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Overlapping brain Community detection using Bayesian tensor decomposition.

S Mirzaei1, H Soltanian-Zadeh2

  • 1School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran.

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Summary
This summary is machine-generated.

Non-negative Tensor Factorization (NTF) identifies overlapping brain communities from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data. This method accurately models individual brain networks, improving diagnostic potential for neuropsychiatric illnesses.

Keywords:
Bayesian tensor factorizationOverlapping community detectionrs-fMRI

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Area of Science:

  • Neuroscience
  • Data Science
  • Medical Imaging

Background:

  • Brain regions exhibit specialized functions, forming complex functional networks.
  • Understanding these networks is crucial for brain functionality and diagnosing neuropsychiatric disorders.
  • Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is a key tool for mapping brain activity.

Purpose of the Study:

  • To introduce Non-negative Tensor Factorization (NTF) for identifying overlapping communities within brain networks.
  • To leverage individual subject connectivity data for more precise network analysis.
  • To enhance the understanding of brain functionality and aid in diagnosing neuropsychiatric conditions.

Main Methods:

  • Utilized Non-negative Tensor Factorization (NTF) on individual subject rs-fMRI connectivity matrices.
  • Constructed tensor data preserving subject-specific information, unlike group-averaged approaches.
  • Applied NTF to both simulated and real Human Connectome Project (HCP) datasets.

Main Results:

  • Decomposed factors revealed community membership probabilities and inter-subject variability.
  • NTF effectively modeled community strengths across individual subjects.
  • Evaluations demonstrated high reproducibility and test-retest reliability (ICC).

Conclusions:

  • The proposed NTF-based framework accurately identifies overlapping communities in brain networks.
  • Preserving individual subject data through tensor factorization enhances network analysis.
  • The method shows promise for stable and accurate brain network characterization in neuroscience and clinical applications.