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IEEE J Sel Top Signal Process. 2016 Oct;10(7):1134-1149. doi: 10.1109/JSTSP.2016.2594945. Epub 2016 Jul 27.

Blind Source Separation for Unimodal and Multimodal Brain Networks: A Unifying Framework for Subspace Modeling.

Author information

1
Dept. of ECE at The University of New Mexico, NM USA.
2
The Mind Research Network, LBERI, Albuquerque, New Mexico USA.
3
Brainnetome Center & NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing China.
4
Calhoun are with the Dept. of ECE at The University of New Mexico, NM USA.
5
Dept. of CSEE, University of Maryland Baltimore County, Baltimore, Maryland USA.
6
Dept. of ECE at The University of New Mexico, NM USAThe Mind Research Network, LBERI, Albuquerque, New Mexico USA.

Abstract

In the past decade, numerous advances in the study of the human brain were fostered by successful applications of blind source separation (BSS) methods to a wide range of imaging modalities. The main focus has been on extracting "networks" represented as the underlying latent sources. While the broad success in learning latent representations from multiple datasets has promoted the wide presence of BSS in modern neuroscience, it also introduced a wide variety of objective functions, underlying graphical structures, and parameter constraints for each method. Such diversity, combined with a host of datatype-specific know-how, can cause a sense of disorder and confusion, hampering a practitioner's judgment and impeding further development. We organize the diverse landscape of BSS models by exposing its key features and combining them to establish a novel unifying view of the area. In the process, we unveil important connections among models according to their properties and subspace structures. Consequently, a high-level descriptive structure is exposed, ultimately helping practitioners select the right model for their applications. Equipped with that knowledge, we review the current state of BSS applications to neuroimaging. The gained insight into model connections elicits a broader sense of generalization, highlighting several directions for model development. In light of that, we discuss emerging multi-dataset multidimensional (MDM) models and summarize their benefits for the study of the healthy brain and disease-related changes.

KEYWORDS:

BSS; modeling; multimodality; multiset data analysis; neuroimaging; overview; subspace; unify; unimodal

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