title: Estimation of the intrinsic dimensionality of fMRI data.
abstract: A new method based on an autoregressive noise model of order 1 is introduced to the problem of detecting the number of components in fMRI data. Unlike current information-theoretic criteria like AIC, MDL, and related PPCA, which do not incorporate autocorrelations in the noise, the new method leads to more consistent estimates of the model order, as illustrated in simulated and real fMRI resting-state data.
authors.affiliation: Department of Radiology, University of Washington, 1959 Pacific Avenue, HSB AA048, Box #357115, Seattle, WA 98195, USA. dcordes@u.washington.edu
authors.forename: Dietmar
authors.initials: D
authors.lastname: Cordes
authors.forename: Rajesh R
authors.initials: RR
authors.lastname: Nandy
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007257:Information Theory; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical