine learning models to identify the roles of metabolic and clinical factors.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for depression in patients with major depressive disorder (MDD) and bipolar disorder (BD), but accurate prediction of treatment response remains a challenge. Th...

Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

Journal of affective disorders
BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) is a promising treatment for major depression disorder (MDD), particularly for treatment-resistant cases. However, identifying translatable biomarkers predictive of treatment outcomes re...

Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk stratification in major depressive disorder in a prospective longitudinal study.

Journal of affective disorders
BACKGROUND: Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymph...