Open AccessEstimating the number of independent components for functional magnetic resonance imaging data
Author(s) -
Li YiOu,
Adalı Tülay,
Calhoun Vince D.
Publication year - 2007
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20359
Subject(s) - independent component analysis , functional magnetic resonance imaging , preprocessor , computer science , pattern recognition (psychology) , curse of dimensionality , artificial intelligence , principal component analysis , data set , selection (genetic algorithm) , smoothness , set (abstract data type) , data mining , mathematics , psychology , mathematical analysis , neuroscience , programming language
Multivariate analysis methods such as independent component analysis (ICA) have been applied to the analysis of functional magnetic resonance imaging (fMRI) data to study brain function. Because of the high dimensionality and high noise level of the fMRI data, order selection, i.e., estimation of the number of informative components, is critical to reduce over/underfitting in such methods. Dependence among fMRI data samples in the spatial and temporal domain limits the usefulness of the practical formulations of information‐theoretic criteria (ITC) for order selection, since they are based on likelihood of independent and identically distributed (i.i.d.) data samples. To address this issue, we propose a subsampling scheme to obtain a set of effectively i.i.d. samples from the dependent data samples and apply the ITC formulas to the effectively i.i.d. sample set for order selection. We apply the proposed method on the simulated data and show that it significantly improves the accuracy of order selection from dependent data. We also perform order selection on fMRI data from a visuomotor task and show that the proposed method alleviates the over‐estimation on the number of brain sources due to the intrinsic smoothness and the smooth preprocessing of fMRI data. We use the software package ICASSO (Himberg et al. [ 2004]: Neuroimage 22:1214–1222) to analyze the independent component (IC) estimates at different orders and show that, when ICA is performed at overestimated orders, the stability of the IC estimates decreases and the estimation of task related brain activations show degradation. Hum Brain Mapp, 2007. © 2007 Wiley‐Liss, Inc.