Lee Jollans
Lee Jollans
Department of Translational Research in Psychiatry, Max Planck Institute for Psychiatry
Verified email at psych.mpg.de
Cited by
Cited by
Quantifying performance of machine learning methods for neuroimaging data
L Jollans, R Boyle, E Artiges, T Banaschewski, S Desrivières, A Grigis, ...
NeuroImage 199, 351-365, 2019
Neural circuitry underlying sustained attention in healthy adolescents and in ADHD symptomatology
L O'Halloran, Z Cao, K Ruddy, L Jollans, MD Albaugh, A Aleni, AS Potter, ...
Neuroimage 169, 395-406, 2018
Neuromarkers for mental disorders: Harnessing population neuroscience
L Jollans, R Whelan
Frontiers in psychiatry 9, 242, 2018
The clinical added value of imaging: a perspective from outcome prediction
L Jollans, R Whelan
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 1 (5), 423-432, 2016
The effects of a single whole-body cryotherapy exposure on physiological, performance, and perceptual responses of professional academy soccer players after repeated sprint …
M Russell, J Birch, T Love, CJ Cook, RM Bracken, T Taylor, E Swift, ...
The Journal of Strength & Conditioning Research 31 (2), 415-421, 2017
The potential of neuroimaging for identifying predictors of adolescent alcohol use initiation and misuse
L O'Halloran, C Nymberg, L Jollans, H Garavan, R Whelan
Addiction 112 (4), 719-726, 2017
Machine learning EEG to predict cognitive functioning and processing speed over a 2-year period in multiple sclerosis patients and controls
H Kiiski, L Jollans, SÓ Donnchadha, H Nolan, R Lonergan, S Kelly, ...
Brain topography 31 (3), 346-363, 2018
Brain-predicted age difference score is related to specific cognitive functions: a multi-site replication analysis
R Boyle, L Jollans, LM Rueda-Delgado, R Rizzo, GG Yener, ...
Brain imaging and behavior 15 (1), 327-345, 2021
Ventral striatum connectivity during reward anticipation in adolescent smokers
L Jollans, C Zhipeng, I Icke, C Greene, C Kelly, T Banaschewski, ...
Developmental neuropsychology 41 (1-2), 6-21, 2016
Computational EEG Modelling of Decision Making Under Ambiguity Reveals Spatio-Temporal Dynamics of Outcome Evaluation
L Jollans, R Whelan, L Venables, OH Turnbull, M Cella, S Dymond
Behavioural Brain Research, 2016
The biological classification of mental disorders (BeCOME) study: a protocol for an observational deep-phenotyping study for the identification of biological subtypes
TM Brückl, VI Spoormaker, PG Sämann, AK Brem, L Henco, D Czamara, ...
BMC psychiatry 20, 1-25, 2020
A combination of impulsivity subdomains predict alcohol intoxication frequency
L O'Halloran, B Pennie, L Jollans, H Kiiski, N Vahey, L Rai, L Bradley, ...
Alcoholism: Clinical and Experimental Research 42 (8), 1530-1540, 2018
Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis
R Boyle, L Jollans, LM Rueda-Delgado, R Rizzo, GG Yener, ...
bioRxiv, 652867, 2019
Inhibitory‐control event‐related potentials correlate with individual differences in alcohol use
L O'Halloran, LM Rueda‐Delgado, L Jollans, Z Cao, R Boyle, C Vaughan, ...
Addiction biology 25 (2), e12729, 2020
Brain event-related potentials predict individual differences in inhibitory control
LM Rueda-Delgado, L O'Halloran, N Enz, KL Ruddy, H Kiiski, M Bennett, ...
International journal of psychophysiology, 2019
Embracing diversity and inclusivity in an academic setting: Insights from the Organization for Human Brain Mapping
A Tzovara, I Amarreh, V Borghesani, MM Chakravarty, E DuPre, ...
NeuroImage 229, 117742, 2021
A method for the optimisation of feature selection with imaging data
L Jollans, R Watts, D Duffy, P Spechler, H Garavan, R Whelan, ...
Poster presented at the Organisation of Human Brain Mapping Annual Meeting, 2015
Individual differences in learning from probabilistic reward and punishment predicts smoking status
LA Rai, L O'Halloran, L Jollans, N Vahey, C O'Brolchain, R Whelan
Addictive behaviors 88, 73-76, 2019
Stress-primed secretory autophagy promotes extracellular BDNF maturation by enhancing MMP9 secretion
S Martinelli, EA Anderzhanova, T Bajaj, S Wiechmann, F Dethloff, ...
Nature Communications 12 (1), 1-17, 2021
P. 232 Cortical thickness variation in major depressive disorder is indicative of symptom profiles: a data-driven approach
L Jollans, P Sämann, N Rost, T Brückl, E Binder
European Neuropsychopharmacology 31, S42-S43, 2020
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