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Multilayer Integration of Networks Toolbox (MINT).

Saman Sarraf1, Bárbara Avelar-Pereira2,3, S M Hadi Hosseini2

  • 1Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford University, Stanford, CA, USA. ssarraf@stanford.edu.

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Summary

This study introduces MINT, a Python toolbox for integrating multimodal Alzheimer's disease (AD) data. MINT effectively identifies distinct patient communities and predicts disease progression using neuroimaging and fluid biomarkers.

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Area of Science:

  • Computational biology
  • Neuroscience
  • Bioinformatics

Background:

  • Alzheimer's disease (AD) diagnosis relies on diverse data types, including neuroimaging, fluid biomarkers, and genetics.
  • Integrating multimodal data is crucial for understanding AD complexity and identifying at-risk individuals.
  • Existing analytical tools may not fully capture the intricate relationships within and across these data modalities.

Purpose of the Study:

  • To introduce the Multilayer Integration of Networks Toolbox (MINT), a Python package designed for multimodal data integration and community detection.
  • To evaluate MINT's ability to improve Alzheimer's disease prediction and identify preclinical cases by modeling intra- and inter-modality associations.
  • To demonstrate MINT's utility in uncovering complex relationships within heterogeneous and multifactorial disorders.

Main Methods:

  • Development of MINT, a Python package incorporating data standardization, Similarity Network Fusion, and Generalized Louvain clustering.
  • Application of MINT to two Alzheimer's disease (AD) cohorts (n=206 and n=143) using structural MRI, PET, CSF, cognition, and genetic data.
  • Optimization of modality selection and cross-validation to identify optimal data combinations for analysis.

Main Results:

  • MINT identified PET and CSF as the most informative modalities for Alzheimer's disease (AD) analysis.
  • Two distinct communities were detected: one AD-dominant and one cognitively normal-dominant (CN).
  • High sensitivity (84.38%) and specificity (92.65%) were achieved in classifying CN and AD groups. The AD-dominant community showed significantly higher AD pathology markers and poorer cognition. Notably, cognitively normal individuals within the AD-dominant community exhibited elevated amyloid and tau pathology compared to their counterparts in the CN-dominant group.

Conclusions:

  • MINT is a powerful tool for integrating multimodal data in complex diseases like Alzheimer's disease (AD).
  • The toolbox can identify biologically relevant subgroups and predict disease progression.
  • MINT facilitates the discovery of intricate relationships across heterogeneous data, aiding in the understanding of multifactorial disorders.