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Proceedings Paper

Partial correlation mapping of brain functional connectivity with resting state fMRI
Author(s): Zonglei Zhen; Jie Tian; Wei Qin; Hui Zhang
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Paper Abstract

The methods to detect resting state functional connectivity presented so far mainly focus on Pearson correlation analysis which calculates Pearson Product Moment correlation coefficient between the time series of two distinct voxels or regions to measure the functional dependency between them. Due to artifacts and noises in the data, functional connectivity maps resulting from the Pearson correlation analysis may risk arising from the correlation of interfering signals other than the neural sources. In the paper, partial correlation analysis is proposed to map resting state functional connectivity. By eliminating of the contributions of interfering signals to pairwise correlations between different voxels or regions, partial correlation analysis allows us to measure the real functional connectivity induced by neural activity. Experiments with real fMRI data, demonstrate that mapping functional connectivity with partial correlation analysis leads to disappearance of a considerable part of the functional connectivity networks relative to that from Pearson correlation analysis and showing small, but consistent networks. The results indicate that partial correlation analysis could perform a better mapping of brain functional connectivity than Pearson correlation analysis.

Paper Details

Date Published: 29 March 2007
PDF: 9 pages
Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 651112 (29 March 2007); doi: 10.1117/12.709012
Show Author Affiliations
Zonglei Zhen, Institute of Automation, Chinese Academy of Science (China)
Jie Tian, Institute of Automation, Chinese Academy of Science (China)
Wei Qin, Institute of Automation, Chinese Academy of Science (China)
Hui Zhang, Institute of Automation, Chinese Academy of Science (China)


Published in SPIE Proceedings Vol. 6511:
Medical Imaging 2007: Physiology, Function, and Structure from Medical Images
Armando Manduca; Xiaoping P. Hu, Editor(s)

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