Alexander M. Puckett

This study uses ultra-high field fMRI to identify columnar-like functional patterns in human somatosensory cortex through vibration stimulation and cortical surface modelling.

This paper introduces a deep learning tool to predict functional topography of the human visual cortex from anatomy, demonstrating its utility across diverse conditions and large-scale datasets.

This study shows significant individual variability in visual field maps of human V2 and V3, challenging the canonical model of their symmetric organization.

This study shows that primary somatosensory cortex digit representation is consistent between active and passive tasks, with active tasks better at fine-grained discrimination.

This paper develops a novel data-driven method to accurately map the retinotopic organization around the physiological blind spot in human visual cortex, outperforming conventional model-based approaches.

This paper develops a geometric deep learning model to predict retinotopic organization in human visual cortex from anatomical data, showing improved individual variation prediction.

The paper uses Bayesian population receptive field modeling to estimate somatosensory pRFs in human primary somatosensory cortex and finds larger representations for the index finger and larger pRF sizes for the little finger.

The paper introduces an anatomy-guided inverse-phase-encoding registration method to correct susceptibility artifacts in sub-millimeter fMRI, showing improved robustness and speed compared to existing methods.

This study investigates how venous artefacts affect retinotopic mapping of hV4, finding that correcting inverted BOLD responses can restore missing visual field coverage in some hemispheres.