about
The Human Connectome Project: Progress and ProspectsBrain Vascular Imaging TechniquesA Symmetry-Based Method to Infer Structural Brain Networks from Probabilistic Tractography DataWin-win data sharing in neuroscience.Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature.Studying neuroanatomy using MRI.Best practices in data analysis and sharing in neuroimaging using MRI.Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRIBrain Mapping and Synapse Quantification In vivo: It's Time to Imaging.Functional Sensitivity of 2D Simultaneous Multi-Slice Echo-Planar Imaging: Effects of Acceleration on g-factor and Physiological Noise.Feature Selection Methods for Zero-Shot Learning of Neural Activity.A specific pattern of gray matter atrophy in Alzheimer's disease with depression.Advances in Studying Brain Morphology: The Benefits of Open-Access Data.Building connectomes using diffusion MRI: why, how and but.Recent theoretical, neural, and clinical advances in sustained attention research.The Bright, Artificial Intelligence-Augmented Future of Neuroimaging Reading.Graph theory reveals amygdala modules consistent with its anatomical subdivisions.On the road towards the global analysis of human synapses.Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project.The challenge of mapping the human connectome based on diffusion tractography.A connectomic approach to the lateral geniculate nucleus.Quasi-periodic patterns of intrinsic brain activity in individuals and their relationship to global signal.A digital 3D atlas of the marmoset brain based on multi-modal MRI.Multimodal surface matching with higher-order smoothness constraints.Ethical and Legal Implications of the Methodological Crisis in Neuroimaging.Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity.Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.Segmenting hippocampal subfields from 3T MRI with multi-modality images.Post-genomic behavioral genetics: From revolution to routine.From abstract topology to real thermodynamic brain activity.On being a circuit psychiatrist.Tau PET imaging predicts cognition in atypical variants of Alzheimer's disease.Cognitive task information is transferred between brain regions via resting-state network topology.Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology.Imaging at ultrahigh magnetic fields: History, challenges, and solutions.Default mode network, connectivity, traumatic brain injury and post-traumatic amnesia.Circuitry-Based Human Neuroanatomy for the Next Generation in Psychiatry and Neuroscience.Advance, Adapt, Achieve: The 2016 Congress of Neurological Surgeons Presidential Address.Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.Towards a new approach to reveal dynamical organization of the brain using topological data analysis.
P2860
Q28584583-6D6931A4-E384-462B-B27E-9D5FF5F484C3Q30363574-1926F880-F76C-483A-8A49-D8B81F4B2228Q31143514-D8B55B57-6418-4D7F-8E3E-7E3BEE34D694Q31158137-5917545A-1AE6-4897-852B-7D1C7F238931Q31159591-8BC3DB61-EC04-452A-BB46-78FA7A68067FQ31165123-F961C68B-CC8D-4663-BC70-57A90B46C0E7Q31165126-1BB3952E-BC76-4130-9B51-643E646E9305Q33586123-23AB243B-EDAB-4317-9367-3DF92EF4D1DEQ37683085-0356A78F-2965-4365-A0E8-4C4E6654235FQ37729526-8D313E0D-AECE-4CD3-A299-11A1534F24B6Q38374308-7B17C6B4-9104-49B9-B605-108BDC36EA6BQ38601094-08A7E6A4-BB3C-4B88-AAEC-7A7DA2334662Q38616812-D1040417-0FFF-4D71-9147-1D918977DDE1Q38666096-7BC78DE5-8C2F-4348-92B4-5BC0F9D33583Q39161976-3E293FAC-21AF-4EE7-8957-B01B8E1E0446Q42363802-317B4BC0-5E13-4900-B96C-F1A838732744Q43134447-FBCD85F6-3832-4DED-818B-20544CDC7C3EQ46493591-92B28BA1-480F-460A-A707-B9E47B31C791Q46681362-B42B7595-3EAC-4B6E-BD7F-33F8436FAE58Q47099419-8CE189C2-38BD-4FC2-B16B-6A5672576E9AQ47110267-97F5B79A-9074-43BB-8530-5F36431BDC53Q47314680-BE39BEF9-5135-4323-999C-B60549F6DF06Q47363395-A6742C9E-29EF-47D2-B7F7-AA55D5FFD989Q47382944-10064CD5-4FF0-441C-AE2D-3C981EB0AAA4Q47550953-35B468C6-FEF7-4B78-B5F1-B0DAB65305CFQ47561456-5253A5A7-895C-42F8-8971-C34CB1DEEA16Q47565391-144ADE8B-6BB3-4E43-A6DB-2B11849CD4FAQ47618470-30B480EB-0E20-4EEA-8119-A2C679E858BAQ47690981-F0DAFD40-D50C-4754-B906-0D0A9BF4B86AQ47823712-23A1097E-D75E-4C8C-9573-355F96924C72Q47899105-62BC415E-720A-4B68-A42B-4725A93FD686Q47985518-D299CD35-0738-4F39-AB9D-0554DE68A915Q48013566-DC01A304-D96C-44CB-8E59-B0009BDCCEB2Q48150857-E4351E0B-EEB2-4333-81C3-4183877E16A9Q48200729-E34A178C-2CE8-4864-8753-A6745F6EBCE4Q48408119-5FA74397-1996-4283-AA21-62E335961BA3Q49916201-17A087A4-7435-4BED-8F6F-EBACDA2A837EQ50130546-CF26CDAC-55F4-4804-A418-5780B0D96CC5Q50629638-A97F3663-BC83-445E-98EB-0D501CAF31C5Q52324469-52820918-A2A0-4E59-8086-86F8DB10D4E3
P2860
description
2016 nî lūn-bûn
@nan
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
2016年论文
@zh
2016年论文
@zh-cn
name
The Human Connectome Project's neuroimaging approach.
@ast
The Human Connectome Project's neuroimaging approach.
@en
type
label
The Human Connectome Project's neuroimaging approach.
@ast
The Human Connectome Project's neuroimaging approach.
@en
prefLabel
The Human Connectome Project's neuroimaging approach.
@ast
The Human Connectome Project's neuroimaging approach.
@en
P2093
P2860
P50
P356
P1433
P1476
The Human Connectome Project's neuroimaging approach.
@en
P2093
Daniel S Marcus
Edward J Auerbach
Jesper L R Andersson
Mark Jenkinson
Matthew F Glasser
Michael P Harms
Steen Moeller
Stephen M Smith
Timothy E J Behrens
Timothy S Coalson
P2860
P2888
P304
P356
10.1038/NN.4361
P407
P577
2016-08-01T00:00:00Z