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Recently, it has been shown that structural connectivity patterns derived from diffusion MRI (dMRI) can be used for cortical parcellation and segmentation. However, most previous methods were based on diffusion tractography, which is limited in depicting local profiles, e.g., in regions beneath the cortical sulcal fundi. Instead, in this paper, we propose to derive effective features directly from...
Cortical folding pattern analysis has attracted significant interest recently due to its significance in understanding the structure and function of the brain. While most previous studies focused on the human brain, the regularity and variability of cortical folding patterns across primate brains such as macaques and chimpanzees are largely unknown. To fill this knowledge gap, this paper develops...
In vivo parcellation of the cerebral cortex via non-invasive neuroimaging techniques has been in active research for over a decade. A variety of model-driven or data-driven computational approaches have been proposed to parcellate the cortex. A fundamental issue in these parcellation methodologies is the features or attributes used to define boundaries between cortical regions. This paper proposes...
Quantitative descriptions of white matter (WM) fiber shape and cortical folding patterns are important for neuroscience research. This paper presents a novel computational method for WM fiber shape pattern analysis, that is, WM fibers are clustered into five primitive shape patterns: closed `U', `M', curved line, open `U' and straight line, based on the automatic clustering of their shape features...
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