Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Antidepressant Drugs: MAOIs and Other Agents01:23

Antidepressant Drugs: MAOIs and Other Agents

1.1K
Atypical antidepressants, including bupropion (Wellbutrin), mirtazapine (Remeron), nefazodone (Serzone), trazodone (Desyrel), and vilazodone (Viibryd), offer unique mechanisms of action. Bupropion weakly inhibits dopamine and norepinephrine reuptake, aiding depression treatment and smoking cessation, with a low risk of sexual dysfunction. Mirtazapine enhances serotonin and norepinephrine neurotransmission, leading to sedation, increased appetite, and weight gain. As a result, it helps treat...
1.1K
Psychosis: Goals of Pharmacotherapy01:26

Psychosis: Goals of Pharmacotherapy

646
Antipsychotic drugs are a crucial treatment method for acute and chronic psychoses, bipolar illness, and behavioral disorders. The selection of these drugs depends on several factors, including the state of the disease, clinical judgment, possible drug interactions, and the patient's sensitivity to adverse effects. In immediate scenarios, such as delirium and dementia, short-term treatment with low doses of high-potency typical or atypical agents can effectively manage symptom exacerbation.
646
Antidepressant Drugs: Overview01:25

Antidepressant Drugs: Overview

1.8K
Antidepressant drugs are a class of medications primarily used for treating various mood disorders, including major depression, anxiety disorders, and other related conditions. These medicines work by modulating the neurotransmitter balance within the brain, alleviating depressive symptoms. Antidepressants can be broadly categorized into several groups according to their mechanism of action and chemical structure: Selective Serotonin Reuptake Inhibitors (SSRIs), Serotonin-Norepinephrine...
1.8K
Depression: Overview01:18

Depression: Overview

1.1K
Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
1.1K
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

815
Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
815
Treatment Strategies for Psychological Disorders01:24

Treatment Strategies for Psychological Disorders

866
Treatment approaches for psychological disorders fall into three main categories: psychological, biological, and sociocultural. Each approach targets different aspects of mental health, requiring varying levels of education and training.
Psychological therapies focus on modifying emotions, thoughts, and behaviors through talking, interpreting, listening, rewarding, challenging, and modeling. Clinical psychologists, counselors, and social workers commonly practice psychotherapy. Clinical...
866

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Gastrointestinal side effects associated with antidepressant treatments in patients with major depressive disorder: A systematic review and meta-analysis.

Progress in neuro-psychopharmacology & biological psychiatry·2021
Same author

Possible Modulatory Role of ARC Gene Variants in Mood Disorders.

Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology·2021
Same author

A Practical Utility and Benefit of Pharmacogenetic-based Antidepressant Treatment Strategy for Major Depressive Disorder Patients with Difficult-to-treat.

Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology·2021
Same author

Research Domain Criteria (RDoC): A Perspective to Probe the Biological Background behind Treatment Efficacy in Depression.

Current medicinal chemistry·2021
Same author

Cost-effectiveness of genetic and clinical predictors for choosing combined psychotherapy and pharmacotherapy in major depression.

Journal of affective disorders·2020
Same author

Higher polygenic risk scores for schizophrenia may be suggestive of treatment non-response in major depressive disorder.

Progress in neuro-psychopharmacology & biological psychiatry·2020

Related Experiment Video

Updated: Mar 9, 2026

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
08:20

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder

Published on: August 11, 2015

14.7K

A New Prediction Model for Evaluating Treatment-Resistant Depression.

Alexander Kautzky1, Pia Baldinger-Melich1, Georg S Kranz1

  • 1Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.

The Journal of Clinical Psychiatry
|January 10, 2017
PubMed
Summary

Machine learning accurately predicts treatment outcomes for major depressive disorder (MDD). The model identifies patients likely to achieve remission (85%) or experience treatment-resistant depression (TRD) (74%).

More Related Videos

Conventional Repetitive Transcranial Magnetic Stimulation for Depression: A Step-by-Step Protocol
10:54

Conventional Repetitive Transcranial Magnetic Stimulation for Depression: A Step-by-Step Protocol

Published on: November 21, 2025

677
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

4.3K

Related Experiment Videos

Last Updated: Mar 9, 2026

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
08:20

MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder

Published on: August 11, 2015

14.7K
Conventional Repetitive Transcranial Magnetic Stimulation for Depression: A Step-by-Step Protocol
10:54

Conventional Repetitive Transcranial Magnetic Stimulation for Depression: A Step-by-Step Protocol

Published on: November 21, 2025

677
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
07:12

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method

Published on: August 2, 2021

4.3K

Area of Science:

  • Psychiatry
  • Computational Psychiatry
  • Machine Learning in Medicine

Background:

  • Major depressive disorder (MDD) affects many patients, with a significant portion not responding to standard antidepressant treatments.
  • Treatment-resistant depression (TRD) poses a clinical challenge, necessitating improved methods for outcome prediction.
  • Existing prediction models often lack the accuracy required for effective clinical decision-making.

Purpose of the Study:

  • To leverage machine learning and a comprehensive dataset of patient predictors to identify factors influencing antidepressant treatment outcomes.
  • To develop a predictive model for classifying treatment resistance and remission in patients with MDD.
  • To assess the predictive accuracy of machine learning models compared to traditional clinical judgment.

Main Methods:

  • Utilized a dataset from the Group for the Study of Resistant Depression, including 48 clinical, sociodemographic, and psychosocial variables.
  • Defined TRD and remission based on Hamilton Depression Rating Scale (HDRS) scores after treatment trials.
  • Employed randomForest for stepwise predictor reduction and machine learning for classification, validated on an independent patient sample.

Main Results:

  • Key predictors for treatment outcome included time between episodes, age at first treatment, initial response, symptom severity, suicidality, melancholia, episode count, admission type, education, occupation, and comorbidities (diabetes, panic, thyroid).
  • A combined model achieved high accuracy: 0.737 for predicting TRD and 0.850 for predicting remission.
  • Individual predictors showed limited accuracy, highlighting the power of integrated analysis.

Conclusions:

  • Machine learning models can predict treatment-resistant depression (TRD) and remission in MDD patients with high accuracy (74% and 85%, respectively), outperforming clinicians.
  • The findings support the use of data mining and interaction-based statistics for understanding treatment response.
  • The identified predictors are readily available in clinical settings, suggesting the model's potential for practical application and further validation.