TY - JOUR
T1 - Multimodal and multi-tissue measures of connectivity revealed by joint independent component analysis
AU - Franco, Alexandre R.
AU - Ling, Josef
AU - Caprihan, Arvind
AU - Calhoun, Vince D.
AU - Jung, Rex E.
AU - Heileman, Gregory L.
AU - Mayer, Andrew R.
N1 - Funding Information:
Manuscript received March 15, 2008; revised August 03, 2008. Current version published January 23, 2009. This work was supported by The Mind Research Network—Mental Illness and Neuroscience Discovery DOE under Grants DE-FG02-99ER62764, NIBIB R01 EB005846, and NIBIB R01 EB006841. The Guest Editor coordinating the review of this manuscript and approving it for publication was Prof. Jane Wang.
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
KW - Brain mapping
KW - Data fusion
KW - Default mode network
KW - Diffusion tensor imaging
KW - Functional magnetic resonance imaging (FMRI)
KW - Independent component analysis (ICA)
KW - Magnetic resonance imaging
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U2 - 10.1109/JSTSP.2008.2006718
DO - 10.1109/JSTSP.2008.2006718
M3 - Article
AN - SCOPUS:60549113519
SN - 1932-4553
VL - 2
SP - 986
EP - 997
JO - IEEE Journal on Selected Topics in Signal Processing
JF - IEEE Journal on Selected Topics in Signal Processing
IS - 6
ER -