Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Ashburner

Showing results (191-200 of 483) with videos related to

Pageof 49
Sort By:
Neuroimage|March 30, 2010
Kernel regression for fMRI pattern predictionCarlton Chu, Yizhao Ni, Geoffrey Tan, et al.
Neuroimage|November 8, 2001
Learning arbitrary visuomotor associations: temporal dynamic of brain activityI Toni, N Ramnani, O Josephs, et al.
The Journal of Ambulatory Care Management|September 3, 2014
An interactive, all-payer, multidomain primary care performance dashboardCharlotte E Ward, Lisa Morella, Jeffrey M Ashburner, et al.
Cell|February 1, 1978
Drosophila: the genetics of two major larval proteinsM E Akam, D B Roberts, G P Richards, et al.
Journal of Neuroscience Methods|March 7, 2016
The first step for neuroimaging data analysis: DICOM to NIfTI conversionXiangrui Li, Paul S Morgan, John Ashburner, et al.
Neuroimage|May 1, 1997
MRI and PET coregistration--a cross validation of statistical parametric mapping and automated image registrationS J Kiebel, J Ashburner, J B Poline, et al.
Neuroimage|February 7, 1998
Incorporating prior knowledge into image registrationJ Ashburner, P Neelin, D L Collins, et al.
Neuroimage|June 5, 2018
A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methodsGemma C Monté-Rubio, Carles Falcón, Edith Pomarol-Clotet, et al.
Neuroimage|July 6, 2000
Optimization of 3-D MP-RAGE sequences for structural brain imagingR Deichmann, C D Good, O Josephs, et al.
Plos One|July 28, 2009
Prognostic and diagnostic potential of the structural neuroanatomy of depressionSergi G Costafreda, Carlton Chu, John Ashburner, et al.
Pageof 49

Showing results (191-200 of 483) with videos related to

Sort By:
Pageof 49
Neuroimage|March 30, 2010
Kernel regression for fMRI pattern predictionCarlton Chu, Yizhao Ni, Geoffrey Tan, et al.
Neuroimage|November 8, 2001
Learning arbitrary visuomotor associations: temporal dynamic of brain activityI Toni, N Ramnani, O Josephs, et al.
The Journal of Ambulatory Care Management|September 3, 2014
An interactive, all-payer, multidomain primary care performance dashboardCharlotte E Ward, Lisa Morella, Jeffrey M Ashburner, et al.
Cell|February 1, 1978
Drosophila: the genetics of two major larval proteinsM E Akam, D B Roberts, G P Richards, et al.
Journal of Neuroscience Methods|March 7, 2016
The first step for neuroimaging data analysis: DICOM to NIfTI conversionXiangrui Li, Paul S Morgan, John Ashburner, et al.
Neuroimage|May 1, 1997
MRI and PET coregistration--a cross validation of statistical parametric mapping and automated image registrationS J Kiebel, J Ashburner, J B Poline, et al.
Neuroimage|February 7, 1998
Incorporating prior knowledge into image registrationJ Ashburner, P Neelin, D L Collins, et al.
Neuroimage|June 5, 2018
A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methodsGemma C Monté-Rubio, Carles Falcón, Edith Pomarol-Clotet, et al.
Neuroimage|July 6, 2000
Optimization of 3-D MP-RAGE sequences for structural brain imagingR Deichmann, C D Good, O Josephs, et al.
Plos One|July 28, 2009
Prognostic and diagnostic potential of the structural neuroanatomy of depressionSergi G Costafreda, Carlton Chu, John Ashburner, et al.
Pageof 49