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For decades, it has been largely unknown to what extent multiple functional networks spatially overlap/interact with each other and jointly realize the total cortical function. Here, by developing novel sparse representation of whole-brain fMRI signals and by using the recently publicly released large-scale Human Connectome Project high-quality fMRI data, we show that a number of reproducible and...
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...
Studying functional brain activities based on analyzing BOLD signals derived from fMRI data has received significant interest in the neuroimaging field. However, there exists considerable variability of BOLD signals for the corresponding brain region of interest (ROI) across different subjects. To extract more reliable and representative information from BOLD signals, in this paper, we propose a novel...
Human connectomes constructed via neuroimaging data offer a comprehensive description of the macro-scale structural connectivity within the brain. Thus quantitative assessment of connectome-scale structural and functional connectivities will not only fundamentally advance our understanding of normal brain organization and function, but also have significant importance to systematically and comprehensively...
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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