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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

Variational Bayesian mixed-effects inference for classification studies.

Kay H Brodersen1, Jean Daunizeau, Christoph Mathys

  • 1Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Switzerland. brodersen@biomed.ee.ethz.ch

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Summary
This summary is machine-generated.

This study introduces an efficient hierarchical model for mixed-effects inference in neuroimaging classification. It offers a powerful and computationally faster alternative to existing methods for analyzing brain data across subjects.

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Area of Science:

  • Neuroimaging analysis
  • Machine learning in neuroscience
  • Statistical modeling for brain data

Background:

  • Multivariate classification algorithms are crucial for predicting cognitive or pathophysiological states from neuroimaging data.
  • Assessing classifier utility requires inference at both individual and population levels, necessitating models accounting for within-subject (fixed-effects) and between-subject (random-effects) variance.
  • Mixed-effects models are standard in mass-univariate fMRI but underexplored in multivariate classification due to computational costs.

Purpose of the Study:

  • To extend a hierarchical model for mixed-effects inference in multivariate classification studies of neuroimaging data.
  • To introduce an efficient variational Bayes approach for inference in these models.
  • To provide a widely applicable framework for robust classification group analyses.

Main Methods:

  • Development and extension of a hierarchical model for mixed-effects inference in multivariate classification.
  • Implementation of an efficient variational Bayes approach for computational tractability.
  • Validation using both synthetic and empirical functional magnetic resonance imaging (fMRI) data.

Main Results:

  • The proposed variational Bayes approach is computationally more efficient than previous sampling and permutation methods.
  • The method demonstrates equal simplicity of use compared to conventional t-tests on subject-specific accuracies.
  • The approach proves more powerful than conventional t-tests for assessing classification performance.

Conclusions:

  • The developed framework provides an efficient and powerful method for mixed-effects inference in multivariate neuroimaging classification.
  • This approach is classifier-independent, ensuring broad applicability across various machine learning techniques.
  • The study advocates for the adoption of this mixed-effects inference framework as a future standard for group-level classification analyses in neuroimaging.