Premium
ROC methods for evaluation of fMRI techniques
Author(s) -
Sorenson James A.,
Wang X.
Publication year - 1996
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910360512
Subject(s) - receiver operating characteristic , computer science , set (abstract data type) , artificial intelligence , pattern recognition (psychology) , data set , machine learning , programming language
Receiver operating characteristic (ROC) methods provide a standardized and statistically meaningful means for comparing signal‐detection accuracy. A brief overview of ROC methods is presented. Example applications include a comparison of four different postprocessing algorithms operating on simulated fMRI time‐course data sets and on human null data sets to which a simulated fMR response had been added. ROC methods also were used to reanalyze one data set from a previously published work. Additional ROC methods that also may be useful for fMRI comparisons are described.