| Serial correlations in single-subject fMRI with sub-second TR S Bollmann, AM Puckett, R Cunnington, M Barth NeuroImage 166, 152-166, 2018 | 72 | 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 | 51 | 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 | 48 | 2016 |
| The attentional field revealed by single-voxel modeling of fMRI time courses AM Puckett, EA DeYoe Journal of Neuroscience 35 (12), 5030-5042, 2015 | 47 | 2015 |
| Bayesian population receptive field modeling in human somatosensory cortex AM Puckett, S Bollmann, K Junday, M Barth, R Cunnington Neuroimage 208, 116465, 2020 | 39 | 2020 |
| 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 | 37 | 2018 |
| 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 | 27 | 2014 |
| Similar somatotopy for active and passive digit representation in primary somatosensory cortex ZB Sanders, DB Wesselink, H Dempsey-Jones, TR Makin BioRxiv, 754648, 2019 | 22 | 2019 |
| Predicting the retinotopic organization of human visual cortex from anatomy using geometric deep learning FL Ribeiro, S Bollmann, AM Puckett NeuroImage 244, 118624, 2021 | 13 | 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 | 13 | 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 | 11 | 2020 |
| 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 |
| 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 | 6 | 2023 |
| An explainability framework for cortical surface-based deep learning FL Ribeiro, S Bollmann, R Cunnington, AM Puckett arXiv preprint arXiv:2203.08312, 2022 | 5 | 2022 |
| Non-linear realignment using minimum deformation averaging for single-subject fMRI at ultra-high field S Bollmann, S Bollmann, A Puckett, A Janke, M Barth Proc. Intl. Soc. Mag. Reson. Med. ISMRM, Honolulu, 2017 | 5 | 2017 |
| 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 | 4 | 2022 |
| 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 | 4 | 2019 |
| 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 |
| Population Attentional Field Modeling E DeYoe, A Puckett, Y Ma Journal of Vision 13 (9), 232-232, 2013 | 1 | 2013 |
