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

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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.

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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

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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.