標(biāo)題: Titlebook: Connectomics in NeuroImaging; Second International Guorong Wu,Islem Rekik,Brent Munsell Conference proceedings 2018 Springer Nature Switzer [打印本頁(yè)] 作者: 惡化 時(shí)間: 2025-3-21 16:33
書(shū)目名稱Connectomics in NeuroImaging影響因子(影響力)
書(shū)目名稱Connectomics in NeuroImaging影響因子(影響力)學(xué)科排名
書(shū)目名稱Connectomics in NeuroImaging網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Connectomics in NeuroImaging網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Connectomics in NeuroImaging被引頻次
書(shū)目名稱Connectomics in NeuroImaging被引頻次學(xué)科排名
書(shū)目名稱Connectomics in NeuroImaging年度引用
書(shū)目名稱Connectomics in NeuroImaging年度引用學(xué)科排名
書(shū)目名稱Connectomics in NeuroImaging讀者反饋
書(shū)目名稱Connectomics in NeuroImaging讀者反饋學(xué)科排名
作者: 吝嗇性 時(shí)間: 2025-3-21 23:09 作者: 有其法作用 時(shí)間: 2025-3-22 03:57
Theories of Dreams and Dreamingrameworks are not suitable for GIFE due to the massive fiber tracts and the registration errors in the original GiTA framework. To address these issues, we propose a novel method called multidimensional extrapolating (MDE) to achieve GIFE. In our experiment, simulation results show quantitatively th作者: 戰(zhàn)役 時(shí)間: 2025-3-22 05:12
Theories of Dreams and Dreamingion, which might not be robust to outliers and constrains the locality of data to a fixed bandwidth. To address these limitations, we propose the . (ICBM), a metric that captures the variation of . changes in brain morphology. In particular, we use . estimated from multiple cortical attributes (e.g.作者: Trigger-Point 時(shí)間: 2025-3-22 12:02 作者: 歌唱隊(duì) 時(shí)間: 2025-3-22 14:22
Understanding Sleep and Dreaming360 brain regions in the Glasser parcellation, we observed .5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance作者: 歌唱隊(duì) 時(shí)間: 2025-3-22 18:12 作者: 一再遛 時(shí)間: 2025-3-22 21:14 作者: 高爾夫 時(shí)間: 2025-3-23 02:42 作者: LINES 時(shí)間: 2025-3-23 07:15
Understanding Sleep and Dreamingblicly available life-span study. We demonstrate changes in rich-club membership with age alongside a shift in importance from ’peripheral’ seeder to feeder subnetworks. Our results show a refinement within the rich-club structure (increase in transitivity and betweenness centrality), as well as inc作者: palliate 時(shí)間: 2025-3-23 12:09 作者: 誓言 時(shí)間: 2025-3-23 16:15
GIFE: Efficient and Robust Group-Wise Isometric Fiber Embedding,rameworks are not suitable for GIFE due to the massive fiber tracts and the registration errors in the original GiTA framework. To address these issues, we propose a novel method called multidimensional extrapolating (MDE) to achieve GIFE. In our experiment, simulation results show quantitatively th作者: harpsichord 時(shí)間: 2025-3-23 21:32 作者: debris 時(shí)間: 2025-3-24 01:16
Neonatal Morphometric Similarity Networks Predict Atypical Brain Development Associated with Preterfference between predicted and actual age. The model predicted chronological age with a mean absolute error of 0.88 (±0.63) weeks, and it consistently predicted preterm infants to have a lower RBNMI than term infants. We conclude that MSNs derived from multimodal imaging predict chronological brain 作者: 落葉劑 時(shí)間: 2025-3-24 05:14
Heritability Estimation of Reliable Connectomic Features,360 brain regions in the Glasser parcellation, we observed .5% of them with significantly high heritability in fractional anisotropy, fiber length or fiber number. All the tested network level measures, capturing the network integrality, segregation or resilience, are highly heritable, with variance作者: GRE 時(shí)間: 2025-3-24 08:40 作者: MONY 時(shí)間: 2025-3-24 14:28 作者: 慟哭 時(shí)間: 2025-3-24 16:14 作者: COST 時(shí)間: 2025-3-24 20:45 作者: 背信 時(shí)間: 2025-3-25 01:55
0302-9743 unction with MICCAI 2018 in Granada, Spain, in September 2018.. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with?new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagn作者: 輕浮女 時(shí)間: 2025-3-25 05:46
