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A false discovery rate control method using a fully connected hidden Markov random field for neuroimaging data.

Taehyo Kim1, Qiran Jia2, Mony J de Leon3

  • 1Department of Biostatistics, School of Global Public Health, New York University, New York, NY, USA.

Medical Image Analysis
|June 13, 2026
PubMed
Summary
This summary is machine-generated.

We developed fcHMRF-LIS, a novel spatial method for neuroimaging analysis that accurately controls the false discovery rate (FDR) while improving power and computational efficiency.

Keywords:
False discovery rateFalse non-discovery rateFully connected hidden Markov random fieldMultiple testing

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Area of Science:

  • Neuroimaging analysis
  • Statistical genetics
  • Computational neuroscience

Background:

  • Voxel-wise multiple testing in neuroimaging requires robust false discovery rate (FDR) control.
  • Classical FDR methods struggle with spatial dependencies and high dimensionality in neuroimaging data.
  • Existing spatial FDR methods face challenges in capturing complex spatial structures, maintaining stability, and computational scalability.

Purpose of the Study:

  • To introduce fcHMRF-LIS, a novel spatial FDR control method for neuroimaging.
  • To address limitations of existing methods in handling spatial dependencies, variability, and computational demands.
  • To enhance statistical power and efficiency in detecting disease-related brain regions.

Main Methods:

  • Integration of the local index of significance (LIS) testing procedure with a fully connected hidden Markov random field (fcHMRF).
  • Development of an efficient expectation-maximization algorithm using mean-field approximation, CRF-RNN, and lattice filtering.
  • Reduction of computational time complexity from quadratic to linear relative to the number of tests.

Main Results:

  • fcHMRF-LIS demonstrates accurate FDR control and lower false non-discovery rates (FNR) in simulations.
  • The method exhibits reduced variability in false discovery proportion (FDP) and false non-discovery proportion (FNP).
  • Application to Alzheimer's Disease Neuroimaging Initiative data identified relevant brain regions with significant computational advantages.

Conclusions:

  • fcHMRF-LIS offers a powerful, stable, and scalable solution for voxel-wise multiple testing in neuroimaging.
  • The method effectively models complex spatial dependencies and improves statistical power.
  • fcHMRF-LIS provides significant computational benefits for high-resolution neuroimaging datasets.