標(biāo)題: Titlebook: Connectomics in NeuroImaging; Third International Markus D. Schirmer,Archana Venkataraman,Ai Wern Ch Conference proceedings 2019 Springer [打印本頁] 作者: 決絕 時(shí)間: 2025-3-21 16:38
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: 低能兒 時(shí)間: 2025-3-21 20:32 作者: wangle 時(shí)間: 2025-3-22 02:48 作者: 機(jī)警 時(shí)間: 2025-3-22 07:21
Covariance Shrinkage for Dynamic Functional Connectivity,nd thoughts. Despite the recent advances in statistical methods, estimating the high dimensional dFC states from a small number of available time points remains a challenge. This paper shows that the challenge is reduced by ., a statistical method used for the estimation of large covariance matrices作者: 過去分詞 時(shí)間: 2025-3-22 10:12
Rapid Acceleration of the Permutation Test via Transpositions,y possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributi作者: 原來 時(shí)間: 2025-3-22 15:57 作者: 原來 時(shí)間: 2025-3-22 19:50
A Mass Multivariate Edge-wise Approach for Combining Multiple Connectomes to Improve the Detection nces. While these connectomes have traditionally been constructed using resting-state data, recent work has highlighted the importance of combining multiple task connectomes, particularly for identifying individual differences. Yet, these methods have not yet been extended to investigate differences作者: RENIN 時(shí)間: 2025-3-23 00:31
Adversarial Connectome Embedding for Mild Cognitive Impairment Identification Using Cortical Morphoques, they can further be utilized to build computer-aided MCI diagnosis models. In this paper, we introduce . (ACE) architecture, which is rooted in graph convolution and adversarial regularization to learn relevant connectional features for MCI classification. Existing connectome-based embedding m作者: CLAN 時(shí)間: 2025-3-23 03:55 作者: 不可思議 時(shí)間: 2025-3-23 06:28 作者: 猛擊 時(shí)間: 2025-3-23 10:23 作者: refraction 時(shí)間: 2025-3-23 17:06 作者: 易彎曲 時(shí)間: 2025-3-23 18:42 作者: EXCEL 時(shí)間: 2025-3-24 01:49
0302-9743 ction with MICCAI 2019 in Shenzhen, China, in October 2019..The 13 full papers presented were carefully reviewed and selected from 14 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis作者: Demonstrate 時(shí)間: 2025-3-24 04:51 作者: 先鋒派 時(shí)間: 2025-3-24 09:06
https://doi.org/10.1007/0-387-27636-Xdation. Our model achieves better localization than linear SVM, random forest, and a multilayer perceptron architecture. Our GNN is able to correctly identify bilateral language areas in the brain even when trained on patients whose language network is lateralized to the left hemisphere.作者: TAIN 時(shí)間: 2025-3-24 12:36
https://doi.org/10.1007/0-387-27636-X-of-the-art estimation approaches. When comparing methods on the rs-fMRI scans of 162 subjects, we found that our approach is better at extracting functional networks and capturing differences in rs-fMRI acquisition and diagnosis.作者: staging 時(shí)間: 2025-3-24 16:11
An Introduction to Intrusion Detection,e explored multiple machine learning algorithms that include a Siamese neural network and several classification algorithms. From our experiments, we observed that the Siamese network outperformed other classification models, with an FC fingerprinting accuracy of ..作者: Crater 時(shí)間: 2025-3-24 22:35 作者: V切開 時(shí)間: 2025-3-25 03:11
,Unsupervised Feature Selection via Adaptive Embedding and Sparse Learning for Parkinson’s Disease Dgression markers initiative (PPMI) dataset to validate the proposed method. Our proposed method outperforms other state-of-the-art methods in terms of classification and regression prediction performance.作者: 容易生皺紋 時(shí)間: 2025-3-25 03:28 作者: Trypsin 時(shí)間: 2025-3-25 08:56 作者: 舉止粗野的人 時(shí)間: 2025-3-25 14:17
A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application oe explored multiple machine learning algorithms that include a Siamese neural network and several classification algorithms. From our experiments, we observed that the Siamese network outperformed other classification models, with an FC fingerprinting accuracy of ..作者: Nebulizer 時(shí)間: 2025-3-25 17:08
Test-Retest Reliability of Functional Networks for Evaluation of Data-Driven Parcellation, investigated the impact of parcellation granularity on the test-retest reliability. We observed that the ICCs for geometric parcellation are better than those of a data-driven parcellation, suggesting that the FCs computed using regular parcels in the geometric atlases are more reliable than those computed using a data-driven parcellation.作者: carbohydrate 時(shí)間: 2025-3-25 20:48
