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A canonical polyadic tensor basis for fast Bayesian estimation of multi-subject brain activation patterns.

Michelle F Miranda1

  • 1Department of Mathematics and Statistics, University of Victoria, Victoria, BC, Canada.

Frontiers in Neuroinformatics
|August 27, 2024
PubMed
Summary

This study introduces a fast Bayesian model using tensor decomposition to analyze brain activity during working memory tasks. The model efficiently estimates population-level brain activation maps from functional MRI data.

Keywords:
Bayesian modelingCP decompositionfMRIfunctional regression modelneuroimagingtensor decomposition

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Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Modeling

Background:

  • Functional magnetic resonance imaging (fMRI) studies, like the Human Connectome Project (HCP), investigate brain activity during cognitive tasks.
  • Understanding brain mechanisms underlying working memory is crucial for cognitive neuroscience.
  • Existing methods for analyzing population-level fMRI data can be computationally intensive.

Purpose of the Study:

  • To propose a fast Bayesian function-on-scalar model for estimating population-level brain activation maps.
  • To leverage canonical polyadic (CP) tensor decomposition for efficient analysis of subject-specific fMRI data.
  • To identify brain signatures associated with working memory using a novel statistical approach.

Main Methods:

  • A Bayesian function-on-scalar model based on CP tensor decomposition of subject-specific coefficient maps.
  • Modeling subject-specific features as a function of covariates, accounting for feature correlations.
  • Fast Markov Chain Monte Carlo (MCMC) estimation enabled by dimensionality reduction via tensor basis.

Main Results:

  • The proposed model was applied to 100 unrelated subjects from the HCP dataset.
  • The model successfully estimated population-level activation maps for the working memory task.
  • Significant brain signatures associated with working memory were identified.

Conclusions:

  • The developed model provides an efficient and effective method for analyzing task-evoked fMRI data.
  • The approach facilitates the identification of population-level brain activity patterns related to cognitive functions.
  • This work offers valuable insights into the neural basis of working memory.