https://doi.org/10.1007/0-387-28698-5s with relatively simple and interpretable models. On ABIDE Preprocessed dataset, our methods classify autism versus control subjects with 71.1% accuracy. We also show that Riemannian methods beat baseline in regressing connectome features to subject autism severity scores.作者: kidney 時(shí)間: 2025-3-25 10:35
Riemannian Regression and Classification Models of Brain Networks Applied to Autism,s with relatively simple and interpretable models. On ABIDE Preprocessed dataset, our methods classify autism versus control subjects with 71.1% accuracy. We also show that Riemannian methods beat baseline in regressing connectome features to subject autism severity scores.作者: 變形 時(shí)間: 2025-3-25 14:38
Conference proceedings 2018th MICCAI 2018 in Granada, Spain, in September 2018.. The 15 full papers presented were carefully reviewed and selected from 20 submissions. The papers deal with?new advancements in network construction, analysis, and visualization techniques in connectomics and their use in clinical diagnosis and g作者: 發(fā)展 時(shí)間: 2025-3-25 18:12
Understanding Sleep and Dreamingowed by a non-rigid registration is proposed. Our approach has been successfully applied to 278 histological sections of a rat brain and the performance has been quantitatively evaluated using manually placed landmarks by an expert.作者: integral 時(shí)間: 2025-3-25 20:48 作者: Critical 時(shí)間: 2025-3-26 00:21
Theories of Dreams and Dreamingrcodes are significantly correlated to individual differences in cognition and personality, with high reproducibility. Topological data analyses, including approaches to model connectivity in the time domain, are promising tools for representing high-level aspects of cognition, development, and neuropathology.作者: 加入 時(shí)間: 2025-3-26 08:12
Theories of Dreams and Dreamingsion and the motor network identification against two baselines: a standard functional parcellation with no reassignment and a recently published method with a purely data-driven reassignment procedure. Our method outperforms the original functional parcellation in intra-network cohesion and both methods in motor network identification.作者: Harridan 時(shí)間: 2025-3-26 10:51
Prologue A Visit to a Sleep and Dreams Labhe simplified connectome in a biological sex classification task. We find that the parcellation based-atlas computed using a greedy search at a hierarchical depth 3 outperforms all other parcellation-based atlases as well as the standard Dessikan-Killiany anatomical atlas in all three assessments.作者: 躺下殘殺 時(shí)間: 2025-3-26 13:30
Towards Ultra-High Resolution 3D Reconstruction of a Whole Rat Brain from 3D-PLI Data,owed by a non-rigid registration is proposed. Our approach has been successfully applied to 278 histological sections of a rat brain and the performance has been quantitatively evaluated using manually placed landmarks by an expert.作者: 有機(jī)體 時(shí)間: 2025-3-26 19:30 作者: 觀點(diǎn) 時(shí)間: 2025-3-26 22:54 作者: lethal 時(shí)間: 2025-3-27 01:38 作者: 脫毛 時(shí)間: 2025-3-27 09:08 作者: 倒轉(zhuǎn) 時(shí)間: 2025-3-27 12:06
Conference proceedings 2018s 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: elucidate 時(shí)間: 2025-3-27 16:40 作者: Inflammation 時(shí)間: 2025-3-27 21:15 作者: deadlock 時(shí)間: 2025-3-27 22:37
FOD-Based Registration for Susceptibility Distortion Correction in Connectome Imaging, of human brain pathways. It was recently noted, however, that significant distortions remain present in the data of most subjects preprocessed by the HCP-Pipeline, which have been widely distributed and used extensively in connectomics research. Fundamentally this is caused by the reliance of the H作者: 古董 時(shí)間: 2025-3-28 02:27
