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

You might also read

Related Articles

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

Sort by
Same author

Calibrated ROI-gated conditional computation for high-throughput and backbone-agnostic brain tumor MRI classification.

Computer methods and programs in biomedicine·2026
Same author

From Traditional Machine Learning to Fine-Tuning Large Language Models: A Review for Sensors-Based Soil Moisture Forecasting.

Sensors (Basel, Switzerland)·2025
Same author

Explainable multimodal fusion for breast carcinoma diagnosis: A systematic review, open problems, and future directions.

Computer methods and programs in biomedicine·2025
Same author

A Novel Agent-Based Approach for Dynamic Emotion Modeling in Social Networks.

IEEE transactions on cybernetics·2025
Same author

Federated Learning in radiomics: A comprehensive meta-survey on medical image analysis.

Computer methods and programs in biomedicine·2025
Same author

Continuous Monitoring of Sleep-Related Biomarkers via a Nearable Solution Based on Fiber Bragg Grating Technology.

IEEE journal of biomedical and health informatics·2025

Related Experiment Video

Updated: Nov 7, 2025

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
06:04

Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

Published on: January 17, 2025

910

Depression Analysis and Recognition Based on Functional Near-Infrared Spectroscopy.

Rui Wang, Yixue Hao, Qiao Yu

    IEEE Journal of Biomedical and Health Informatics
    |April 30, 2021
    PubMed
    Summary

    Functional near-infrared spectroscopy (fNIRS) effectively identifies depression by analyzing brain connectivity and prefrontal lobe activity. This method shows promise for clinical diagnosis and treatment of depression.

    More Related Videos

    Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design
    06:18

    Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

    Published on: December 3, 2020

    4.0K
    Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy
    12:40

    Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy

    Published on: July 28, 2009

    20.8K

    Related Experiment Videos

    Last Updated: Nov 7, 2025

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling
    06:04

    Functional Near-Infrared Spectroscopy Hyperscanning Study in Psychological Counseling

    Published on: January 17, 2025

    910
    Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design
    06:18

    Qualitative and Comparative Cortical Activity Data Analyses from a Functional Near-Infrared Spectroscopy Experiment Applying Block Design

    Published on: December 3, 2020

    4.0K
    Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy
    12:40

    Exploring Cognitive Functions in Babies, Children & Adults with Near Infrared Spectroscopy

    Published on: July 28, 2009

    20.8K

    Area of Science:

    • Neuroscience
    • Medical Imaging
    • Artificial Intelligence

    Background:

    • Depression stems from complex bio-psycho-social factors.
    • Understanding brain function in depression is crucial for diagnosis and treatment.
    • Functional near-infrared spectroscopy (fNIRS) offers a non-invasive method for brain activity assessment.

    Purpose of the Study:

    • To establish a comprehensive fNIRS-based architecture for deep modeling of depression.
    • To develop novel feature extraction and deep neural network methods for depression recognition.
    • To investigate brain activity patterns in depressed individuals using fNIRS.

    Main Methods:

    • Developed a source-feature-model architecture for fNIRS data.
    • Employed time and frequency domain methods for feature extraction.
    • Utilized deep neural networks, including AlexNet and ResNet18, for depression recognition.
    • Analyzed encephalic area connectivity and prefrontal lobe activation.

    Main Results:

    • Depression patients exhibit weaker encephalic area connectivity and reduced prefrontal lobe activation compared to non-depressed individuals.
    • The AlexNet model, particularly using channel correlations, achieved the highest performance.
    • Achieved an accuracy of 0.90 and a precision of 0.91, outperforming other models and algorithms.

    Conclusions:

    • Brain region correlation analysis using fNIRS is effective for depression recognition.
    • The proposed fNIRS-based deep learning architecture shows significant potential for clinical diagnosis and treatment of depression.
    • fNIRS provides valuable insights into brain function alterations associated with depression.