Abstract
Blood oxygenation level dependent (BOLD) contrast based functional magnetic resonance imaging (fMRI) has been widely utilized to detect brain neural activities and great efforts are now stressed on the hemodynamic processes of different brain regions activated by a stimulus. The focus of this paper is Gaussian dynamic convolution models of the fMRI BOLD response. The convolutions are between the perfusion function of the neural response to a stimulus and a Gaussian function. The parameters of the models are estimated by a nonlinear least-squares optimal algorithm for the fMRI data of eight subjects collected in a visual stimulus experiment. The results show that the Gaussian model is better in fitting the data.
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Chen, H., Zeng, L., Yao, D., Gao, Q. (2006). A Gaussian Dynamic Convolution Models of the FMRI BOLD Response. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_4
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DOI: https://doi.org/10.1007/11759966_4
Publisher Name: Springer, Berlin, Heidelberg
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