14 April 2005 Spatial embedding of fMRI for investigating local coupling in human brain
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Abstract
In this paper, we have investigated local spatial couplings in the human brain by applying nonlinear dynamical techniques on fMRI data. We have recorded BOLD-contrast echo-planar fMRI data along with high-resolution T1-weighted anatomical images from the resting brain of healthy human subjects and performed physiological correction on the functional data. The corrected data from resting subjects is spatially embedded into its phase space and the largest Lyapunov exponent of the resulting attractor is calculated and whole slice maps are obtained. In addition, we segment the high-resolution anatomical image and obtain a down sampled mask corresponding to gray and white matter, which is used to obtain mean indices of the exponents for both the tissues separately. The results show the existence of local couplings, its tissue specificity (more local coupling in gray matter than white matter) and dependence on the size of the neighborhood (larger the neighborhood, lesser the coupling). We believe that these techniques capture the information of a nonlinear and evolving system like the brain that may not be evident from static linear methods. The results show that there is evidence of spatio-temporal chaos in the brain, which is a significant finding hitherto not reported in literature to the best of our knowledge. We try to interpret our results from healthy resting subjects based on our knowledge of the native low frequency fluctuations in the resting brain and obtain a better understanding of the local spatial behavior of fMRI. This exploratory study has demonstrated the utility of nonlinear dynamical techniques like spatial embedding in analyzing fMRI data to gain meaningful insights into the working of human brain.
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Gopikrishna Deshpande, Stephen M. LaConte, Scott Peltier, Xiaoping Hu, "Spatial embedding of fMRI for investigating local coupling in human brain", Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.595262; https://doi.org/10.1117/12.595262
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