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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity

Wei Wang1, Li Xiao2, Gang Qu3

  • 1MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China, Hefei 230052, China.

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|March 22, 2024
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Summary
This summary is machine-generated.

This study introduces a new framework for analyzing brain connectivity in multisite fMRI data, improving autism spectrum disorder identification by accounting for multiple brain atlases and reducing site-specific biases.

Keywords:
AutismFunctional connectivityGraph convolution networksGraph embeddingHypergraph

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Brain Connectivity Analysis

Background:

  • Functional magnetic resonance imaging (fMRI) and functional connectivity networks (FCNs) show promise for brain disease diagnosis using graph convolutional networks (GCNs).
  • Multisite fMRI studies face challenges with multiview information and site influences, which remain understudied.
  • Existing methods often overlook high-order relationships within brain networks and the impact of multi-atlas constructions.

Purpose of the Study:

  • To propose a novel framework, Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL), for integrating multi-atlas FCNs in multisite fMRI studies.
  • To address the understudied issues of multiview information and site influences in FCN analysis.
  • To enhance the accuracy and interpretability of automated brain disease diagnosis, specifically for autism spectrum disorder (ASD).

Main Methods:

  • Modeling brain networks as hypergraphs for each brain atlas to capture high-order vertex relations.
  • Utilizing a multiview hyperedge-aware hypergraph convolutional network (HGCN) for adaptive learning of hyperedge weights.
  • Implementing class-consistency and site-independence modules to learn embeddings that are discriminative and free from site-specific variations.
  • Employing a softmax classifier for final diagnosis based on learned multi-atlas FCN embeddings.

Main Results:

  • The proposed CcSi-MHAHGEL framework effectively integrates multi-atlas FCNs from multisite fMRI data.
  • The method demonstrates significant effectiveness in identifying autism spectrum disorder (ASD) in experiments on the ABIDE dataset.
  • The framework provides interpretable results, highlighting biologically significant brain regions relevant to ASD.

Conclusions:

  • The CcSi-MHAHGEL framework offers a robust approach for analyzing complex brain connectivity in multisite fMRI studies.
  • This method enhances diagnostic accuracy for neurological disorders like ASD by mitigating site-specific biases and leveraging multiview information.
  • The interpretability of the model aids in understanding the neurobiological underpinnings of ASD.