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Lee Jollans
Lee Jollans
Centre for Medical Imaging and Visualization, Linköping University
Verified email at liu.se
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Year
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
1382019
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, 327-345, 2021
782021
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), 4643, 2021
612021
Neuromarkers for mental disorders: harnessing population neuroscience
L Jollans, R Whelan
Frontiers in psychiatry 9, 312450, 2018
592018
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
552018
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
452020
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, 346-363, 2018
412018
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
322017
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
322016
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
252021
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
222016
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 163, 22-34, 2021
212021
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
192018
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
152016
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
132020
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
82019
Neuromarkers for Mental Disorders: Harnessing Population Neuroscience. Front Psychiatry. 2018; 9: 242
L Jollans, R Whelan
42018
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
42015
Stress-primed secretory autophagy drives extracellular BDNF maturation
S Martinelli, EA Anderzhanova, S Wiechmann, F Dethloff, K Weckmann, ...
bioRxiv, 2020.05. 13.090514, 2020
12020
Predicting future drinking among young adults: using ensemble machine-learning to combine MRI with psychometrics and behaviour
MM Groefsema, M Luijten, RCME Engels, G Sescousse, L Jollans
bioRxiv, 2020.03. 03.974931, 2020
12020
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Articles 1–20