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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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

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A Multiview Deep Learning Method for Brain Functional Connectivity Classification.

Yu Ji¹, Cuicui Yang¹, Yuze Liang¹

¹Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, College of Computer Science, Faculty of Information Technology, Beijing University of Technology, Beijing, China.

Computational Intelligence and Neuroscience

|October 18, 2022

Summary

This summary is machine-generated.

This study introduces a novel multiview deep learning approach to improve brain functional connectivity classification for clinical use. The method enhances diagnostic accuracy for neuropsychiatric disorders by integrating diverse data perspectives.

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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

Area of Science:

Neuroscience
Computer Science
Medical Imaging

Background:

Deep learning for brain functional connectivity classification is a growing field.
Current methods struggle to meet clinical application demands due to performance limitations.

Purpose of the Study:

To propose a multiview deep learning method to enhance brain functional connectivity classification.
To improve the accuracy and clinical utility of deep learning models in this domain.

Main Methods:

Utilized multiple brain atlases to define distinct views of brain functional connectivity.
Employed a multiview feature selection strategy for optimal feature identification.
Applied stacked autoencoders for deep feature extraction within each view.
Implemented a multiview fusion strategy to integrate complementary information.

Main Results:

The proposed multiview deep learning method demonstrated superior performance compared to existing deep learning approaches.
Validation on three public datasets for neuropsychiatric disorders confirmed the method's effectiveness.
The approach successfully leveraged complementary information across different views for improved classification.

Conclusions:

The developed multiview deep learning framework significantly advances brain functional connectivity classification.
This method shows promise for more accurate and reliable clinical applications in diagnosing neuropsychiatric disorders.