Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Functional Brain Systems: Reticular Formation

Functional Brain Systems: Reticular Formation

The reticular formation is a complex network of gray and white matter located within the brainstem extending from the medulla to the midbrain.
Within the reticular formation, there are several distinct nuclei that can be classified into three broad categories. The Raphe nuclei are located along the midline of the brainstem. They are primarily known for their role in synthesizing and releasing serotonin, a neurotransmitter involved in regulating mood, appetite, sleep, and circadian rhythms. The...

Neural Circuits

Neural Circuits

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...

You might also read

Related Articles

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

Sort by

Same author

Foundation Model-Based Zero-Shot Tissue Segmentation of Pathological Images via the Mixture of Local-to-Global Experts.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

TAFNet: Trusted Multiview Associative Fusion Neural Networks for Analyzing Dynamic Brain Networks.

IEEE transactions on neural networks and learning systems·2026

Same author

EfficientCovNet: Modeling the Pairwise Voxel Dependency for Brain ROI Segmentation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026

Same author

MoHD: Multi-mOdal survival prediction through Hierarchical Decoupling of whole-slide image pyramids and genomics.

Medical image analysis·2026

Same author

JointRel: Joint semantic embedding with relational message passing for knowledge graph completion.

Neural networks : the official journal of the International Neural Network Society·2026

Same author

Functional system-specific brain aging across the Alzheimer's disease continuum.

Translational psychiatry·2026

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026

Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026

Same journal

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Medical image analysis·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 20, 2025

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

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

Published on: March 21, 2019

SD-CNN: A static-dynamic convolutional neural network for functional brain networks.

Jiashuang Huang¹, Mingliang Wang², Hengrong Ju³

¹School of Information Science and Technology, Nantong University, Nantong, 226019, China; MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Medical Image Analysis

|November 24, 2022

Summary

This summary is machine-generated.

This study introduces a novel deep learning model that combines static and dynamic functional brain connectivity (sFCs and dFCs) from resting-state fMRI to improve brain disease identification. The method effectively leverages both static network topology and time-varying patterns for enhanced diagnostic accuracy.

Keywords:

Diffusion connections Dynamic functional connections Resting-state fMRI Static functional connections

More Related Videos

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

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

Published on: June 30, 2018

Related Experiment Videos

Last Updated: Aug 20, 2025

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

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

Published on: March 21, 2019

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

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

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

Published on: June 30, 2018

Area of Science:

Neuroimaging
Computational Neuroscience
Machine Learning in Medicine

Background:

Resting-state functional MRI (rs-fMRI) analysis commonly uses static functional connections (sFCs) and dynamic functional connections (dFCs).
sFCs capture overall brain network topology, while dFCs reveal temporal changes in functional connectivity.
Integrating sFCs and dFCs within deep learning frameworks for disease identification remains an active research area.

Purpose of the Study:

To propose a novel deep learning model that synergistically utilizes both static and dynamic functional brain connectivity for improved brain disease identification.
To develop a convolutional neural network architecture with distinct pathways for processing sFCs and dFCs.

Main Methods:

A static-dynamic convolutional neural network (CNN) was developed, featuring separate pathways for sFCs (high-resolution filters) and dFCs (low-resolution filters).
Diffusion connections were implemented to facilitate information transfer between the static and dynamic pathways, enhancing feature discriminability.
A combined classifier integrated features from both pathways for the final disease identification task.

Main Results:

The proposed static-dynamic CNN effectively captured both static and time-varying functional connectivity patterns.
Diffusion connections improved the discriminative power of learned features by enabling cross-pathway information exchange.
Experiments on two real-world datasets validated the method's effectiveness and superiority in brain disease identification.

Conclusions:

The static-dynamic CNN offers a powerful approach for integrating sFCs and dFCs in rs-fMRI analysis.
This integrated approach enhances the ability to identify brain diseases by leveraging comprehensive functional connectivity information.
The model demonstrates significant potential for clinical applications in neurological disorder diagnosis.