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论文类型：期刊论文

发表时间：2017-04-01

发表刊物：JOURNAL OF NEUROSCIENCE METHODS

收录刊物：SCIE、PubMed、Scopus

卷号：281

页面范围：49-63

ISSN号：0165-0270

关键字：Independent vector analysis (IVA); Complex-valued fMRI data; MGGD; Shape parameter; Subspace de-noising; Post-IVA phase de-noising; Noncircularity

摘要：Background: Complex-valued fMRI data can provide additional insights beyond magnitude-only data. However, independent vector analysis (IVA), which has exhibited great potential for group analysis of magnitude-only fMRI data, has rarely been applied to complex-valued fMRI data. The main challenges in this application include the extremely noisy nature and large variability of the source component vector (SCV) distribution.
   New method: To address these challenges, we propose an adaptive fixed-point IVA algorithm for analyzing multiple-subject complex-valued fMRI data. We exploited a multivariate generalized Gaussian distribution (MGGD)-based nonlinear function to match varying SCV distributions in which the MGGD shape parameter was estimated using maximum likelihood estimation. To achieve our de-noising goal, we updated the MGGD-based nonlinearity in the dominant SCV subspace, and employed a post-IVA de-noising strategy based on phase information in the IVA estimates. We also incorporated the pseudo covariance matrix of fMRI data into the algorithm to emphasize the noncircularity of complex-valued fMRI sources.
   Results: Results from simulated and experimental fMRI data demonstrated the efficacy of our method. Comparison with existing method(s): Our approach exhibited significant improvements over typical complex-valued IVA algorithms, especially during higher noise levels and larger spatial and temporal changes. As expected, the proposed complex-valued IVA algorithm detected more contiguous and reasonable activations than the magnitude-only method for task-related (393%) and default mode (301%) spatial maps.
   Conclusions: The proposed approach is suitable for decomposing multi-subject complex-valued fMRI data, and has great potential for capturing additional subject variability. (C) 2017 Elsevier B.V. All rights reserved.