Guided by an application in the analysis of Magnetic Resonance Imaging (MRI) scans from the neuroimaging realm, we provide some perspectives on statistical techniques that are able to address issues commonly encountered when dealing with Magnetic Resonance images. The first section of the chapter is devoted to a boostrap-based inferential tool to test for correlation between anatomy and functional activity. The second provides a Bayesian framework to improve estimation of fiber counts from Diffusion Tensor Imaging (DTI) scans. The third one introduces an object-oriented framework to explore and perform testing over network-valued data.
Three Testing Perspectives on Connectome Data
FONTANA, MATTEO;
2018-01-01
Abstract
Guided by an application in the analysis of Magnetic Resonance Imaging (MRI) scans from the neuroimaging realm, we provide some perspectives on statistical techniques that are able to address issues commonly encountered when dealing with Magnetic Resonance images. The first section of the chapter is devoted to a boostrap-based inferential tool to test for correlation between anatomy and functional activity. The second provides a Bayesian framework to improve estimation of fiber counts from Diffusion Tensor Imaging (DTI) scans. The third one introduces an object-oriented framework to explore and perform testing over network-valued data.| File | Dimensione | Formato | |
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2018_Book_StudiesInNeuralDataScience.pdf
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