Bootstrap enhanced penalized regression for variable selection with neuroimaging data

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

Original languageEnglish (US)
Article number344
JournalFrontiers in Neuroscience
Volume10
Issue numberJUL
DOIs
StatePublished - 2016

Keywords

  • Bootstrap
  • FMRI
  • Functional connectivity
  • Independent component analysis
  • Individual differences
  • Penalized regression

Fingerprint Dive into the research topics of 'Bootstrap enhanced penalized regression for variable selection with neuroimaging data'. Together they form a unique fingerprint.

Cite this