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Unpublished Paper
Permutation-based inference for spatially localized signals in longitudinal MRI data
(2020)
  • Jun Young Park, University of Minnesota
  • Mark Fiecas, University of Minnesota
Abstract
Alzheimer's disease is a neurodegenerative disease in which the degree of cortical atrophy in specific structures of the brain serves as a useful imaging biomarker. Successful identification of brain regions showing decay rate differences between patients with Alzheimer's disease and healthy controls would contribute to the treatment of the disease. However, massive-univariate analysis, a simplified approach that obtains a summary statistic (e.g., a $p$-value) for every vertex along the cortex, is insufficient to model cortical atrophy because it does not account for the spatial correlation of cortical thickness from structural magnetic resonance imaging (sMRI) and/or similarities of the signals in nearby locations.  In this article, we develop a permutation-based inference procedure to detect spatial {\it clusters} of vertices showing statistically significant differences in the rates of cortical atrophy. The proposed method uses spatial information to combine the signals adaptively across nearby vertices, yielding high statistical power while controlling family-wise error rate (FWER) accurately. When the global null hypothesis is rejected, we use a cluster selection algorithm to identify the spatial clusters of significant vertices. We validate our method using simulation studies and apply it to the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to show its superior performance over existing methods.
Keywords
  • Alzheimer's disease,
  • cortical atrophy,
  • permutation,
  • generalized estimating equation,
  • spatially localized signals
Publication Date
2020
Citation Information
Jun Young Park and Mark Fiecas. "Permutation-based inference for spatially localized signals in longitudinal MRI data" (2020)
Available at: http://works.bepress.com/mfiecas/22/