9 March 2010 Extension of dVCA model and its application in estimating fMRI components
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Abstract
General linear model (GLM) and independent component analysis (ICA) are widely used methods in the community of functional magnetic resonance imaging (fMRI) data analysis. GLM and ICA are all assuming that fMRI components are location locked. Here we extend the Differentially variable component analysis (dVCA) and introduce it into fMRI data to analyze the transient changes during fMRI experiments which are ignored in GLM and ICA. We apply the extended dVCA to model fMRI images as the linear combination of ongoing activity and multiple fMRI components. We test our extended dVCA method on simulated images that mimicked the fMRI slice images containing two components, and employ the iterative maximum a posteriori (MAP) solution succeed to estimate each component's time-invariant spatial patterns, and its time-variant amplitude scaling factors and location shifts. The extended dVCA algorithm also identify two fMRI components that reflect the fact of hemispheric asymmetry for motor area in another test with fMRI data acquired with the block design task of right/left hand finger tapping alternately. This work demonstrates that our extended dVCA method is robustness to detect the variability of the fMRI components that maybe existent during the fMRI experiments.
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Gaoyan Zhang, Jiacai Zhang, Li Yao, Xiaojie Zhao, "Extension of dVCA model and its application in estimating fMRI components", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76261R (9 March 2010); doi: 10.1117/12.844079; https://doi.org/10.1117/12.844079
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KEYWORDS
Functional magnetic resonance imaging

Independent component analysis

Data modeling

Brain

Signal to noise ratio

Interference (communication)

Computer simulations

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