Quantifying inter-subject agreement in brain-imaging analyses.
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.
Wong
Dik Kin
DK
Center for the Study of Language and Information, Ventura Hall, 200 Panama St., Stanford University, CA, USA. dkwong@stanford.edu
Grosenick
Logan
L
Uy
E Timothy
ET
Perreau Guimaraes
Marcos
M
Carvalhaes
Claudio G
CG
Desain
Peter
P
Suppes
Patrick
P
D000465:Algorithms; D000704:Analysis of Variance; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D001931:Brain Mapping; D003198:Computer Simulation; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008953:Models, Anatomic