Integration of network topological and connectivity properties for neuroimaging classification.
about
A review of heterogeneous data mining for brain disorder identificationSingle subject prediction of brain disorders in neuroimaging: Promises and pitfalls.Frequent and discriminative subnetwork mining for mild cognitive impairment classification.Manifold regularized multitask feature learning for multimodality disease classificationWhole-brain connectivity analysis and classification of spinocerebellar ataxia type 7 by functional MRI.A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.Diagnosis of Autism Spectrum Disorders Using Temporally Distinct Resting-State Functional Connectivity NetworksHyper-connectivity of functional networks for brain disease diagnosis.Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.High-order resting-state functional connectivity network for MCI classification.Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease.Combining Disrupted and Discriminative Topological Properties of Functional Connectivity Networks as Neuroimaging Biomarkers for Accurate Diagnosis of Early Tourette Syndrome Children.Novel Cortical Thickness Pattern for Accurate Detection of Alzheimer's Disease.Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy.Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach.Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.Hierarchical High-Order Functional Connectivity Networks and Selective Feature Fusion for MCI Classification.Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease.Sub-Network Kernels for Measuring Similarity of Brain Connectivity Networks in Disease Diagnosis.Diagnosis of Autism Spectrum Disorders Using Multi-Level High-Order Functional Networks Derived From Resting-State Functional MRI.Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls With Subnetwork Selection and Graph Kernel Principal Component Analysis Based on Minimum Spanning Tree Brain Functional Network.Resting-State Brain Functional Hyper-Network Construction Based on Elastic Net and Group Lasso Methods.Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but ChallengingIntegrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive ImpairmentClassification of Alzheimer's Disease, Mild Cognitive Impairment and Normal Control Subjects Using Resting-State fMRI Based Network Connectivity AnalysisLearning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification
P2860
Q28076677-00D4D81E-5034-45EB-9A40-9B5495A579EBQ31063019-4E8CB19B-6858-462B-90D7-86043AF131EAQ33782862-DCC5A491-81B2-4FF6-8029-67C48E712D5AQ35751040-FA81D5C0-FB1C-4322-9B59-1BA4EC7DA474Q35995336-D140CD0E-BB08-44EE-A1AA-A177A971A427Q36348208-51B8E34F-BCB9-4233-8AD1-4521B24FBE64Q36820107-F8832455-D9BC-478C-9400-184A25F4969DQ37676900-CD222263-9604-4D31-B30A-5CBBB93B37E0Q38700565-2DB18A17-7718-46B5-9BC7-195B6BCAA417Q42365669-ECD0FF81-57CF-4C4E-B315-DA98CE616075Q45943253-07EFF3CB-25B4-4264-BB51-06A0887B6953Q45947482-5ABF6772-D479-4045-938F-2E0A484C0301Q45953234-5A730F79-E940-4D95-B23E-F26B3B16A542Q47912810-0D24FACB-5DF0-4D22-B7FF-DD2D3B332E4AQ48001976-D11FD265-B493-4B8C-91DC-11D6F9BC2012Q48187632-FDF05355-2FEC-4BBC-83DD-C2DCAD41E596Q48363857-31A5CBFE-375A-4BB6-BBD1-CE93A02DC99CQ52558552-5ABC3F94-44A9-4DFB-AF11-B364532E5C85Q52690307-BDA1187C-4EFA-4C20-8728-3540A443E649Q54974963-6D4B01F1-939D-434E-AE0E-A0D17162117AQ55008234-78429C58-F21E-4289-8938-869AEDAFD85BQ55260375-A81B9B54-5669-4A4E-BB02-8E3FDFE81139Q55400981-7E2CE331-2C7A-43B5-8210-DB126028A81FQ57170569-E8187F74-2538-4B0D-9282-1C4925C939F0Q57817570-EED04273-B10E-4525-8DFE-36ADCBA3FFA3Q58562914-DCA67F85-3D81-4E20-83D0-565AA02ABB9FQ58758651-7EEE469E-A82E-44B2-9DAA-110119432362
P2860
Integration of network topological and connectivity properties for neuroimaging classification.
description
2014 nî lūn-bûn
@nan
2014 թուականի Փետրուարին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի փետրվարին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Integration of network topolog ...... r neuroimaging classification.
@ast
Integration of network topolog ...... r neuroimaging classification.
@en
type
label
Integration of network topolog ...... r neuroimaging classification.
@ast
Integration of network topolog ...... r neuroimaging classification.
@en
prefLabel
Integration of network topolog ...... r neuroimaging classification.
@ast
Integration of network topolog ...... r neuroimaging classification.
@en
P2093
P2860
P1476
Integration of network topolog ...... r neuroimaging classification.
@en
P2093
Chong-Yaw Wee
Daoqiang Zhang
P2860
P304
P356
10.1109/TBME.2013.2284195
P577
2014-02-01T00:00:00Z