title: Identifying FMRI model violations with Lagrange multiplier tests.
abstract: The standard modeling framework in functional magnetic resonance imaging (fMRI) is predicated on assumptions of linearity, time invariance and stationarity. These assumptions are rarely checked because doing so requires specialized software, although failure to do so can lead to bias and mistaken inference. Identifying model violations is an essential but largely neglected step in standard fMRI data analysis. Using Lagrange multiplier testing methods we have developed simple and efficient procedures for detecting model violations such as nonlinearity, nonstationarity and validity of the common double gamma specification for hemodynamic response. These procedures are computationally cheap and can easily be added to a conventional analysis. The test statistic is calculated at each voxel and displayed as a spatial anomaly map which shows regions where a model is violated. The methodology is illustrated with a large number of real data examples.
authors.affiliation: School of Electrical Engineering, University of New South Wales, Sydney 2052, Australia. b.cassidy@unsw.edu.au
authors.forename: Ben
authors.initials: B
authors.lastname: Cassidy
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Long
authors.forename: Caroline
authors.initials: C
authors.lastname: Rae
authors.forename: Victor
authors.initials: V
authors.lastname: Solo
mesh_terms: D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D003198:Computer Simulation; D006439:Hemodynamics / Q000502:physiology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D012372:ROC Curve; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio