AIMC Topic: Depressive Disorder, Major

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Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning.

Asian journal of psychiatry Feb 10, 2023
The present study aims to identify suicide risks in major depressive disorders (MDD) patients from structural MRI (sMRI) data using deep learning. In this paper, we collected the sMRI data of 288 MDD patients, including 110 patients with suicide idea...
Suicide, Attempted Deep Learning Suicidal Ideation Humans Depressive Disorder, Major
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A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data.

Computer methods and programs in biomedicine Sep 20, 2022
BACKGROUND: Depression (Major Depressive Disorder) is one of the most common mental illnesses. According to the World Health Organization, more than 300 million people in the world are affected. A first depressive episode can be solved by a spontaneo...
Depressive Disorder, Major Deep Learning Depression Humans Prospective Studies Recurrence
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Sex differences in rTMS treatment response: A deep learning-based EEG investigation.

Brain and behavior Jul 25, 2022
INTRODUCTION: The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MD...
Male Depressive Disorder, Major Deep Learning Treatment Outcome Transcranial Magnetic Stimulation Humans Electroencephalography Female Sex Characteristics
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Deep learning-based integration of genetics with registry data for stratification of schizophrenia and depression.

Science advances Jun 29, 2022
Currently, psychiatric diagnoses are, in contrast to most other medical fields, based on subjective symptoms and observable signs and call for new and improved diagnostics to provide the most optimal care. On the basis of a deep learning approach, we...
Humans Depressive Disorder, Major Deep Learning Schizophrenia Depression Registries
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Employing biochemical biomarkers for building decision tree models to predict bipolar disorder from major depressive disorder.

Journal of affective disorders Apr 16, 2022
BACKGROUND: Conventional biochemical parameters may have predictive values for use in clinical identification between bipolar disorder (BD) and major depressive disorder (MDD).
Uric Acid Humans Bipolar Disorder Prealbumin Bilirubin Biomarkers Decision Trees Triglycerides Female Thyrotropin Male Depressive Disorder, Major
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Treatment selection using prototyping in latent-space with application to depression treatment.

PloS one Nov 12, 2021
Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. W...
Clinical Trials as Topic Antidepressive Agents Area Under Curve Remission Induction Drug Therapy, Combination Depression Treatment Outcome Humans Clinical Decision-Making Precision Medicine Depressive Disorder, Major Deep Learning
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Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis.

Psychological medicine Oct 12, 2021
BACKGROUND: Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a gi...
Treatment Outcome Prognosis Humans Depressive Disorder, Major Machine Learning Depression
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Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Human brain mapping Aug 13, 2021
Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive symptoms relates to separable neural networks remains poorly understood. We build on work describing thre...
Depressive Disorder, Major Magnetic Resonance Imaging Outcome Assessment, Health Care Adult Male Middle Aged Neuroimaging Cerebral Cortex Electroconvulsive Therapy Humans Machine Learning Female Aged
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Reproducibility of automated habenula segmentation via deep learning in major depressive disorder and normal controls with 7 Tesla MRI.

Scientific reports Jun 29, 2021
The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to...
Magnetic Resonance Imaging Habenula Male Depressive Disorder, Major Deep Learning Reproducibility of Results Adult Humans Female
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Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

PloS one Jun 28, 2021
OBJECTIVES: Antidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict trea...
Humans Machine Learning Algorithms Biomarkers Datasets as Topic Clinical Trials as Topic Antidepressive Agents Depressive Disorder, Treatment-Resistant Depressive Disorder, Major Female Canada Adult Treatment Outcome Male
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