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

Influence of antihypertensive drug classes on circadian blood pressure patterns and left ventricular remodeling - a retrospective study.

European journal of clinical pharmacology·2026
Same author

Coordinated fault-tolerant pressure control for integrated electro-hydraulic braking system.

ISA transactions·2026
Same author

The Effect of Pyrite Content in Aggregates on Concrete Deformation and Failure Prediction.

Materials (Basel, Switzerland)·2026
Same author

Enhanced Antitumor Efficacy of Combined PTC596 and PD1 Blockade in Hepatocellular Carcinoma.

Molecular cancer therapeutics·2026
Same author

Preparation, immunological and pharmacological effects of flavonoids in Scutellariae radix: a review.

Frontiers in pharmacology·2026
Same author

Hidden Diversity in the Sands: Genomic Footprints of Pleistocene Refugia and Fragile Futures of the Turkestan Ground-Jay (Podoces panderi) in Central Asia.

Integrative zoology·2026
Same journal

Transfer Learning with Simulated and Recorded Data Improves Predictions of Lateral Pinch Thumb-Tip Forces.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Sensing Muscle Deformation for Upper-Limb Prosthetic Control: a Narrative Review.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Entropy-Based Graph Learning Framework for Cross-Subject Detection of Electrical Status Epilepticus During Sleep (ESES).

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Touch-related electrophysiology activity promotes human movements initiation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same journal

Motion Intention Recognition and DDPG-Based Adaptive Impedance Control for a Robotic Upper-Limb Exoskeleton.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
See all related articles

Related Experiment Video

Updated: May 24, 2025

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
12:21

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Published on: September 12, 2011

25.1K

Improving fMRI-Based Autism Severity Identification via Brain Network Distance and Adaptive Label Distribution

Junling Du, Shangyu Wang, Rentong Chen

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel machine learning method to accurately identify autism spectrum disorder (ASD) severity using brain functional networks (BFN). The approach enhances diagnostic performance, offering potential for clinical application.

    More Related Videos

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.6K
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.4K

    Related Experiment Videos

    Last Updated: May 24, 2025

    Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
    12:21

    Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

    Published on: September 12, 2011

    25.1K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    15.6K
    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.4K

    Area of Science:

    • Neuroscience
    • Machine Learning
    • Developmental Psychology

    Background:

    • Autism spectrum disorder (ASD) diagnosis and severity assessment remain challenging due to label ambiguity and individual variability.
    • Current functional magnetic resonance imaging (fMRI)-based methods for ASD severity identification lack satisfactory performance.
    • The relationship between brain functional networks (BFN) and ASD symptom severity requires further investigation.

    Purpose of the Study:

    • To develop an advanced machine learning framework for accurate autism spectrum disorder (ASD) severity identification.
    • To address limitations in current fMRI-based ASD severity assessment methods.
    • To explore the association between BFN characteristics and ASD symptom severity.

    Main Methods:

    • Proposed a low- and high-level BFN distance (HBFND) method to construct BFN reflecting ASD severity differences.
    • Utilized a multi-task network to account for individual variations in ASD communication and social skills.
    • Employed an adaptive label distribution (ALD) technique to train the model and prevent overfitting.

    Main Results:

    • The proposed HBFND-AMLD framework demonstrated superior performance in ASD severity identification compared to state-of-the-art methods.
    • The HBFND method effectively measured differences between individuals with ASD and healthy controls (HC) in low- and high-order BFN.
    • The ALD technique successfully prevented model overfitting, enhancing identification accuracy.

    Conclusions:

    • The developed HBFND-AMLD framework shows significant potential for practical clinical diagnosis of ASD severity.
    • This approach offers improved identification performance by considering BFN and individual differences in ASD.
    • Further research into BFN and ASD severity could lead to more refined diagnostic tools.