Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control

• Published in: NeuroImage (Orlando, Fla.) Vol. 65; pp. 69 - 82
• Main Authors: Stelzer, Johannes, Chen, Yi, Turner, Robert
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 15.01.2013; Elsevier; Elsevier Limited
• Subjects: Binomial distribution; Biological and medical sciences; Brain Mapping - methods; Classification; Cluster size control; fMRI; Fundamental and applied biological sciences. Psychology; Humans; Hypotheses; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Methods; Models, Neurological; Models, Theoretical; Multiple testing; MVPA; Probability distribution; Second level analysis; Sensitivity and Specificity; Software; Statistics; Vertebrates: nervous system and sense organs; Second level analysis; Cluster size control; fMRI; Multiple testing; Statistics; MVPA; Nuclear magnetic resonance imaging; Functional imaging
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2012.09.063

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate frameworks. How...