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Enhanced neuroimaging genetics using multi-view non-negative matrix factorization with sparsity and prior knowledge.

Ji Hye Won1, Jinyoung Youn2, Hyunjin Park3

  • 1Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea; Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.

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Summary

This study introduces a new imaging genetics method using non-negative matrix factorization (NMF) for neurodegenerative diseases. The approach enhances understanding of genetic and brain imaging links in Parkinson's disease.

Keywords:
InterpretabilityMultimodal integrationNeuroimaging geneticsNon-negative matrix factorizationParkinson's diseasesPrior knowledgeTopic modeling

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Area of Science:

  • Neuroscience
  • Genetics
  • Medical Imaging

Background:

  • Neuroimaging genetics combines genetic data with brain imaging for studying neurodegenerative diseases.
  • Conventional methods struggle with multimodal data integration and interpretability.

Purpose of the Study:

  • To develop a novel imaging genetics approach using non-negative matrix factorization (NMF).
  • To improve multimodal data integration and interpretability in imaging genetics studies.
  • To identify biologically relevant genetic and imaging features associated with neurodegenerative diseases.

Main Methods:

  • Proposed a novel imaging genetics approach based on non-negative matrix factorization (NMF).
  • Incorporated multi-view NMF with sparsity constraints and prior information for feature selection.
  • Applied the algorithm to simulated and real imaging genetics datasets of Parkinson's disease (PD).

Main Results:

  • The NMF-based algorithm identified important associated features more robustly than conventional methods.
  • The approach revealed interpretable topics linking single-nucleotide polymorphisms and brain regions to PD clinical scores.
  • Demonstrated improved multimodal integration and interpretability in imaging genetics analysis.

Conclusions:

  • The proposed NMF approach offers enhanced capabilities for imaging genetics research.
  • This method can uncover novel associations between genetic and neuroimaging features in neurodegenerative diseases.
  • The findings contribute to a better understanding of the genetic underpinnings of Parkinson's disease.