Development and implementation of algorithms with diffusion tensor images to evaluate brain connectivity

Abstract

Diffusion-weighted magnetic resonance imaging (DWI) is the use of specific MRI sequences, which uses the diffusion of Hydrogen atoms to generate contrast and it allows the mapping of the diffusion process of molecules in vivo and reflects interactions with macromolecules, fibers, and membranes among other. Hydrogen atom diffusion patterns (quantification of anisotropy) can reveal microscopic details about tissue architecture, either normal or in a diseased state. A special kind of DWI, diffusion tensor imaging (DTI), has been used extensively to map white matter tractography in the brain. Tractography is a procedure that is used to highlight neural tracts (axon), its fibers position estimation in brain areas has broad potential implications in cognitive neuroscience fields. An algorithm based on diffusion tensor Image is developed and implemented in order to evaluate brain connectivity in different regions of interest. The major objective of this work is represent two-dimensional and three-dimensional connectivity between areas thereby show the potential of the DTI. Results shows how Connectivity Matrix provides statistical data on the pattern of anatomical relationships, this connectivity pattern is formed by synapses that represent the cross correlations and the flow of information.