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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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A local group differences test for subject-level multivariate density neuroimaging outcomes.

Jordan D Dworkin1, Kristin A Linn1, Andrew J Solomon2

  • 1Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA.

Biostatistics (Oxford, England)
|December 27, 2019
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Summary

This study introduces multi-modal density testing (MMDT) to find subtle differences in brain scans for diseases like multiple sclerosis (MS). MMDT effectively identifies group variations in voxel intensity profiles, aiding in diagnosing complex neuropathologies.

Keywords:
High-dimensional dataMultivariate densitiesNeuroimaging

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Area of Science:

  • Neuroimaging
  • Radiology
  • Biostatistics

Background:

  • Neuroimaging research often uses voxel-wise analysis or tissue segmentation.
  • Many diseases exhibit diffuse neuropathology not captured by regional analysis.
  • Current methods struggle with unknown or complex patterns in voxel intensities across modalities.

Purpose of the Study:

  • To introduce a novel framework, multi-modal density testing (MMDT), for discovering group differences in voxel intensity profiles.
  • To address limitations of summary statistics in detecting diffuse neuropathology.
  • To enable naive discovery of group differences without prior knowledge of disease manifestation patterns.

Main Methods:

  • Operationalized multi-modal magnetic resonance imaging (MRI) data as multivariate subject-level densities.
  • Utilized kernel density estimation for a local two-sample test within density spaces.
  • Validated through simulations for type I error control and power.

Main Results:

  • Simulations confirmed MMDT controls type I error and recovers relevant differences.
  • The method maintained statistical power while controlling for family-wise error rate and false discovery rate.
  • Applied to multiple sclerosis (MS) versus mimic conditions, MMDT found significant voxel intensity profile differences in the thalamus.

Conclusions:

  • MMDT provides a robust framework for naive discovery of group differences in neuroimaging data.
  • The method is effective for detecting diffuse neuropathology characterized by complex intensity patterns.
  • MMDT identified distinct voxel intensity profiles in the thalamus between MS and mimic conditions, aiding differential diagnosis.