title: Theoretical, statistical, and practical perspectives on pattern-based classification approaches to the analysis of functional neuroimaging data.
abstract: The goal of pattern-based classification of functional neuroimaging data is to link individual brain activation patterns to the experimental conditions experienced during the scans. These "brain-reading" analyses advance functional neuroimaging on three fronts. From a technical standpoint, pattern-based classifiers overcome fatal f laws in the status quo inferential and exploratory multivariate approaches by combining pattern-based analyses with a direct link to experimental variables. In theoretical terms, the results that emerge from pattern-based classifiers can offer insight into the nature of neural representations. This shifts the emphasis in functional neuroimaging studies away from localizing brain activity toward understanding how patterns of brain activity encode information. From a practical point of view, pattern-based classifiers are already well established and understood in many areas of cognitive science. These tools are familiar to many researchers and provide a quantitatively sound and qualitatively satisfying answer to most questions addressed in functional neuroimaging studies. Here, we examine the theoretical, statistical, and practical underpinnings of pattern-based classification approaches to functional neuroimaging analyses. Pattern-based classification analyses are well positioned to become the standard approach to analyzing functional neuroimaging data.
authors.affiliation: The University of Texas at Dallas, School of Behavioral and Brain Sciences, USA. otoole@utdallas.edu
authors.forename: Alice J
authors.initials: AJ
authors.lastname: O'Toole
authors.forename: Fang
authors.initials: F
authors.lastname: Jiang
authors.forename: Hervé
authors.initials: H
authors.lastname: Abdi
authors.forename: Nils
authors.initials: N
authors.lastname: Pénard
authors.forename: Joseph P
authors.initials: JP
authors.lastname: Dunlop
authors.forename: Marc A
authors.initials: MA
authors.lastname: Parent
mesh_terms: D001931:Brain Mapping / Q000295:instrumentation*; D006801:Humans; D008432:Mathematical Computing; D008959:Models, Neurological; D009482:Neurophysiology / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011582:Psychological Theory; D012815:Signal Processing, Computer-Assisted