Multimodal and multi-tissue measures of connectivity revealed by joint independent component analysis

Alexandre R. Franco, Josef Ling, Arvind Caprihan, Vince D. Calhoun, Rex E. Jung, Gregory L. Heileman, Andrew R. Mayer

Research output: Contribution to journalArticlepeer-review

28 Scopus citations


The human brain functions as an efficient system where signals arising from gray matter are transported via white matter tracts to other regions of the brain to facilitate human behavior. However, with a few exceptions, functional and structural neuroimaging data are typically optimized to maximize the quantification of signals arising from a single source. For example, functional magnetic resonance imaging (FMRI) is typically used as an index of gray matter functioning whereas diffusion tensor imaging (DTI) is typically used to determine white matter properties. While it is likely that these signals arising from different tissue sources contain complementary information, the signal processing algorithms necessary for the fusion of neuroimaging data across imaging modalities are still in a nascent stage. In the current paper we present a data-driven method for combining measures of functional connectivity arising from gray matter sources (FMRI resting state data) with different measures of white matter connectivity (DTI). Specifically, a joint independent component analysis (J-ICA) was used to combine these measures of functional connectivity following intensive signal processing and feature extraction within each of the individual modalities. Our results indicate that one of the most predominantly used measures of functional connectivity (activity in the default mode network) is highly dependent on the integrity of white matter connections between the two hemispheres (corpus callosum) and within the cingulate bundles. Importantly, the discovery of this complex relationship of connectivity was entirely facilitated by the signal processing and fusion techniques presented herein and could not have been revealed through separate analyses of both data types as is typically performed in the majority of neuroimaging experiments. We conclude by discussing future applications of this technique to other areas of neuroimaging and examining potential limitations of the methods.

Original languageEnglish (US)
Pages (from-to)986-997
Number of pages12
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number6
StatePublished - 2008
Externally publishedYes


  • Brain mapping
  • Data fusion
  • Default mode network
  • Diffusion tensor imaging
  • Functional magnetic resonance imaging (FMRI)
  • Independent component analysis (ICA)
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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