Depression

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

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 remains underexplored.

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

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 abnormalities in MDD. However, predicting treatment response remains challenging. This study leverages inter-brain similarity from sMRI as a novel feature...

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

BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagno...

Identifying key risk factors for intentional self-harm, including suicide, among a cohort of people prescribed opioid agonist treatment: A predictive modelling study.

BACKGROUND AND AIMS: People with opioid use disorder are at increased risk of intentional self-harm and suicide. Although risk factors are well known,...

Using Digital Phenotyping to Discriminate Unipolar Depression and Bipolar Disorder: Systematic Review.

BACKGROUND: Differentiating bipolar disorder (BD) from unipolar depression (UD) is essential, as these conditions differ greatly in their progression ...

Unlocking the Potential of Wear Time of a Wearable Device to Enhance Postpartum Depression Screening and Detection: Cross-Sectional Study.

BACKGROUND: Postpartum depression (PPD) is a mood disorder affecting 1 in 7 women after childbirth that is often underscreened and underdetected. If n...

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

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tac...

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

BACKGROUND: Current antidepressants have shown certain limitations in the treatment of unipolar depression. Their long onset of action, interactions, ...

Optimizing Treatment for Depression in Primary Care Using Psychotherapy Versus Antidepressant Medication in a Low-Resource Setting: Protocol for the OptimizeD Randomized Controlled Trial.

Psychotherapy and antidepressant medications are first-line treatments for depression, and they both have significant treatment effects on average. H...

NSSI-Net: A Multi-Concept GAN for Non-Suicidal Self-Injury Detection Using High-Dimensional EEG in a Semi-Supervised Framework.

Non-suicidal self-injury (NSSI) is a serious threat to the physical and mental health of adolescents, significantly increasing the risk of suicide and...

Speech and Voice Quality as Digital Biomarkers in Depression: A Systematic Review.

OBJECTIVE: To review the current evidence on the use of artificial intelligence-driven speech and voice analysis as a biomarker for depression.

Postpartum depression in Northeastern China: a cross-sectional study 6 weeks after giving birth.

INTRODUCTION: Postpartum depression (PPD) is a prevalent mental health issue that poses significant challenges to maternal wellbeing and infant develo...

Unveiling the invisible: How cutting-edge neuroimaging transforms adolescent depression diagnosis.

Yu 's study has advanced the understanding of the neural mechanisms underlying major depressive disorder (MDD) in adolescents, emphasizing the signifi...

MT-RCAF: A Multi-Task Residual Cross Attention Framework for EEG-based emotion recognition and mood disorder detection.

BACKGROUND AND OBJECTIVE: Prolonged abnormal emotions can gradually evolve into mood disorders such as anxiety and depression, making it critical to s...

Automated Depression Detection from Text and Audio: A Systematic Review.

Depression is a prevalent mental health disorder that presents significant challenges for timely diagnosis and intervention. Automated Depression Dete...

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

Type 2 diabetes mellitus (T2DM) and Major depressive disorder (MDD) act as risk factors for each other, and the comorbidity of both significantly incr...

Subtyping first-episode psychosis based on longitudinal symptom trajectories using machine learning.

Clinical course after first episode psychosis (FEP) is heterogeneous. Subgrouping and predicting longitudinal symptom trajectories after FEP may help ...

Nursing Students' Perceptions of AI-Driven Mental Health Support and Its Relationship with Anxiety, Depression, and Seeking Professional Psychological Help: Transitioning from Traditional Counseling to Digital Support.

: The integration of artificial intelligence (AI) into mental health care is reshaping psychological support systems, particularly for digitally liter...

Incorporating AI Into Military Behavioral Health: A Narrative Review.

INTRODUCTION: Concerns regarding suicide rates and declining mental health among service members highlight the need for impactful approaches to addres...

WavFace: A Multimodal Transformer-Based Model for Depression Screening.

Depression, a prevalent mental health disorder with severe health and economic consequences, can be costly and difficult to detect. To alleviate this ...