title: Order Selection of the Linear Mixing Model for Complex-valued FMRI Data.
abstract: Functional magnetic resonance imaging (fMRI) data are originally acquired as complex-valued images, which motivates the use of complex-valued data analysis methods. Due to the high dimension and high noise level of fMRI data, order selection and dimension reduction are important procedures for multivariate analysis methods such as independent component analysis (ICA). In this work, we develop a complex-valued order selection method to estimate the dimension of signal subspace using information-theoretic criteria. To correct the effect of sample dependence to information-theoretic criteria, we develop a general entropy rate measure for complex Gaussian random process to calibrate the independent and identically distributed (i.i.d.) sampling scheme in the complex domain. We show the effectiveness of the approach for order selection on both simulated and actual fMRI data. A comparison between the results of order selection and ICA on real-valued and complex-valued fMRI data demonstrates that a fully complex analysis extracts more information about brain activation.
authors.affiliation: University of Maryland Baltimore County, Baltimore, MD 21250, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Xiong
authors.forename: Yi-Ou
authors.initials: YO
authors.lastname: Li
authors.forename: Nicolle
authors.initials: N
authors.lastname: Correa
authors.forename: Tülay
authors.initials: T
authors.lastname: Adalı
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
keywords: Order selection; complex-valued fMRI; entropy rate; i.i.d. sampling; linear mixing model