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

Rogers F. Silva, Sergey M. Plis, Jing Sui, Marios S. Pattichis, Tulay Adali, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


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 multidataset multidimensional models and summarize their benefits for the study of the healthy brain and disease-related changes.

Original languageEnglish (US)
Article number7523915
Pages (from-to)1134-1149
Number of pages16
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number7
StatePublished - Oct 2016


  • Blind source separation (BSS)
  • modeling
  • multimodality
  • multiset data analysis
  • neuroimaging
  • overview
  • subspace
  • unify
  • unimodal

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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