GIFE: Efficient and Robust Group-Wise Isometric Fiber Embedding, We previously propose the Group-w.se Tractogram Analysis (GiTA) framework for identifying anatomically valid fibers across subjects according to cross-subject consistency. However, the original framework is based on computationally expensive brute-force KNN search. In this work, we propose a more g作者: CERE 時(shí)間: 2025-3-28 09:00
Multi-modal Brain Tensor Factorization: Preliminary Results with AD Patients,, the variability in connectivity definitions poses a challenge. We propose to represent multi-modal brain networks over a population with a single 4D brain tensor (.) and factorize . to get a lower dimensional representation per case and per modality. We used 7 known functional networks as the cano作者: Ophthalmologist 時(shí)間: 2025-3-28 11:32
Intact Connectional Morphometricity Learning Using Multi-view Morphological Brain Networks with App identifying the morphological signature of a specific brain disorder can improve diagnosis and better explain how neuroanatomical changes associate with function and cognition. To capture this signature, a landmark study introduced, brain ., a global metric defined as the proportion of phenotypic v作者: 脫水 時(shí)間: 2025-3-28 15:14 作者: 問(wèn)到了燒瓶 時(shí)間: 2025-3-28 22:32 作者: Strength 時(shí)間: 2025-3-29 02:28 作者: Measured 時(shí)間: 2025-3-29 05:30
Riemannian Regression and Classification Models of Brain Networks Applied to Autism,thods that exploit the Riemannian geometry of SPD matrices appropriately adhere to the positive definite constraint, unlike Euclidean methods. Recently proposed approaches for rsfMRI analysis have achieved high accuracy on public datasets, but are computationally intensive and difficult to interpret作者: 教義 時(shí)間: 2025-3-29 10:58
Defining Patient Specific Functional Parcellations in Lesional Cohorts via Markov Random Fields, initial parcellation and then iteratively reassigns the voxel memberships at the subject level. Our algorithm uses a maximum . inference strategy based on the neighboring voxel assignments and the Pearson correlation coefficients between the voxel time series and the parcel reference signals. Our m作者: 競(jìng)選運(yùn)動(dòng) 時(shí)間: 2025-3-29 12:26
Data-Specific Feature Selection Method Identification for Most Reproducible Connectomic Feature Dison of extremely high-dimensional connectomic data drawn from multiple neuroimaging sources (e.g., functional and structural MRIs), effective feature selection (FS) methods have become indispensable components for (i) disentangling brain states (e.g., early vs late mild cognitive impairment) and (ii)作者: ANNUL 時(shí)間: 2025-3-29 19:18 作者: 催眠 時(shí)間: 2025-3-29 23:10
Connectivity-Driven Brain Parcellation via Consensus Clustering,oposed dense connectivity representation, termed continuous connectivity, by first performing graph-based hierarchical clustering of individual brains, and subsequently aggregating the individual parcellations into a consensus parcellation. The search for consensus minimizes the sum of cluster membe作者: Hippocampus 時(shí)間: 2025-3-30 03:32 作者: conference 時(shí)間: 2025-3-30 04:58 作者: 假 時(shí)間: 2025-3-30 09:50
Connectomics in NeuroImaging978-3-030-00755-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: anaphylaxis 時(shí)間: 2025-3-30 13:11
https://doi.org/10.1007/978-3-030-00755-3Artificial intelligence; Brain Connectivity; Classification; Connectome; Connectomics; Diffusion MRI; Elec作者: 河潭 時(shí)間: 2025-3-30 20:16
978-3-030-00754-6Springer Nature Switzerland AG 2018作者: anniversary 時(shí)間: 2025-3-30 23:44 作者: JAMB 時(shí)間: 2025-3-31 04:33
Understanding Sleep and Dreamingzation of the brain. We introduce a new method for registration and 3D reconstruction of high- and ultra-high resolution (?64 .m and 1.3 .m pixel size) histological images of a Wistar rat brain acquired by 3D polarized light imaging (3D-PLI). Our method exploits multi-scale and multi-modal 3D-PLI da
