Interpretable High-Dimensional Inference Via Score Projection With an Application in Neuroimaging

Simon N. Vandekar, Philip T. Reiss, Russell T. Shinohara

Research output: Contribution to journalArticlepeer-review


In the fields of neuroimaging and genetics, a key goal is testing the association of a single outcome with a very high-dimensional imaging or genetic variable. Often, summary measures of the high-dimensional variable are created to sequentially test and localize the association with the outcome. In some cases, the associations between the outcome and summary measures are significant, but subsequent tests used to localize differences are underpowered and do not identify regions associated with the outcome. Here, we propose a generalization of Rao’s score test based on projecting the score statistic onto a linear subspace of a high-dimensional parameter space. The approach provides a way to localize signal in the high-dimensional space by projecting the scores to the subspace where the score test was performed. This allows for inference in the high-dimensional space to be performed on the same degrees of freedom as the score test, effectively reducing the number of comparisons. Simulation results demonstrate the test has competitive power relative to others commonly used. We illustrate the method by analyzing a subset of the Alzheimer’s Disease Neuroimaging Initiative dataset. Results suggest cortical thinning of the frontal and temporal lobes may be a useful biological marker of Alzheimer’s disease risk. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)820-830
Number of pages11
JournalJournal of the American Statistical Association
Issue number526
StatePublished - 3 Apr 2019

Bibliographical note

Publisher Copyright:
© 2018, © 2018 American Statistical Association.


  • Alzheimer’s disease
  • Association test
  • Cortical thickness
  • Neuroimaging
  • Post hoc inference
  • Score test

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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