9 March 2010 Inferring visual system connectivity using dynamic causal modeling of functional magnetic resonance imaging data
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Abstract
One of the recent themes to study the brain dynamics is studying the effective connectivity between brain regions in the target area. We propose and apply algorithm model, dynamic causal modeling (DCM), Psychophysiological interaction (PPI) and first order kernels and also SVD applied directly to singular intrinsic connectivity matrix end up to integrate and describe the interaction of several Brain Regions based on functional magnetic resonance imaging time series to make inferences about functional integration and segregation within the human brain. The method is to demonstrate using real data to show how such models are able to characterize interregional dependence. We extend estimating and reviewing designed model to characterize the interactions between regions and then to estimate the effective connectivity between these regions. All designs, estimates, reviews are implemented using SPM, one of the free best software packages used for design models and analysis for inferring about FMRI functional magnetic resonance imaging time series.
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Ashraf M. Mahroos, Ashraf M. Mahroos, Yasser M. Kadah, Yasser M. Kadah, } "Inferring visual system connectivity using dynamic causal modeling of functional magnetic resonance imaging data", Proc. SPIE 7626, Medical Imaging 2010: Biomedical Applications in Molecular, Structural, and Functional Imaging, 76261S (9 March 2010); doi: 10.1117/12.844625; https://doi.org/10.1117/12.844625
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