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Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment.

Lu Zhang1, Li Wang2, Jean Gao1

  • 1Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019 USA.

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|May 18, 2021
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Summary

This study introduces a novel Deep Brain Connectome approach using graph neural networks to fuse brain structure and function data. This method effectively differentiates Mild Cognitive Impairment patients, achieving high classification accuracy.

Keywords:
Brain structure and function fusionMCIgraph-based deep learning

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Multimodal neuroimaging data offers complementary insights not available in single modalities.
  • Deep learning models excel at uncovering complex, non-linear relationships in data.
  • Existing deep models often struggle with non-Euclidean data like brain connectivity.

Purpose of the Study:

  • To develop a novel deep learning framework for multimodal neuroimaging data fusion.
  • To integrate neuroscience knowledge into a unified deep learning model.
  • To effectively model and differentiate brain structure and function in Mild Cognitive Impairment (MCI).

Main Methods:

  • Developed a graph-based deep neural network for simultaneous modeling of brain structure and function.
  • Initialized graph topology using diffusion MRI (structural network).
  • Iteratively updated graph incorporating functional MRI (functional network) to enhance MCI classification.

Main Results:

  • Created a 'Deep Brain Connectome' by exploring deep structure-function relationships in MCI.
  • The Deep Brain Connectome revealed consistent group-level alterations compared to structural networks.
  • The deep model achieved 92.7% classification accuracy on the ADNI dataset for MCI detection.

Conclusions:

  • The proposed graph-based deep learning approach effectively fuses multimodal neuroimaging data.
  • The Deep Brain Connectome provides a powerful tool for understanding brain alterations in MCI.
  • This method demonstrates significant potential for clinical diagnosis and research in neurodegenerative diseases.