| Serial correlations in single-subject fMRI with sub-second TR S Bollmann, AM Puckett, R Cunnington, M Barth NeuroImage 166, 152-166, 2018 | 73 | 2018 |
| Measuring the effects of attention to individual fingertips in somatosensory cortex using ultra-high field (7T) fMRI AM Puckett, S Bollmann, M Barth, R Cunnington Neuroimage 161, 179-187, 2017 | 56 | 2017 |
| The spatiotemporal hemodynamic response function for depth-dependent functional imaging of human cortex AM Puckett, KM Aquino, PA Robinson, M Breakspear, MM Schira Neuroimage 139, 240-248, 2016 | 52 | 2016 |
| Bayesian population receptive field modeling in human somatosensory cortex AM Puckett, S Bollmann, K Junday, M Barth, R Cunnington Neuroimage 208, 116465, 2020 | 48 | 2020 |
| The attentional field revealed by single-voxel modeling of fMRI time courses AM Puckett, EA DeYoe Journal of Neuroscience 35 (12), 5030-5042, 2015 | 48 | 2015 |
| Using multi-echo simultaneous multi-slice (SMS) EPI to improve functional MRI of the subcortical nuclei of the basal ganglia at ultra-high field (7T) AM Puckett, S Bollmann, BA Poser, J Palmer, M Barth, R Cunnington NeuroImage 172, 886-895, 2018 | 42 | 2018 |
| Similar somatotopy for active and passive digit representation in primary somatosensory cortex ZB Sanders, H Dempsey‐Jones, DB Wesselink, LR Edmondson, ... Human Brain Mapping 44 (9), 3568-3585, 2023 | 37 | 2023 |
| An investigation of positive and inverted hemodynamic response functions across multiple visual areas AM Puckett, JR Mathis, EA DeYoe Human brain mapping 35 (11), 5550-5564, 2014 | 30 | 2014 |
| Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning FL Ribeiro, S Bollmann, AM Puckett NeuroImage 244, 118624, 2021 | 18 | 2021 |
| Manipulating the structure of natural scenes using wavelets to study the functional architecture of perceptual hierarchies in the brain AM Puckett, MM Schira, ZJ Isherwood, JD Victor, JA Roberts, ... NeuroImage 221, 117173, 2020 | 15 | 2020 |
| Susceptibility artifact correction for sub-millimeter fMRI using inverse phase encoding registration and T1 weighted regularization STM Duong, SL Phung, A Bouzerdoum, HGB Taylor, AM Puckett, ... Journal of Neuroscience Methods 336, 108625, 2020 | 15 | 2020 |
| Highly accurate retinotopic maps of the physiological blind spot in human visual cortex PWB Urale, AM Puckett, A York, D Arnold, DS Schwarzkopf Human Brain Mapping 43 (17), 5111-5125, 2022 | 11 | 2022 |
| An explainability framework for cortical surface-based deep learning FL Ribeiro, S Bollmann, R Cunnington, AM Puckett arXiv preprint arXiv:2203.08312, 2022 | 7 | 2022 |
| Bayesian population receptive field modeling in human somatosensory cortex. NeuroImage, 208, Article 116465 AM Puckett, S Bollmann, K Junday, M Barth, R Cunnington | 7 | 2019 |
| Non-linear realignment using minimum deformation averaging for single-subject fMRI at ultra-high field S Bollmann, S Bollmann, AM Puckett, A Janke, M Barth Proc. Intl. Soc. Mag. Reson. Med. ISMRM, Honolulu, 2017 | 6 | 2017 |
| Vascular effects on the BOLD response and the retinotopic mapping of hV4 HG Boyd Taylor, AM Puckett, ZJ Isherwood, MM Schira Plos one 14 (6), e0204388, 2019 | 5 | 2019 |
| Human Retinotopic Mapping: from Empirical to Computational Models of Retinotopy FL Ribeiro, NC Benson, AM Puckett | 1 | 2024 |
| Variability of visual field maps in human early extrastriate cortex challenges the canonical model of organization of V2 and V3 FL Ribeiro, A York, E Zavitz, S Bollmann, MGP Rosa, A Puckett Elife 12, e86439, 2023 | 1 | 2023 |
| Predicting the functional organization of human visual cortex from anatomy using geometric deep learning A Puckett, S Bollmann, F Ribeiro Journal of Vision 20 (11), 928-928, 2020 | 1 | 2020 |
| DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning FL Ribeiro, S Bollmann, AM Puckett arXiv preprint arXiv:2005.12513, 2020 | 1 | 2020 |
