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A novel node-level structure embedding and alignment representation of structural networks for brain disease

Jiashuang Huang1, Mingliang Wang1, Xijia Xu2

  • 1College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing 210029, China.

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This study introduces a novel node-level structure embedding and alignment (nSEA) method to capture subtle brain network changes for improved disease analysis and classification.

Keywords:
Brain networkGroup-level analysisNode vector alignmentSchizophreniaStructural embedding

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Area of Science:

  • Neuroscience
  • Network Science
  • Medical Imaging Analysis

Background:

  • Brain network analysis using neuroimaging (DTI, fMRI) is crucial for understanding brain diseases.
  • Existing node-level measures capture specific structural information but miss subtle changes.
  • Improved methods are needed to enhance the accuracy of brain disease analysis.

Purpose of the Study:

  • To develop a novel node-level structure embedding and alignment (nSEA) representation for brain networks.
  • To create an nSEA representation-based learning (nSEAL) framework for brain disease analysis.
  • To improve the identification of disease-related brain regions and enhance classification performance.

Main Methods:

  • Defined a novel node-level structure embedding and alignment (nSEA) representation.
  • Developed an nSEA representation-based learning (nSEAL) framework.
  • Applied structural embedding and alignment for statistical analysis and brain disease classification.

Main Results:

  • The nSEA method captures richer structural information, including small changes, compared to existing measures.
  • The nSEAL framework successfully identified disease-related brain regions in a schizophrenia dataset.
  • The proposed method demonstrated improved brain disease classification performance over state-of-the-art techniques.

Conclusions:

  • The nSEA representation offers a more comprehensive characterization of node-level brain network structures.
  • The nSEAL framework provides a powerful tool for brain disease analysis and understanding pathology.
  • This approach enhances diagnostic capabilities and contributes to the study of neurological disorders.