title: Multinomial inference on distributed responses in SPM.
abstract: In this work, we propose statistical methods to perform inference on the spatial distribution of topological features (e.g. maxima or clusters) in statistical parametric maps (SPMs). This contrasts with local inference on the features per se (e.g., height or extent), which is well-studied (e.g. Friston et al., 1991, 1994; Worsley et al., 1992, 2003, 2004). We present a Bayesian approach to detecting experimentally-induced patterns of distributed responses in SPMs with anisotropic, non-stationary noise and arbitrary geometry. We extend the framework to accommodate fixed- and random-effects analyses at the within and between-subject levels respectively. We illustrate the method by characterising the anatomy of language at different scales of functional segregation.
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, UCL, UK. jrchum@gmail.com
authors.forename: J R
authors.initials: JR
authors.lastname: Chumbley
authors.forename: G
authors.initials: G
authors.lastname: Flandin
authors.forename: M L
authors.initials: ML
authors.lastname: Seghier
authors.forename: K J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D005071:Evoked Potentials / Q000502:physiology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007802:Language; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity