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

Lee Jollans

Showing results (1-10 of 19) with videos related to

Pageof 2
Sort By:
Biological Psychiatry. Cognitive Neuroscience and Neuroimaging|March 22, 2018
The Clinical Added Value of Imaging: A Perspective From Outcome PredictionLee Jollans, Robert Whelan
Frontiers in Psychiatry|June 22, 2018
Neuromarkers for Mental Disorders: Harnessing Population NeuroscienceLee Jollans, Robert Whelan
European Heart Journal. Imaging Methods and Practice|March 7, 2025
Accurate fully automated assessment of left ventricle, left atrium, and left atrial appendage function from computed tomography using deep learningLee Jollans, Mariana Bustamante, Lilian Henriksson, et al.
Addiction (Abingdon, England)|December 6, 2016
The potential of neuroimaging for identifying predictors of adolescent alcohol use initiation and misuseLaura O'Halloran, Charlotte Nymberg, Lee Jollans, et al.
Addictive Behaviors|August 28, 2018
Individual differences in learning from probabilistic reward and punishment predicts smoking statusLaura A Rai, Laura O'Halloran, Lee Jollans, et al.
Behavioural Brain Research|December 31, 2016
Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluationLee Jollans, Robert Whelan, Louise Venables, et al.
Alcoholism, Clinical and Experimental Research|June 16, 2018
A Combination of Impulsivity Subdomains Predict Alcohol Intoxication FrequencyLaura O'Halloran, Brian Pennie, Lee Jollans, et al.
Addiction Biology|March 29, 2019
Inhibitory-control event-related potentials correlate with individual differences in alcohol useLaura O'Halloran, Laura M Rueda-Delgado, Lee Jollans, et al.
Neuroimage|June 8, 2019
Quantifying performance of machine learning methods for neuroimaging dataLee Jollans, Rory Boyle, Eric Artiges, et al.
Brain Topography|January 31, 2018
Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and ControlsHanni Kiiski, Lee Jollans, Seán Ó Donnchadha, et al.
Pageof 2

Showing results (1-10 of 19) with videos related to

Sort By:
Pageof 2
Biological Psychiatry. Cognitive Neuroscience and Neuroimaging|March 22, 2018
The Clinical Added Value of Imaging: A Perspective From Outcome PredictionLee Jollans, Robert Whelan
Frontiers in Psychiatry|June 22, 2018
Neuromarkers for Mental Disorders: Harnessing Population NeuroscienceLee Jollans, Robert Whelan
European Heart Journal. Imaging Methods and Practice|March 7, 2025
Accurate fully automated assessment of left ventricle, left atrium, and left atrial appendage function from computed tomography using deep learningLee Jollans, Mariana Bustamante, Lilian Henriksson, et al.
Addiction (Abingdon, England)|December 6, 2016
The potential of neuroimaging for identifying predictors of adolescent alcohol use initiation and misuseLaura O'Halloran, Charlotte Nymberg, Lee Jollans, et al.
Addictive Behaviors|August 28, 2018
Individual differences in learning from probabilistic reward and punishment predicts smoking statusLaura A Rai, Laura O'Halloran, Lee Jollans, et al.
Behavioural Brain Research|December 31, 2016
Computational EEG modelling of decision making under ambiguity reveals spatio-temporal dynamics of outcome evaluationLee Jollans, Robert Whelan, Louise Venables, et al.
Alcoholism, Clinical and Experimental Research|June 16, 2018
A Combination of Impulsivity Subdomains Predict Alcohol Intoxication FrequencyLaura O'Halloran, Brian Pennie, Lee Jollans, et al.
Addiction Biology|March 29, 2019
Inhibitory-control event-related potentials correlate with individual differences in alcohol useLaura O'Halloran, Laura M Rueda-Delgado, Lee Jollans, et al.
Neuroimage|June 8, 2019
Quantifying performance of machine learning methods for neuroimaging dataLee Jollans, Rory Boyle, Eric Artiges, et al.
Brain Topography|January 31, 2018
Machine Learning EEG to Predict Cognitive Functioning and Processing Speed Over a 2-Year Period in Multiple Sclerosis Patients and ControlsHanni Kiiski, Lee Jollans, Seán Ó Donnchadha, et al.
Pageof 2