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 Experiment Video

Updated: May 19, 2026

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

A Deep Learning Framework for Spatiotemporal Modeling of Visual Task fMRI.

Mingyang Li1, Yiwei Chen1, Chengling Ning1

  • 1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China.

Biorxiv : the Preprint Server for Biology
|May 18, 2026
PubMed
Summary

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

White matter structure-function coupling in neonatal brain and its association with Autism-related traits in early childhood.

Translational psychiatry·2026
Same author

Distinct neuroimaging subtypes of ADHD among adolescents based on semi-supervised learning.

Translational psychiatry·2025
Same author

Neural correlates differ between crystallized and fluid intelligence in adolescents.

Translational psychiatry·2025
Same author

Causal Relationships Between Screen Use, Reading, and Brain Development in Early Adolescents.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same author

Development of visual cortex in human neonates is selectively modified by postnatal experience.

eLife·2022
Same author

N-Phenyl indole derivatives as AT1 antagonists with anti-hypertension activities: Design, synthesis and biological evaluation.

European journal of medicinal chemistry·2016

This study introduces STREAM, a deep learning model for analyzing brain activity. It reveals how brain networks dynamically communicate and reconfigure for complex cognitive tasks like visual processing.

Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Traditional fMRI analysis often overlooks the dynamic information flow between brain regions, focusing instead on localized activations.
  • Understanding effective connectivity and whole-brain information flow is crucial for deciphering cognitive processes.

Purpose of the Study:

  • To introduce STREAM (Spatiotemporal Representation for Effective connectivity Analysis Model), a deep-learning framework for analyzing task-fMRI data.
  • To characterize effective connectivity and whole-brain information flow during cognitive tasks.
  • To challenge existing characterizations of brain networks, such as the Default Mode Network.

Main Methods:

  • Developed and applied the STREAM deep-learning framework to task-fMRI data from 1074 participants.
Keywords:
Deep Neural NetworksEffective ConnectivityFunctional ReconfigurationTask fMRIVirtual Brain PerturbationVisual Category Processing

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: May 19, 2026

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

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

  • STREAM learns neural transition functions to model information flow between brain regions.
  • Analyzed visual category processing to investigate dynamic signaling patterns.

    • Main Results:

    • STREAM accurately reconstructs fMRI activation maps.
    • Identified that traditional activation regions are primarily driven by incoming signals.
    • The Default Mode Network exhibits significant outgoing regulatory influence, contrary to passive characterizations.
    • Discovered that brain communication during visual tasks arises from dynamic reconfiguration of signaling patterns among hubs, not static pathways.

    Conclusions:

    • STREAM provides a novel computational paradigm for uncovering directional signaling mechanisms in task-fMRI.
    • The brain flexibly reconfigures its functional architecture to support complex cognition.
    • This approach offers new insights into how information flows and drives neural dynamics.