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J Neurosci Methods. 2017 Apr 1;281:49-63. doi: 10.1016/j.jneumeth.2017.01.017. Epub 2017 Feb 16.

Adaptive independent vector analysis for multi-subject complex-valued fMRI data.

Author information

1
School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China.
2
School of Information and Communication Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China. Electronic address: qhlin@dlut.edu.cn.
3
Department of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, China; Department of Mathematical Information Technology, University of Jyvaskyla, Finland.
4
The Mind Research Network, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.

Abstract

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.

KEYWORDS:

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

PMID:
28214528
DOI:
10.1016/j.jneumeth.2017.01.017
[Indexed for MEDLINE]

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