標(biāo)題: Titlebook: Connectomics in NeuroImaging; First International Guorong Wu,Paul Laurienti,Brent C. Munsell Conference proceedings 2017 Springer Internat [打印本頁] 作者: 厭氧 時間: 2025-3-21 16:51
書目名稱Connectomics in NeuroImaging影響因子(影響力)
書目名稱Connectomics in NeuroImaging影響因子(影響力)學(xué)科排名
書目名稱Connectomics in NeuroImaging網(wǎng)絡(luò)公開度
書目名稱Connectomics in NeuroImaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Connectomics in NeuroImaging被引頻次
書目名稱Connectomics in NeuroImaging被引頻次學(xué)科排名
書目名稱Connectomics in NeuroImaging年度引用
書目名稱Connectomics in NeuroImaging年度引用學(xué)科排名
書目名稱Connectomics in NeuroImaging讀者反饋
書目名稱Connectomics in NeuroImaging讀者反饋學(xué)科排名
作者: Diverticulitis 時間: 2025-3-21 21:38
The uniform output regulation problem,ising tool for imaging-based brain disease diagnosis. Conventional low-order FC network (LON) usually characterizes pairwise temporal correlation of rs-fMRI signals between any pair of brain regions. Meanwhile, high-order FC network (HON) has provided an alternative brain network modeling strategy, 作者: 連鎖 時間: 2025-3-22 02:45 作者: adhesive 時間: 2025-3-22 07:36 作者: Asperity 時間: 2025-3-22 12:40
https://doi.org/10.1007/0-8176-4465-2 them using graph theory. As a result, it can be non-intuitive to grasp the contribution of each edge within a graph, both at a local and global scale. Here, we introduce a new platform that enables tractography-based networks to be explored in a highly interactive real-time fashion. The framework a作者: 控制 時間: 2025-3-22 15:11
https://doi.org/10.1007/0-8176-4465-2vity (functional MRI). However, how early dementia affects the morphology of the cortical surface remains poorly understood. In this paper, we first introduce . architecture which stacks multiple networks, each quantifying a cortical attribute (e.g., thickness). Second, to model the relationship bet作者: 控制 時間: 2025-3-22 18:35 作者: Autobiography 時間: 2025-3-22 23:43 作者: 課程 時間: 2025-3-23 04:27 作者: PANT 時間: 2025-3-23 07:12
Alexey Pavlov,Nathan Wouw,Henk Nijmeijericine. Recently, machine learning techniques typically use .-.-. or .-. connectivity features to understand how the brain is organized, and then use this information to predict the clinical outcome. Unfortunately, computational models that are trained with these types of features are very localized 作者: Water-Brash 時間: 2025-3-23 11:40
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作者: 強化 時間: 2025-3-23 16:06
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作者: SAGE 時間: 2025-3-23 21:15 作者: 蝕刻術(shù) 時間: 2025-3-23 22:36 作者: MENT 時間: 2025-3-24 05:13
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作者: forbid 時間: 2025-3-24 09:33 作者: 親愛 時間: 2025-3-24 11:32
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作者: 懶惰人民 時間: 2025-3-24 17:45
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作者: 失敗主義者 時間: 2025-3-24 21:08
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 作者: Occupation 時間: 2025-3-25 00:54 作者: projectile 時間: 2025-3-25 06:52 作者: propose 時間: 2025-3-25 10:29
Connectomics in NeuroImaging978-3-319-67159-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 嗎啡 時間: 2025-3-25 14:36 作者: SLING 時間: 2025-3-25 19:41
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/235641.jpg作者: Biomarker 時間: 2025-3-25 21:38
Conference proceedings 2017apers deal with?new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group comparison studies as well as in various neuroimaging applications..作者: 正常 時間: 2025-3-26 01:38
The uniform output regulation problem,ily given by widespread network differences, or the difference lies in specific local connections which are just captured by global metrics. Namely, whether miswiring of brain connections related to ASD is localized or diffuse. The presented results suggest that the widespread hypothesis is more likely.作者: CAJ 時間: 2025-3-26 05:37
Upon What Does the Turtle Stand?er spectral densities. Experimental results show significant increases in pairwise node to node correlations and coherences following alignment. We apply this method for finding group differences in connectivity between patients with major depression and healthy controls.作者: insurgent 時間: 2025-3-26 11:33 作者: fatuity 時間: 2025-3-26 13:59 作者: Insubordinate 時間: 2025-3-26 18:42 作者: 卡死偷電 時間: 2025-3-26 22:04 作者: Orthodontics 時間: 2025-3-27 03:32 作者: 食品室 時間: 2025-3-27 08:36
Topological Network Analysis of Electroencephalographic Power Maps, model the spatial distribution of an EEG topographic power map via its dynamic local connectivity with respect to a changing scale. We compare topological features of the network filtrations between long-term meditators and mediation-na?ve practitioners to investigate if long-term meditation practice changes power patterns in the brain.作者: 孵卵器 時間: 2025-3-27 11:05
Topological Distances Between Brain Networks,in persistent homology based brain network models. The superior performance of KS-distance is contrasted against matrix norms and GH-distance in random network simulations with the ground truths. The KS-distance is then applied in characterizing the multimodal MRI and DTI study of maltreated children.作者: cogitate 時間: 2025-3-27 17:24 作者: 容易懂得 時間: 2025-3-27 21:34 作者: 仇恨 時間: 2025-3-27 23:35
