Addressing the "problem" of temporal correlations in MVPA analysis

Roee Gilron, Jonathan D. Rosenblatt, Roy Mukamel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The use of multivariate pattern analysis (MVPA) has grown substantially over the past few years. Many studies using MVPA estimate the response of individual trial activity and perform hypothesis testing using a non-parametric approach. Here we show that the default auto regression model of order 1 used for temporal whitening of BOLD data is problematic in that it leads to biased permutation tests. We show that the correlation of activity estimates across trials can cause extreme bias in non-parametric hypothesis testing so that the proportion of type I or type II errors are inflated. Crucially for MVPA, this inflation increases with sphere size. The error magnitude is such that in our data set, strong univariate effects are completely missed. By whitening the data with a more general auto regression (AR) model, one can correct the bias in permutation testing for better signal detection. Applying higher order AR models is already implemented in many neuroimaging software packages as a non-default option. The use of more aggressive temporal whitening may also prove crucial for valid MVPA inference in fast event related designs.

Original languageEnglish
Title of host publicationPRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)9781467365307
DOIs
StatePublished - 24 Aug 2016
Event6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016 - Trento, Italy
Duration: 22 Jun 201624 Jun 2016

Publication series

NamePRNI 2016 - 6th International Workshop on Pattern Recognition in Neuroimaging

Conference

Conference6th International Workshop on Pattern Recognition in Neuroimaging, PRNI 2016
Country/TerritoryItaly
CityTrento
Period22/06/1624/06/16

Keywords

  • Localization
  • MVPA
  • fMRI
  • testing

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Biomedical Engineering

Fingerprint

Dive into the research topics of 'Addressing the "problem" of temporal correlations in MVPA analysis'. Together they form a unique fingerprint.

Cite this