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

Long-term Depression01:05

Long-term Depression

31.3K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
31.3K
Depression: Overview01:18

Depression: Overview

322
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,...
322
Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

201
Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...
201
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

153
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...
153
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.9K
Learning Disabilities01:25

Learning Disabilities

261
Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...
261

You might also read

Related Articles

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

Sort by
Same author

A deep learning-based framework for standardized analysis of trabecular bone compartments from micro-CT imaging data in the mouse tibia.

Scientific reports·2025
Same author

Advancing medical question answering with a knowledge embedding transformer.

PloS one·2025
Same author

Multichannel convolutional transformer for detecting mental disorders using electroancephalogrpahy records.

Scientific reports·2025
Same author

A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone.

Scientific reports·2025
Same author

Prediction and detection of terminal diseases using Internet of Medical Things: A review.

Computers in biology and medicine·2025
Same author

Artificial Intelligence non-invasive methods for neonatal jaundice detection: A review.

Artificial intelligence in medicine·2025
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
See all related articles

Related Experiment Video

Updated: Aug 27, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K

A Model of Normality Inspired Deep Learning Framework for Depression Relapse Prediction Using Audiovisual Data.

Alice Othmani1, Assaad-Oussama Zeghina1, Muhammad Muzammel1

  • 1Université Paris-Est Créteil (UPEC), LISSI, Vitry sur Seine 94400, France.

Computer Methods and Programs in Biomedicine
|October 2, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning method for detecting depression and predicting relapse using audiovisual cues. The model achieved 87.4% accuracy, offering a promising tool for patient monitoring.

Keywords:
Biomedical applicationClinical depressionComputer-aided diagnosis (CAD)Deep learningDepression relapse predictionHealth informaticsVideo analysis

More Related Videos

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.4K
Animal Models of Depression - Chronic Despair Model CDM
05:47

Animal Models of Depression - Chronic Despair Model CDM

Published on: September 23, 2021

7.4K

Related Experiment Videos

Last Updated: Aug 27, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.2K
Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
05:19

Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment

Published on: July 7, 2023

2.4K
Animal Models of Depression - Chronic Despair Model CDM
05:47

Animal Models of Depression - Chronic Despair Model CDM

Published on: September 23, 2021

7.4K

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Psychiatry

Background:

  • Major Depressive Disorder affects over 300 million globally.
  • Spontaneous remission occurs in 6-12 months for first episodes.
  • Depression impacts speech and facial expressions, yet relapse prediction via audiovisual cues is understudied.

Purpose of the Study:

  • To develop a deep learning approach for depression recognition and relapse prediction.
  • To define depression relapse as the proximity of audiovisual patterns post-remission to those of depressed individuals.
  • To utilize a Model of Normality framework for anomaly detection.

Main Methods:

  • A deep learning framework based on the Model of Normality.
  • Anomaly detection using distance-based computation of normality.
  • Analysis of audiovisual data for deep encoding and pattern comparison.

Main Results:

  • Achieved 87.4% accuracy for depression and relapse prediction.
  • Obtained an F1-score of 82.3% on the DAIC-Woz dataset.
  • Employed a Leave-One-Subject-Out cross-validation strategy.

Conclusions:

  • The Model of Normality framework accurately detects depression and predicts relapse.
  • A prospective monitoring system is proposed to aid depressed patients.
  • The framework is extensible for integrating additional data modalities.