Cortical surface based identification of brain networks using high spatial resolution resting state FMRI data

Authors:
Kaiming Li;Lei Guo;Gang Li;Jingxin Nie;Carlos Faraco;Qun Zhao;L. Stephen Miller;Tianming Liu
Affiliations:
School of Automation, Northwestern Polytechnic University, Xi'an, China and Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA;School of Automation, Northwestern Polytechnic University, Xi'an, China;School of Automation, Northwestern Polytechnic University, Xi'an, China;School of Automation, Northwestern Polytechnic University, Xi'an, China;Department of Psychology and Bioimaging Research Center, The University of Georgia, Athens, GA;Department of Physics and Bioimaging Research Center, The University of Georgia, Athens, GA;Department of Psychology and Bioimaging Research Center, The University of Georgia, Athens, GA;Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Resting state fMRI (rsfMRI) has been demonstrated to be an effective modality by which to explore the functional networks of the human brain, as the low-frequency OSCIllatIons in rsfMRI time courses between spatially distant brain regions show the evidence of correlated activity patterns in the brain. This paper proposes a novel surface-based data-driven framework to explore these networks through the use of high resolution rsfMRI data. Guided by DTI defined fiber pathways and constrained by the gray matter, we map the rsfMRI BOLD signals onto the cortical surface generated by DTI-based tissue segmentation. We then use a data-driven affinity propagation clustering algorithm to identify these functional networks. Our experimental results demonstrate that the framework has high reproducibility and that several networks are detected reliably among individual subjects. Furthermore, our results exhibit that functional networks are highly correlated with structural connections. Finally, our framework is able to reveal visual sub-networks, indicating its potential role in sub-network exploration.