Titlebook: Connectomics in NeuroImaging; First International Guorong Wu,Paul Laurienti,Brent C. Munsell Conference proceedings 2017 Springer Internat

Water-Brash

Portable Computing Challenges Schoolingsing, and white matter that facilitates neuronal communication between gray matter regions. To better understand the organization of white matter connections in the brain, white matter fiber tracts derived from a diffusion tensor image scan is estimated and visualized by publically available softwar

強化

https://doi.org/10.1007/1-4020-2799-0hat maps brain regions with covarying gray matter density across subjects. It provides a way to probe the anatomical structures underlying intrinsic connectivity networks (ICNs) through the analysis of the gray matter signal covariance. In this paper, we apply topological data analysis in conjunctio

SAGE

蝕刻術

MENT

Upon What Does the Turtle Stand?al geometry and functional data analysis to define a functional representation for fMRI signals. The space of fMRI functions is then equipped with a reparameterization invariant Riemannian metric that enables elastic alignment of both amplitude and phase of the fMRI time courses as well as their pow

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親愛

N.A. Kulikova,E.V. Stepanova,O.V. Korolevaethods that mostly first estimate functional connectivity and then extract features with a graph theory, in this paper, we propose a novel method that directly models the temporal stochastic patterns inherent in BOLD signals for each Region Of Interest (ROI) individually. Specifically, we model temp

懶惰人民

D.R. van Stempvoort,S. Lesage,J. Molsonlti-shell diffusion imaging. Existing tools for fiber orientation distribution (FOD) reconstruction, however, predominantly solves this problem on a voxel-by-voxel basis, disregarding the spatial regularity in brain anatomy. In this work, we propose a novel computational framework for the joint reco

失敗主義者

Upscaling Multiphase Flow in Porous MediaFurther, matrix norms are sensitive to outliers. A few extreme edge weights may severely affect the distance. Thus it is necessary to develop network distances that recognize topology. In this paper, we introduce Gromov-Hausdorff (GH) and Kolmogorov-Smirnov (KS) distances. GH-distance is often used

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