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

Multi-view Chest X-Ray Vision-Language Pre-training via Semantic-Aware Masked Language Modeling and High-order Alignment.

IEEE transactions on medical imaging·2026
Same author

Diffusion models for brain imaging computing: a survey of frameworks and applications.

Brain informatics·2026
Same author

Multimodal artificial intelligence in retinopathy of prematurity: A comprehensive narrative review.

Survey of ophthalmology·2026
Same author

Semi-URF: Progressive Uncertainty-Aware Region Filtering and Fusion for Semi-Supervised Medical Image Segmentation.

IEEE journal of biomedical and health informatics·2026
Same author

Structural-Functional Connectome Generation via Diffusion-Guided Graph Transformer for Alzheimer's Disease Analysis.

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

A fundus image dataset for intelligent diabetic retinopathy system.

Scientific data·2026

Related Experiment Video

Updated: Dec 27, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

12.1K

Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification.

Yang Li, Jingyu Liu, Zhenyu Tang

    IEEE Transactions on Medical Imaging
    |March 1, 2020
    PubMed
    Summary

    This study introduces a new adaptive dynamic functional connectivity model for identifying mild cognitive impairment (MCI). The novel method improves classification accuracy, offering a more effective tool for early brain abnormality detection.

    More Related Videos

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.6K
    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
    08:36

    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

    Published on: March 21, 2019

    7.6K

    Related Experiment Videos

    Last Updated: Dec 27, 2025

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    12.1K
    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
    07:12

    Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

    Published on: July 1, 2014

    12.6K
    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
    08:36

    Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

    Published on: March 21, 2019

    7.6K

    Area of Science:

    • Neuroimaging
    • Computational Neuroscience
    • Biomedical Engineering

    Background:

    • Resting-state functional Magnetic Resonance Imaging (rs-fMRI) with dynamic functional connectivity (dFC) is crucial for understanding brain activity dynamics in diseases.
    • Traditional sliding window methods for dFC are sensitive to parameter selection, limiting their ability to capture temporal brain activity variations.

    Purpose of the Study:

    • To propose a novel adaptive dFC model for improved mild cognitive impairment (MCI) identification.
    • To overcome the limitations of parameter sensitivity in conventional dFC analysis.

    Main Methods:

    • An adaptive Ultra-weighted-lasso recursive least squares algorithm was employed to estimate adaptive dFC, mitigating parameter optimization issues.
    • Deep spatial-temporal feature fusion was utilized to map extracted features into comprehensive representations for classification.

    Main Results:

    • The proposed adaptive dFC model achieved a classification accuracy of 87.7% for MCI identification.
    • This represents a significant improvement of at least 5.5% compared to existing state-of-the-art methods.

    Conclusions:

    • The developed adaptive dFC model demonstrates superior performance in MCI classification.
    • The findings highlight the method's potential for effective early identification of brain abnormalities.