Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics.
about
Computational analysis in epilepsy neuroimaging: A survey of features and methodsStructural imaging biomarkers of sudden unexpected death in epilepsy.Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy.Multicenter mapping of structural network alterations in autism.Multimodal data and machine learning for surgery outcome prediction in complicated cases of mesial temporal lobe epilepsy.Neuroimaging-Based Phenotyping of the Autism Spectrum.Frontal gray matter abnormalities predict seizure outcome in refractory temporal lobe epilepsy patientsPreoperative prediction of temporal lobe epilepsy surgery outcome.Neuroimaging evaluation in refractory epilepsy.In vivo MRI signatures of hippocampal subfield pathology in intractable epilepsy.The superficial white matter in temporal lobe epilepsy: a key link between structural and functional network disruptions.Personalized structural image analysis in patients with temporal lobe epilepsy.Subregional Mesiotemporal Network Topology Is Altered in Temporal Lobe Epilepsy.Automated analysis of seizure semiology and brain electrical activity in presurgery evaluation of epilepsy: A focused survey.Quantitative Measurement of Longitudinal Relaxation Time (qT1) Mapping in TLE: A Marker for Intracortical Microstructure?Substrates of metacognition on perception and metacognition on higher-order cognition relate to different subsystems of the mentalizing network.Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder.A Comparative Study of Feature Selection Methods for the Discriminative Analysis of Temporal Lobe Epilepsy.The spectrum of structural and functional imaging abnormalities in temporal lobe epilepsy.Gray matter structural compromise is equally distributed in left and right temporal lobe epilepsy.Gray Matter and White Matter Abnormalities in Temporal Lobe Epilepsy Patients with and without Hippocampal Sclerosis.The impact of epilepsy surgery on the structural connectome and its relation to outcome.
P2860
Q26750063-35DF724E-18C7-47DF-8600-BECF9C449B21Q27303660-362AB694-FF7E-4EBB-A3A1-ADA20F835185Q27348629-B8A683D6-744C-4803-87B1-B9CB1AB581A2Q30487689-2A1A28B1-2846-40BE-9D4C-8175F19D52C8Q30978934-9785C898-AA37-4341-9C9A-E228495A423AQ31053462-04F558E8-AEC5-4BC0-BF8E-57F5DD9F34FCQ36133399-64929E87-3FD9-4D6F-8F3D-C99EB0444402Q36152591-3D8BA968-05CD-443A-8886-6DE527141133Q36589825-DB547C59-0DBD-4E2F-AAE1-C14F217E6725Q40200110-B3C14155-354A-4608-950B-CD72CF00C6F9Q41337656-2526ECC1-58CE-4EEB-A109-6EEE68E46298Q41543100-E21AAC80-45DD-4D85-BF4B-C7A850BB032EQ42155757-F0658AB8-5EC4-4977-B7DF-E660A6E5B43DQ45945130-BFC051BA-DEA4-4AFB-ACA3-BAECE89AF5A8Q47110145-B69E7393-49CE-4AB5-945E-FD7887BE34F1Q47430600-3BA4398E-6D24-4F01-96A3-B2DF2522EF9BQ47670117-BE0F9F6F-ECCA-48B5-9FAD-29B5EA1F543BQ47835991-149149FE-56AC-4348-97B4-47C53F8A6D7EQ48712283-0AAC47A6-58A7-40BC-B8BD-F28DFD239F23Q50763034-AEF575C0-5CB7-4CEE-8299-79471B1665A5Q54968552-4622FB94-43D9-4FDB-8682-76353C947619Q54988006-EAA9FC66-5D88-47FA-9199-DD1BFFF8331B
P2860
Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Magnetic resonance imaging pat ...... lassification and prognostics.
@en
type
label
Magnetic resonance imaging pat ...... lassification and prognostics.
@en
prefLabel
Magnetic resonance imaging pat ...... lassification and prognostics.
@en
P2093
P2860
P356
P1433
P1476
Magnetic resonance imaging pat ...... lassification and prognostics.
@en
P2093
Andrea Bernasconi
Boris C Bernhardt
Neda Bernasconi
Seok-Jun Hong
P2860
P304
P356
10.1002/ANA.24341
P577
2015-01-13T00:00:00Z