Conference proceedings 2019al 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: Banquet 時(shí)間: 2025-3-26 04:07
0302-9743 papers 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..978-3-030-32390-5978-3-030-32391-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 空洞 時(shí)間: 2025-3-26 07:38 作者: Immunotherapy 時(shí)間: 2025-3-26 10:03 作者: 猜忌 時(shí)間: 2025-3-26 13:38
Conference proceedings 2019 MICCAI 2019 in Shenzhen, China, in October 2019..The 13 full papers presented were carefully reviewed and selected from 14 submissions. The papers deal with new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and group作者: Evocative 時(shí)間: 2025-3-26 20:42 作者: BILE 時(shí)間: 2025-3-26 22:28
Constraining Disease Progression Models Using Subject Specific Connectivity Priors,imental results on a subset of the Alzheimer’s Disease Neuroimaging Initiative data set (ADNI 2). Though trained solely on cross-sectional data, our model successfully assigns higher progression scores to patients converting to more severe stages of dementia.作者: Neutral-Spine 時(shí)間: 2025-3-27 03:55
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/c/image/235639.jpg作者: 侵略主義 時(shí)間: 2025-3-27 09:13
https://doi.org/10.1007/978-3-030-32391-2artificial intelligence; brain connectivity; classification; data mining; diffusion MRI; feature selectio作者: GRILL 時(shí)間: 2025-3-27 13:21 作者: arthrodesis 時(shí)間: 2025-3-27 14:42 作者: Legend 時(shí)間: 2025-3-27 20:56 作者: Derogate 時(shí)間: 2025-3-28 01:16
https://doi.org/10.1007/0-387-27636-Xvity is a popular approach in investigating the relationship between the brain morphology, structure, and function and the emergence of neurological diseases. However, extracting relevant diagnostic information from the connectome is still one of the most challenging problems. Many works have thorou作者: Allowance 時(shí)間: 2025-3-28 05:23 作者: ERUPT 時(shí)間: 2025-3-28 07:32
https://doi.org/10.1007/0-387-27636-Xy possible permutation for large-scale brain imaging datasets such as HCP and ADNI with hundreds of subjects is not practical. Many previous attempts at speeding up the permutation test rely on various approximation strategies such as estimating the tail distribution with known parametric distributi作者: 尋找 時(shí)間: 2025-3-28 12:03 作者: opprobrious 時(shí)間: 2025-3-28 17:05 作者: PHON 時(shí)間: 2025-3-28 20:05 作者: Glucose 時(shí)間: 2025-3-29 01:06
An Introduction to Intrusion Detection, in this problem has increased substantially with efforts made to understand the factors that affect the accuracy of fingerprinting and to develop more effective approaches. In this work, we developed a novel machine learning framework for FC fingerprinting. Specifically, while existing approaches m作者: ALLEY 時(shí)間: 2025-3-29 04:28 作者: 徹底明白 時(shí)間: 2025-3-29 11:10 作者: 可忽略 時(shí)間: 2025-3-29 14:11
Understanding Sleep and DreamingRI) measures one of these neurophysiological parameters, which is the blood oxygen level dependent (BOLD) response. The general linear model (GLM) used in fMRI task experiments relates activated brain areas to extrinsic task stimuli. The translation of task-induced neural activation into a hemodynam作者: 是限制 時(shí)間: 2025-3-29 18:48 作者: antecedence 時(shí)間: 2025-3-29 22:52
Connectomics in NeuroImaging978-3-030-32391-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 畸形 時(shí)間: 2025-3-30 00:52
https://doi.org/10.1007/0-387-27636-Xdesign a subgraph matching technique for matching a particular graph-based SE with an input brain connectome. ., we propose GMGA which identifies the optimal sequence of morphological operations using a predefined structural element for distinguishing between two brain states (e.g., late mild cognit作者: 一夫一妻制 時(shí)間: 2025-3-30 05:44
An Introduction to Intrusion Detection,pply our framework to resting-state functional MRI connectomes from a large, publically available autism dataset, ABIDE. We show that energy propagating through the brain over time are different between subnetworks, and that heat kernel features significantly differ between autism and controls. Furt作者: Confound 時(shí)間: 2025-3-30 11:01 作者: 窒息 時(shí)間: 2025-3-30 12:36
