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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 Stimulation (TMS).

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Updated: May 11, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

A hypergraph transformer method for brain disease diagnosis.

Xiangmin Han¹, Jingxi Feng², Heming Xu²

¹School of Software, Tsinghua University, Beijing, China.

Frontiers in Medicine

|November 29, 2024

Summary

This summary is machine-generated.

This study introduces a hypergraph transformer method to model complex brain network correlations for improved disease diagnosis. The novel approach enhances accuracy in identifying brain diseases, offering new tools for clinical practice and future brain science research.

Keywords:

brain disease diagnosis brain network high-order correlation hypergraph computation transformer

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Last Updated: May 11, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

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Published on: June 26, 2013

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

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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

Area of Science:

Neuroscience
Medical Imaging
Artificial Intelligence

Background:

Functional and structural brain networks exhibit complex high-order correlations.
Accurate modeling and fusion of these networks are crucial for understanding brain function and diagnosing diseases.
Existing methods face challenges in capturing intricate relationships within multimodal brain imaging data.

Purpose of the Study:

To propose a novel hypergraph transformer method for modeling high-order correlations between functional and structural brain networks.
To address limitations in current approaches for multimodal brain imaging analysis.
To enhance the accuracy of brain disease diagnosis through advanced network modeling.

Main Methods:

Utilized hypergraphs to effectively capture high-order correlations within brain networks.
Employed a Transformer model for robust feature extraction and integration from multimodal brain imaging.
Developed a hypergraph transformer framework for unified analysis of functional and structural brain data.

Main Results:

The hypergraph transformer method demonstrated superior performance on the ABIDE and ADNI datasets.
Outperformed traditional and graph-based methods in diagnosing various brain diseases.
Experimental results confirmed the method's potential for clinical application.

Conclusions:

The proposed method offers new tools and insights for brain disease diagnosis.
Significantly improves diagnostic accuracy by understanding complex brain network relationships.
Lays a foundation for future advancements in brain science research and clinical practice.