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Robust spatial extent inference with a semiparametric bootstrap joint inference procedure.

Simon N Vandekar1, Theodore D Satterthwaite2, Cedric H Xia2

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Summary
This summary is machine-generated.

New spatial extent inference (SEI) methods, (semi-)parametric bootstrap joint (PBJ; sPBJ) procedures, offer robust control of error rates in neuroimaging studies. These methods improve upon existing techniques for brain-phenotype association analysis.

Keywords:
FWERSpatial extent inferencebootstrapneuroimagingsemiparametric inference

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Area of Science:

  • Neuroimaging analysis
  • Statistical inference in neurobiology
  • Brain-phenotype association studies

Background:

  • Spatial extent inference (SEI) is crucial for neuroimaging studies, particularly for adjusting multiple comparisons in brain-phenotype associations.
  • Existing Gaussian random field (GRF)-based SEI methods may inflate family-wise error rates (FWERs) due to unrealistic spatial covariance assumptions.
  • Permutation procedures, while robust, can fail when the assumption of exchangeability is violated in neuroimaging data.

Purpose of the Study:

  • To propose novel (semi-)parametric bootstrap joint (PBJ; sPBJ) testing procedures for SEI of multilevel neuroimaging data.
  • To address limitations of GRF and permutation methods in controlling FWER and handling violated assumptions.
  • To provide a reliable statistical framework for analyzing brain-phenotype associations.

Main Methods:

  • Development of (semi-)parametric bootstrap joint (PBJ; sPBJ) testing procedures for SEI.
  • Utilizing robust spatial covariance function estimation for consistent standard error estimates, even with model misspecification.
  • Application of PBJ and sPBJ methods to a working memory functional magnetic resonance imaging (fMRI) study examining performance and executive functioning.

Main Results:

  • The sPBJ procedure demonstrates robust control of the nominal type 1 error rate across various sample sizes.
  • sPBJ exhibits comparable or superior statistical power to existing PBJ and permutation procedures.
  • The proposed methods were successfully applied to fMRI data, revealing associations between performance and executive functioning.

Conclusions:

  • The (semi-)parametric bootstrap joint (PBJ; sPBJ) procedures offer a statistically sound and powerful approach for spatial extent inference in neuroimaging.
  • These methods provide a reliable alternative to GRF and permutation tests, particularly when assumptions are potentially violated.
  • An R package is available to facilitate the implementation of PBJ and sPBJ procedures in neuroimaging research.