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Statistical parametric mapping of biomechanical one-dimensional data with Bayesian inference.

Ben Serrien1, Maggy Goossens2,3, Jean-Pierre Baeyens1,2,3

  • 1Faculty of Physical Education and Physiotherapy, Vrije Universiteit Brussel, Brussels, Belgium.

International Biomechanics
|May 27, 2021
PubMed
Summary

This study introduces Bayesian Statistical Parametric Mapping (SPM) for biomechanics, offering a new approach to analyzing continuum data like kinematic time series using Bayes factors and posterior probabilities.

Keywords:
Bayes FactorBayesian inferenceQ-valueStatistical Parametric Mappingfalse discovery rateposterior probabilitytime series

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Area of Science:

  • Biomechanics
  • Statistical analysis
  • Neuroimaging applications

Background:

  • Statistical Parametric Mapping (SPM) is increasingly used in biomechanics for continuum data.
  • The spm1d package facilitates frequentist SPM analyses in biomechanics.
  • A need exists for Bayesian alternatives to complement frequentist approaches.

Purpose of the Study:

  • To propose and demonstrate Bayesian analogs of SPM for biomechanical data.
  • To compare Bayesian SPM results with classical frequentist SPM.
  • To highlight the advantages of Bayesian inference in SPM for biomechanics.

Main Methods:

  • Application of Bayesian SPM using Bayes factors and posterior probability.
  • Utilized the BayesFactor package in R for Bayesian analyses.
  • Analyzed two-sample and paired-sample t-test designs.

Main Results:

  • Provided results for Bayesian SPM applied to common biomechanical designs.
  • Compared Bayesian results against classical SPM findings.
  • Demonstrated the feasibility of Bayesian SPM for kinematic time series.

Conclusions:

  • Bayesian SPM offers a valuable alternative for analyzing biomechanical continuum data.
  • Bayesian methods provide complementary insights to frequentist SPM.
  • The proposed Bayesian framework enhances statistical rigor in biomechanics.