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Spatially adaptive variable screening in presurgical functional magnetic resonance imaging data analysis

  • 0Department of Statistics, North Carolina State University, Raleigh, NC 27695, United States.
Biometrics +

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January 2, 2025

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January 2, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

A new Bayesian missed discovery rate (BMDR) method improves presurgical functional magnetic resonance imaging (fMRI) analysis. This approach better identifies critical brain regions, reducing risks during tumor surgery and preserving patient function.

Area Of Science

  • Neuroimaging
  • Neurosurgery
  • Statistical analysis

Background

  • Accurate mapping of functional brain regions near tumors is crucial for safe neurosurgery.
  • Presurgical functional magnetic resonance imaging (fMRI) is vital for planning and patient counseling.
  • False negatives in fMRI analysis pose a greater risk than false positives, potentially leading to severe patient harm.

Purpose Of The Study

  • To introduce a novel metric, the Bayesian missed discovery rate (BMDR), for controlling false negatives in voxel-specific mixture models.
  • To develop a new data-driven variable screening procedure for fMRI analysis that leverages spatial information and controls false negatives.
  • To enhance the accuracy of functional brain region delineation for improved neurosurgical planning.

Main Methods

  • Development of the Bayesian missed discovery rate (BMDR) metric.
  • Proposal of a novel variable screening procedure incorporating spatial information from fMRI data.
  • Utilizing voxel-specific mixture models for statistical analysis.

Main Results

  • The new procedure effectively controls false negatives at a desired level.
  • The method capitalizes on the spatial structure of fMRI data for improved accuracy.
  • Numerical examples show superior performance compared to state-of-the-art methods in retaining subtle signal voxels and separating functional regions from noise.

Conclusions

  • The proposed BMDR-based method offers a significant advancement in presurgical fMRI analysis.
  • This approach enhances the identification of critical brain regions, thereby improving the safety and efficacy of function-preserving neurosurgery.
  • The data-driven and spatially informed nature of the method provides a cleaner separation of functional areas, crucial for surgical planning.

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