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

Biological Influences on Intelligence01:30

Biological Influences on Intelligence

387
Intelligence is often thought to be linked to brain size, but the relationship is more complex than that. While brain size does correlate modestly with some abilities, like verbal skills, the connection is weaker for others, such as spatial reasoning. Other factors, like brain structure, also play crucial roles. For instance, despite Einstein's smaller-than-average brain, his parietal cortex, which is involved in spatial reasoning, was 15% wider, suggesting that neural density might matter...
387
Neural Circuits01:25

Neural Circuits

2.4K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
2.4K

You might also read

Related Articles

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

Sort by
Same author

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026
Same author

Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations.

Nature communications·2026
Same author

Dual controllability de-differentiation of functional brain networks in major depressive disorder: Insights from large-scale neuroimaging and transcriptomic integration.

Journal of affective disorders·2026
Same author

Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.

PLOS digital health·2026
Same author

Multi-Site Transfer Classification of Major Depressive Disorder: An fMRI Study in 3335 Subjects.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping.

Medical image analysis·2026

Related Experiment Video

Updated: Dec 6, 2025

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

7.5K

A Deep Network Model on Dynamic Functional Connectivity With Applications to Gender Classification and Intelligence

Liangwei Fan1, Jianpo Su1, Jian Qin1

  • 1College of Intelligence Science and Technology, National University of Defense Technology, Changsha, China.

Frontiers in Neuroscience
|October 5, 2020
PubMed
Summary

A new deep learning model accurately predicts gender and intelligence using brain connectivity patterns. This approach effectively captures dynamic functional connectivity, outperforming previous methods for individualized characterization.

Keywords:
deep learningdynamic functional connectivity (dFC)gender classificationintelligence predictionresting-state functional magnetic resonance imaging

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

1.4K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.4K

Related Experiment Videos

Last Updated: Dec 6, 2025

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

7.5K
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

1.4K
A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
09:01

A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance

Published on: May 7, 2014

10.4K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Dynamic properties of functional brain networks correlate with behavior and cognition.
  • Current fMRI methods analyzing dynamic functional connectivity (dFC) may miss subtle temporal patterns.
  • Resting-state functional connectivity analysis is crucial for understanding brain function.

Purpose of the Study:

  • To develop an advanced deep learning model for analyzing dynamic functional connectivity (dFC).
  • To capture both temporal and spatial features of functional connectivity sequences.
  • To improve the prediction of demographic and cognitive traits using brain network dynamics.

Main Methods:

  • An end-to-end deep learning model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) was proposed.
  • The model was applied to resting-state fMRI data from the Human Connectome Project (n=1,050).
  • Model performance was evaluated on gender classification and intelligence prediction tasks.

Main Results:

  • The deep learning model achieved 93% accuracy in gender classification.
  • Prediction accuracies for fluid and crystallized intelligence were 0.31 and 0.49 (Pearson's r), respectively.
  • The model demonstrated significant learning of spatiotemporal dFC dynamics.

Conclusions:

  • Deep learning models offer advantages in utilizing dynamic information from resting-state functional connectivity.
  • Time-varying connectivity patterns hold significant potential for individualized characterization.
  • This approach enhances the analysis of brain network dynamics for predicting traits.