| The PhysIO toolbox for modeling physiological noise in fMRI data L Kasper, S Bollmann, AO Diaconescu, C Hutton, J Heinzle, S Iglesias, ... Journal of neuroscience methods 276, 56-72, 2017 | 270 | 2017 |
| DeepQSM-using deep learning to solve the dipole inversion for quantitative susceptibility mapping S Bollmann, KGB Rasmussen, M Kristensen, RG Blendal, LR Østergaard, ... Neuroimage 195, 373-383, 2019 | 134 | 2019 |
| Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD) SS Poil, S Bollmann, C Ghisleni, RL O’Gorman, P Klaver, J Ball, ... Clinical Neurophysiology 125 (8), 1626-1638, 2014 | 114 | 2014 |
| Developmental changes in gamma-aminobutyric acid levels in attention-deficit/hyperactivity disorder S Bollmann, C Ghisleni, SS Poil, E Martin, J Ball, D Eich-Höchli, ... Translational psychiatry 5 (6), e589-e589, 2015 | 80 | 2015 |
| Echo time‐dependent quantitative susceptibility mapping contains information on tissue properties S Sood, J Urriola, D Reutens, K O’Brien, S Bollmann, M Barth, V Vegh Magnetic resonance in medicine 77 (5), 1946-1958, 2017 | 74 | 2017 |
| Coupling between resting cerebral perfusion and EEG RL O’Gorman, SS Poil, D Brandeis, P Klaver, S Bollmann, C Ghisleni, ... Brain topography 26, 442-457, 2013 | 62 | 2013 |
| Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities W Jung, S Bollmann, J Lee NMR in Biomedicine 35 (4), e4292, 2022 | 53 | 2022 |
| Subcortical glutamate mediates the reduction of short-range functional connectivity with age in a developmental cohort C Ghisleni, S Bollmann, SS Poil, D Brandeis, E Martin, L Michels, ... Journal of Neuroscience 35 (22), 8433-8441, 2015 | 44 | 2015 |
| Assessment of microstructural signal compartments across the corpus callosum using multi-echo gradient recalled echo at 7 T K Thapaliya, V Vegh, S Bollmann, M Barth NeuroImage 182, 407-416, 2018 | 32 | 2018 |
| Effects of steroid hormones on sex differences in cerebral perfusion C Ghisleni, S Bollmann, A Biason-Lauber, SS Poil, D Brandeis, E Martin, ... PLoS One 10 (9), e0135827, 2015 | 32 | 2015 |
| Deep learning in magnetic resonance image reconstruction SS Chandra, M Bran Lorenzana, X Liu, S Liu, S Bollmann, S Crozier Journal of Medical Imaging and Radiation Oncology 65 (5), 564-577, 2021 | 31 | 2021 |
| SHARQnet–Sophisticated harmonic artifact reduction in quantitative susceptibility mapping using a deep convolutional neural network S Bollmann, MH Kristensen, MS Larsen, MV Olsen, MJ Pedersen, ... Zeitschrift für Medizinische Physik 29 (2), 139-149, 2019 | 31 | 2019 |
| Brainhack: Developing a culture of open, inclusive, community-driven neuroscience R Gau, S Noble, K Heuer, KL Bottenhorn, IP Bilgin, YF Yang, ... Neuron 109 (11), 1769-1775, 2021 | 23 | 2021 |
| Age-dependent and-independent changes in attention-deficit/hyperactivity disorder (ADHD) during spatial working memory performance S Bollmann, C Ghisleni, SS Poil, E Martin, J Ball, D Eich-Höchli, P Klaver, ... The World Journal of Biological Psychiatry, 1-12, 2015 | 23 | 2015 |
| The challenge of bias‐free coil combination for quantitative susceptibility mapping at ultra‐high field S Bollmann, SD Robinson, K O'Brien, V Vegh, A Janke, L Marstaller, ... Magnetic Resonance in Medicine 79 (1), 97-107, 2018 | 22 | 2018 |
| Centering inclusivity in the design of online conferences—An OHBM–Open Science perspective E Levitis, CDG Van Praag, R Gau, S Heunis, E DuPre, G Kiar, ... GigaScience 10 (8), giab051, 2021 | 21 | 2021 |
| Accelerated mapping of magnetic susceptibility using 3D planes‐on‐a‐paddlewheel (POP) EPI at ultra‐high field strength D Stäb, S Bollmann, C Langkammer, K Bredies, M Barth NMR in Biomedicine 30 (4), e3620, 2017 | 17 | 2017 |
| Pulsed arterial spin labelling at ultra-high field with a B 1 + -optimised adiabatic labelling pulse F Zimmer, K O’Brien, S Bollmann, J Pfeuffer, K Heberlein, M Barth Magnetic Resonance Materials in Physics, Biology and Medicine 29, 463-473, 2016 | 17 | 2016 |
| QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping AW Stewart, SD Robinson, K O’Brien, J Jin, G Widhalm, G Hangel, ... Magnetic resonance in medicine 87 (3), 1289-1300, 2022 | 12 | 2022 |
| Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning FL Ribeiro, S Bollmann, AM Puckett NeuroImage 244, 118624, 2021 | 12 | 2021 |
Markus BarthThe University of QueenslandVerified email at uq.edu.au
