Dataset Information

A correlation-matrix-based hierarchical clustering method for functional connectivity analysis.

ABSTRACT: In this study, a correlation matrix based hierarchical clustering (CMBHC) method is introduced to extract multiple correlation patterns from resting-state functional magnetic resonance imaging (fMRI) data. It was applied to spontaneous fMRI signals acquired from anesthetized rats, and the results were then compared with those obtained using independent component analysis (ICA), one of the most popular multivariate analysis method for analyzing spontaneous fMRI signals. It was demonstrated that the CMBHC has a higher sensitivity than the ICA, particularly on a single run data, for identifying correlation structures with relatively weak connections, for instance, the thalamocortical connections. Compared to the seed-based correlation analysis, the CMBHC does not require a priori information and thus can avoid potential biases caused by seed selection, and multiple patterns can be extracted at one time. In contrast to other multivariate methods, the CMBHC is based on spatiotemporal correlations of fMRI signals and its analysis outcomes are easy to interpret as the strength of functional connectivity. Moreover, its sensitivity of detecting patterns remains relatively high even for a single dataset. In conclusion, the CMBHC method could be a useful tool for investigating resting-state brain connectivity and function.

SUBMITTER: Liu X

PROVIDER: S-EPMC3477851 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A correlation-matrix-based hierarchical clustering method for functional connectivity analysis.

Liu Xiao X Zhu Xiao-Hong XH Qiu Peihua P Chen Wei W

Journal of neuroscience methods 20120823 1

In this study, a correlation matrix based hierarchical clustering (CMBHC) method is introduced to extract multiple correlation patterns from resting-state functional magnetic resonance imaging (fMRI) data. It was applied to spontaneous fMRI signals acquired from anesthetized rats, and the results were then compared with those obtained using independent component analysis (ICA), one of the most popular multivariate analysis method for analyzing spontaneous fMRI signals. It was demonstrated that t ...[more]

PMID: 22939920

Similar Datasets

Project description:Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,(1) a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium.

Dataset Information

A correlation-matrix-based hierarchical clustering method for functional connectivity analysis.

Publications

A correlation-matrix-based hierarchical clustering method for functional connectivity analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets