Robust regression for large-scale neuroimaging studies

Neuroimage. 2015 May 1;111:431-41. doi: 10.1016/j.neuroimage.2015.02.048. Epub 2015 Feb 28.

Abstract

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. >100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain-behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies.

Keywords: Large cohorts; Neuroimaging genetics; Outliers; Robust regression; fMRI.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Computer Simulation
  • Data Interpretation, Statistical*
  • Functional Neuroimaging / methods
  • Functional Neuroimaging / standards
  • Humans
  • Neuroimaging / methods*
  • Neuroimaging / standards
  • Regression Analysis*
  • Sample Size
  • Sensitivity and Specificity