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.
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Liu X,Zhu XH,Qiu P,Chen Wdoi
10.1016/j.jneumeth.2012.08.016subject
Has Abstractpub_date
2012-10-15 00:00:00pages
94-102issue
1eissn
0165-0270issn
1872-678Xpii
S0165-0270(12)00327-5journal_volume
211pub_type
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