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<p>Sonification, the process of translating data into sound, has recently gained traction as a tool for both disseminating scientific findings and enabling visually impaired individuals to analyze data. Despite its potential, most current sonification methods remain limited to one-dimensional data, primarily due to the absence of practical, quantitative, and robust techniques for handling multi-dimensional datasets. We analyze structural magnetic resonance imaging (MRI) data of the human brain by integrating two- and three-point statistical measures in Fourier space: the power spectrum and bispectrum. These quantify the spatial correlations of three-dimensional voxel intensity distributions, yielding reduced bispectra that capture higher-order interactions. To illustrate the potential of the approach, we focus on one representative reduced-bispectrum configuration (<Emphasis FontCategory="NonProportional">Q019036</Emphasis>) in the <Emphasis FontCategory="NonProportional">OASIS-3</Emphasis> dataset (864 imaging sessions) and provide audio renderings for five age groups (40–50, 50–60, 60–70, 70–80, 80–100 years). As context, prior work with this configuration reported an age-prediction MAE of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\approx\)</EquationSource> </InlineEquation>4.2 years based on neural networks, trained on bispectrum-derived features–not through sonification itself. But here we emphasize sonification rather than predictive benchmarking. Finally, we treat these audio examples as exploratory illustrations rather than a perceptual evaluation. Our results demonstrate that the information loss (e.g., normalized mean squared error) during the reconstruction of the original bispectra, specifically in configurations sensitive to brain aging, from the sonified signal is minimal. Future studies should include systematic statistical inference and perceptual validation to assess the robustness and perceptual utility of the method. Nevertheless, the approach presented here already provides a general framework for encoding multi-dimensional data into time-series-like arrays suitable for sonification, thereby opening new avenues for scientific exploration and improving accessibility for a broader audience.</p>

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Higher-order sonification of the human brain

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Francisco-Shu Kitaura,
  • Emi-Pauline Kitaura,
  • Niels Janssen,
  • Antonella Maselli,
  • Ernesto Pereda,
  • Aurelio Carnero Rosell

摘要

Sonification, the process of translating data into sound, has recently gained traction as a tool for both disseminating scientific findings and enabling visually impaired individuals to analyze data. Despite its potential, most current sonification methods remain limited to one-dimensional data, primarily due to the absence of practical, quantitative, and robust techniques for handling multi-dimensional datasets. We analyze structural magnetic resonance imaging (MRI) data of the human brain by integrating two- and three-point statistical measures in Fourier space: the power spectrum and bispectrum. These quantify the spatial correlations of three-dimensional voxel intensity distributions, yielding reduced bispectra that capture higher-order interactions. To illustrate the potential of the approach, we focus on one representative reduced-bispectrum configuration (Q019036) in the OASIS-3 dataset (864 imaging sessions) and provide audio renderings for five age groups (40–50, 50–60, 60–70, 70–80, 80–100 years). As context, prior work with this configuration reported an age-prediction MAE of \(\approx\) 4.2 years based on neural networks, trained on bispectrum-derived features–not through sonification itself. But here we emphasize sonification rather than predictive benchmarking. Finally, we treat these audio examples as exploratory illustrations rather than a perceptual evaluation. Our results demonstrate that the information loss (e.g., normalized mean squared error) during the reconstruction of the original bispectra, specifically in configurations sensitive to brain aging, from the sonified signal is minimal. Future studies should include systematic statistical inference and perceptual validation to assess the robustness and perceptual utility of the method. Nevertheless, the approach presented here already provides a general framework for encoding multi-dimensional data into time-series-like arrays suitable for sonification, thereby opening new avenues for scientific exploration and improving accessibility for a broader audience.