An integrated neighborhood correlation and hierarchical clustering approach of functional MRI

IEEE Transactions on Bio-medical Engineering
Huafu ChenWufan Chen

Abstract

Clustering analysis is a promising data-driven method for the analysis of functional magnetic resonance imaging (fMRI) time series, however, the huge computation load makes it difficult for practical use. In this paper, neighborhood correlation (NC) and hierarchical clustering (HC) methods are integrated as a new approach where fMRI data are processed first by NC to get a preliminary image of brain activations, and then by HC to remove some noises. In HC, to better use spatial and temporal information in fMRI data, a new spatio-temporal measure is introduced. A simulation study and an application to visual fMRI data show that the brain activations can be effectively detected and that different response patterns can be discriminated. These results suggest that the proposed new integrated approach could be useful in detecting weak fMRI signals.

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Citations

Feb 25, 2011·Computer Methods in Biomechanics and Biomedical Engineering·Jiang ZhangFang Fang
Apr 25, 2013·IEEE Transactions on Bio-medical Engineering·Santosh B KatwalBaxter P Rogers
Sep 18, 2009·IEEE Transactions on Bio-medical Engineering·Jiang ZhangWei Liao
May 6, 2009·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Filip Deleus, Marc M Van Hulle
Sep 18, 2007·IEEE Transactions on Bio-medical Engineering·Huaien Luo, Sadasivan Puthusserypady
Nov 8, 2007·Medical Physics·Chun-Cheng R ChenRobert L Galloway
Apr 7, 2017·Biomedizinische Technik. Biomedical Engineering·Savita V Raut, Dinkar M Yadav

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