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METHODS article

Front. Neuroinform.
Volume 18 - 2024 | doi: 10.3389/fninf.2024.1399391
This article is part of the Research Topic Emerging Trends in Large-Scale Data Analysis for Neuroscience Research View all 7 articles

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

Provisionally accepted
Michelle F. Miranda Michelle F. Miranda *
  • University of Victoria, Victoria, Canada

The final, formatted version of the article will be published soon.

    Task-evoked functional magnetic resonance imaging studies, such as the Human Connectome Project (HCP), are a powerful tool for exploring how brain activity is influenced by cognitive tasks like memory retention, decision-making, and language processing. A fast Bayesian function-onscalar model is proposed for estimating population-level activation maps linked to the working memory task. The model is based on the canonical polyadic (CP) tensor decomposition of coefficient maps obtained for each subject. This decomposition effectively yields a tensor basis capable of extracting both common features and subject-specific features from the coefficient maps. These subject-specific features, in turn, are modeled as a function of covariates of interest using a Bayesian model that accounts for the correlation of the CP-extracted features. The dimensionality reduction achieved with the tensor basis allows for a fast MCMC estimation of population-level activation maps. This model is applied to one hundred unrelated subjects from the HCP dataset, yielding significant insights into brain signatures associated with working memory.

    Keywords: Tensor decomposition, Neuroimaging, fMRI, CP decomposition, Bayesian modeling, Functional regression model

    Received: 11 Mar 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Miranda. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Michelle F. Miranda, University of Victoria, Victoria, Canada

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