AIMC Topic: Depressive Disorder, Major

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Contrastive functional connectivity defines neurophysiology-informed symptom dimensions in major depression.

Cell reports. Medicine
Major depressive disorder (MDD) is highly heterogeneous, posing challenges for effective treatment due to complex interactions between clinical symptoms and neurobiological features. To address this, we apply contrastive principal-component analysis ...

Predicting treatment response in individuals with major depressive disorder using structural MRI-based similarity features.

BMC psychiatry
BACKGROUND: Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Structural magnetic resonance imaging (sMRI) and machine learning have shown promise in psychiatry, offering insights into brain abno...

A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.

Journal of neuroengineering and rehabilitation
BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagnosis, treatment planning, and monitoring, yet current clinical tools are largely subjective, relying ...

Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.

Scientific reports
Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tackle this, we developed a cloud-based computer-aided depression diagnostic (CCADD) system that utiliz...

Exploring potential diagnostic markers and therapeutic targets for type 2 diabetes mellitus with major depressive disorder through bioinformatics and in vivo experiments.

Scientific reports
Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly increases the all-cause mortality rate. Therefore, studying the diagnosis and treatment of diabetes with...

A depression detection approach leveraging transfer learning with single-channel EEG.

Journal of neural engineering
Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods combining electroencephalography (EEG) with machine learning or deep learning have been proposed to objectively distinguish between MDD and healthy indi...

Machine learning based differential diagnosis of schizophrenia, major depression disorder and bipolar disorder using structural magnetic resonance imaging.

Journal of affective disorders
BACKGROUND: Cortical morphological abnormalities in schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD) have been identified in past research. However, their potential as objective biomarkers to differentiate these disorde...

Specific expression and common potential therapeutic drugs in different brain regions of major depressive disorder patients: bioinformatics analysis.

Journal of affective disorders
Major depressive disorder (MDD) is a prevalent and debilitating mental health condition characterized by persistent feelings of sadness and loss of interest. Despite its high prevalence, the underlying molecular mechanisms remain poorly understood. T...

Integrative analysis of signaling and metabolic pathways, immune infiltration patterns, and machine learning-based diagnostic model construction in major depressive disorder.

Scientific reports
Major depressive disorder (MDD) is a multifactorial disorder involving genetic and environmental factors, with unclear pathogenesis. This study aims to explore the pathogenic pathway of MDD and its relationship with immune responses and to discover i...

Semantic signals in self-reference: The detection and prediction of depressive symptoms from the daily diary entries of a sample with major depressive disorder.

Journal of psychopathology and clinical science
Individuals with major depressive disorder (MDD) experience fewer positive and more negative emotions and use fewer positive words to describe themselves. Natural language processing techniques have been used to predict depression, with pronoun and e...