Jove
Visualize
Contact Us

Related Concept Videos

Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

1.1K
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...
1.1K
Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

846
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...
846
Bipolar Disorder01:30

Bipolar Disorder

1.4K
Bipolar disorder is a chronic mental health condition marked by significant mood fluctuations, including episodes of mania and depression. Elevated energy levels, heightened mood or irritability, impulsive behavior, reduced sleep needs, rapid speech, racing thoughts, inflated self-esteem, and distractibility characterize mania. Individuals with bipolar disorder often alternate between depressive and manic states, with periods of emotional stability lasting an average of six months to a year.
1.4K
Long-term Depression01:05

Long-term Depression

33.7K
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.
33.7K
Long-term Depression01:03

Long-term Depression

3.6K
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.
Calcium Ion Concentration Mechanism
If over...
3.6K
Depression: Overview01:18

Depression: Overview

1.2K
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,...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Correction: Depression detection using deep learning and large language models from multimodalities.

Frontiers in digital health·2026
Same author

Depression detection using deep learning and large language models from multimodalities.

Frontiers in digital health·2026
Same author

Associations between food-related behaviours, nutrient intake and nutritional status through Structural Equation Model (SEM) among clients undergoing Community-Based Treatment and Rehabilitation (CBTaR): A cross-sectional study in Kelantan, Malaysia.

BMJ open·2026
Same author

Unraveling cognitive impairments in mental and neurological disorders: A biomarker-based mapping framework.

Neuroscience and biobehavioral reviews·2025
Same author

Explainable AI in Diagnostic Radiology for Neurological Disorders: A Systematic Review, and What Doctors Think About It.

Diagnostics (Basel, Switzerland)·2025
Same author

Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.

Sensors (Basel, Switzerland)·2024
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 Experiment Video

Updated: Mar 27, 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

14.8K

Detrended fluctuation analysis for major depressive disorder.

Wajid Mumtaz, Aamir Saeed Malik, Syed Saad Azhar Ali

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    Electroencephalography (EEG) analysis using machine learning shows promise for diagnosing major depressive disorder (MDD). De-trended fluctuation analysis (DFA) effectively identified long-range temporal correlations in EEG data for MDD patient discrimination.

    More Related Videos

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
    08:25

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

    Published on: December 6, 2024

    1.3K
    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

    4.4K

    Related Experiment Videos

    Last Updated: Mar 27, 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

    14.8K
    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
    08:25

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

    Published on: December 6, 2024

    1.3K
    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

    4.4K

    Area of Science:

    • Neuroscience
    • Computational Psychiatry
    • Biomedical Engineering

    Background:

    • The clinical utility of Electroencephalography (EEG) for diagnosing major depressive disorder (MDD) remains unclear.
    • Objective diagnostic biomarkers are needed to complement current MDD assessment methods.

    Purpose of the Study:

    • To develop and validate a novel machine learning (ML) scheme for discriminating MDD patients from healthy controls using EEG data.
    • To investigate the efficacy of de-trended fluctuation analysis (DFA) in extracting relevant features from EEG signals for MDD diagnosis.

    Main Methods:

    • EEG data were acquired under eyes closed (EC) and eyes open (EO) conditions.
    • De-trended fluctuation analysis (DFA) was employed for feature extraction, quantifying long-range temporal correlations (LRTC) via scaling exponents.
    • Feature selection techniques and a logistic regression classifier were utilized, with validation through 10-fold cross-validation.

    Main Results:

    • The study evaluated the impact of different reference montages (LE, IR, AR) on DFA-derived features.
    • DFA analysis demonstrated superior performance in LE data compared to IR and AR data.
    • Feature selection methods showed varying performance, with AR performing better than LE and IR during Wilcoxon ranking.

    Conclusions:

    • De-trended fluctuation analysis (DFA) provides valuable information for discriminating MDD patients from healthy individuals.
    • The proposed ML scheme, particularly utilizing DFA, shows potential for clinical application in MDD diagnosis, pending further validation.