https://doi.org/10.1007/0-8176-4465-2 controls from Schizophrenia patients. The new kernel offers superior classification accuracy to previous kernels, and the adjusted eigenvalues allow discovery of clinically meaningful differences in connectivity between patients and controls.作者: separate 時間: 2025-3-28 02:14
https://doi.org/10.1007/0-8176-4465-2propose a ., which projects each pair of brain multiplex sets onto a low-dimensional space where they are fused, then classified. Our framework achieved the best classification results for the right hemisphere 90.8% and the left hemisphere 89.5%.作者: 厭倦嗎你 時間: 2025-3-28 07:55 作者: 折磨 時間: 2025-3-28 11:06
Discriminative Log-Euclidean Kernels for Learning on Brain Networks, controls from Schizophrenia patients. The new kernel offers superior classification accuracy to previous kernels, and the adjusted eigenvalues allow discovery of clinically meaningful differences in connectivity between patients and controls.作者: formula 時間: 2025-3-28 18:35 作者: Crepitus 時間: 2025-3-28 21:21 作者: Humble 時間: 2025-3-29 01:57 作者: 搖曳 時間: 2025-3-29 06:58 作者: 不適 時間: 2025-3-29 10:02 作者: exquisite 時間: 2025-3-29 12:22
Conference proceedings 2017h MICCAI 2017 in Quebec City, Canada, in September 2017...The 19 full papers presented were carefully reviewed and selected from 26 submissions. The papers deal with?new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis a作者: AWRY 時間: 2025-3-29 16:19 作者: rheumatology 時間: 2025-3-29 23:27 作者: ULCER 時間: 2025-3-30 02:52
FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from Functional MRI,me-series signals. The FCNet consists of a convolutional neural network that extracts features from time-series signals and a fully connected network that computes the similarity between the extracted features in a Siamese architecture. The functional connectivity computed using FCNet is combined wi作者: 脊椎動物 時間: 2025-3-30 07:45 作者: PAC 時間: 2025-3-30 12:04
A Simple and Efficient Cylinder Imposter Approach to Visualize DTI Fiber Tracts,ased parallel processing systems. Using 10,000 fiber tracts derived from a real DTI scan, we show the rendering speed of our imposter approach is 50% times faster, and requires 900% less memory, when compared visualization approach that uses 3D cylinders.作者: 神化怪物 時間: 2025-3-30 15:43
Revisiting Abnormalities in Brain Network Architecture Underlying Autism Using Topology-Inspired Stining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using s作者: interior 時間: 2025-3-30 19:08 作者: 個阿姨勾引你 時間: 2025-3-30 22:08
Early Brain Functional Segregation and Integration Predict Later Cognitive Performance, the triple networks harbor at the medial prefrontal cortex, an ideal brain region for unveiling early development of the high-level functions. Further parcellation of this area indicates consistent subdivisions from 0 to 2 years old, indicating largely predefined functional segregation in this high作者: Incorporate 時間: 2025-3-31 04:31 作者: dapper 時間: 2025-3-31 08:54
A Whole-Brain Reconstruction Approach for FOD Modeling from Multi-Shell Diffusion MRI,arizations and show that anisotropic regularization produces more robust results across various fiber configurations. We also apply our method to . data from 80 HCP subjects and evaluate the impact of FOD modeling methods on the reconstruction of the challenging fiber bundles from the locus coeruleu作者: OGLE 時間: 2025-3-31 10:34
The uniform output regulation problem,ific HONs (.-. HONs), . a series of cross-frequency interaction-based HONs (.-. HONs) based on the low-order FC sub-networks constructed at different frequency bands. Both types of these HONs, together with the frequency-specific LONs, are used for the complex network analysis-based feature extracti作者: GNAW 時間: 2025-3-31 13:51
The uniform output regulation problem,fication method against supervised and unsupervised state-of-the-art methods, while depicting the most discriminative high- and low-order relationships between morphological regions in the left and right hemispheres.作者: 自愛 時間: 2025-3-31 17:51
https://doi.org/10.1007/0-8176-4465-2me-series signals. The FCNet consists of a convolutional neural network that extracts features from time-series signals and a fully connected network that computes the similarity between the extracted features in a Siamese architecture. The functional connectivity computed using FCNet is combined wi作者: 小官 時間: 2025-4-1 01:23
Alexey Pavlov,Nathan Wouw,Henk Nijmeijerecognize the identity of 20 different subjects. The results show person identification models trained with our feature are approximately 30% and 50% more accurate than models trained only using hub-based features and region-to-region features, respectively. Lastly, a . is identified using a neural n作者: 故意釣到白楊 時間: 2025-4-1 01:56 作者: Cryptic 時間: 2025-4-1 09:43
https://doi.org/10.1007/1-4020-2799-0ining topological data analysis with statistical inference, our results provide evidence of statistically significant network-specific structural abnormalities in autism, from SCNs derived from SN and ECN. These differences in brain architecture are consistent with direct structural analysis using s作者: 一致性 時間: 2025-4-1 14:15 作者: Fortuitous 時間: 2025-4-1 16:29
