Wavelet-domain regression and predictive inference in psychiatric neuroimaging

Philip T. Reiss, Lan Huo, Yihong Zhao, Clare Kelly, R. Todd Ogden

Research output: Contribution to journalArticlepeer-review


An increasingly important goal of psychiatry is the use of brain imaging data to develop predictive models. Here we present two contributions to statistical methodology for this purpose. First, we propose and compare a set of wavelet-domain procedures for fitting generalized linear models with scalar responses and image predictors: sparse variants of principal component regression and of partial least squares, and the elastic net. Second, we consider assessing the contribution of image predictors over and above available scalar predictors, in particular, via permutation tests and an extension of the idea of confounding to the case of functional or image predictors. Using the proposed methods, we assess whether maps of a spontaneous brain activity measure, derived from functional magnetic resonance imaging, can meaningfully predict presence or absence of attention deficit/hyperactivity disorder (ADHD). Our results shed light on the role of confounding in the surprising outcome of the recent ADHD-200 Global Competition, which challenged researchers to develop algorithms for automated image-based diagnosis of the disorder.

Original languageEnglish
Pages (from-to)1076-1101
Number of pages26
JournalAnnals of Applied Statistics
Issue number2
StatePublished - 1 Jun 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© Institute of Mathematical Statistics, 2015.


  • ADHD-200
  • Elastic net
  • Functional confounding
  • Functional regression
  • Functionalmagnetic resonance imaging
  • Sparse partial least squares
  • Sparse principal component regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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