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Abstract:
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Human brain mapping from functional Magnetic Resonance Imaging has outgrown in several interdisciplinary subjects and has worthwhile contributions over last few decades.The field of brain connectivity can be broadly classified into three types, anatomical,functional and,effective connectivity.The latest one works at neuroanatomical or neuronal level and, leads to more interesting statistical parametric maps.Structural equation modeling(SEM) and dynamic causal modeling(DCM) are mostly used models to study effective connectivity.Further SEM lies on explaining the underlying covariance structure of the data.We will consider our SEM analysis based on the observed blood oxygen level dependent(BOLD) contrast which has a spatio-temporal covariance structure.We propose a technique of estimating the underlying covariance structure which leads us to the interpretation of connectivity using non-zero off-diagonal entries from our estimated covariance matrix.The observed data are at 85 slices of image with size 60×85 on human brain.We select centrally spaced 93079 voxels to exclude low signal voxels outside the brain region.Also at each voxel, we further observe 256 temporal resolution.
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