Quantifying inter-subject agreement in brain-imaging analyses. Academic Article uri icon

Overview

abstract

  • In brain-imaging research, we are often interested in making quantitative claims about effects across subjects. Given that most imaging data consist of tens to thousands of spatially correlated time series, inter-subject comparisons are typically accomplished with simple combinations of inter-subject data, for example methods relying on group means. Further, these data are frequently taken from reduced channel subsets defined either a priori using anatomical considerations, or functionally using p-value thresholding to choose cluster boundaries. While such methods are effective for data reduction, means are sensitive to outliers, and current methods for subset selection can be somewhat arbitrary. Here, we introduce a novel "partial-ranking" approach to test for inter-subject agreement at the channel level. This non-parametric method effectively tests whether channel concordance is present across subjects, how many channels are necessary for maximum concordance, and which channels are responsible for this agreement. We validate the method on two previously published and two simulated EEG data sets.

publication date

  • August 23, 2007

Research

keywords

  • Algorithms
  • Brain
  • Electroencephalography
  • Image Processing, Computer-Assisted

Identity

Scopus Document Identifier

  • 37849022107

Digital Object Identifier (DOI)

  • 10.1016/j.neuroimage.2007.07.064

PubMed ID

  • 18023210

Additional Document Info

volume

  • 39

issue

  • 3