Andrew Morgan

This study investigates the neural mechanisms of afterimages using fMRI, finding that they engage widespread brain activity and specific cortical layers, challenging previous views on their origins.

This paper identifies and mitigates Fuzzy Ripple artifacts in high-resolution fMRI, enabling sub-millimeter resolution and faster acquisition protocols.

This paper introduces a rapid fMRI acquisition method, T1234, that provides distortion-matched structural reference data, improving registration accuracy with functional scans.

The paper investigates how real and imaginary experiences are processed in V1 using fMRI and pattern analysis, finding distinct decoding depths for illusions and imagery.

The paper investigates the relationship between visual imagery vividness and afterimage perception, finding a positive correlation between imagery vividness and afterimage contrast and sharpness.

The paper shows that V1 neurons in monkeys and humans respond to occluded scenes using contextual information, with representations resembling those in humans.

This paper compares a self-supervised CNN for image completion with fMRI data from early visual cortex, finding the CNN's decoder pathway more similar to brain activity than a supervised model.

LayNii is a software suite designed for analyzing layer-specific functional MRI data with SNR and coverage constraints, offering tools for layerification, smoothing, and artifact mitigation.

This study assesses the reproducibility of 7 Tesla fMRI hand digit representation across multiple sites and within sites, finding lower inter-site than intra-site reproducibility but overall feasibility for multi-site studies.

This study evaluates the reproducibility of 7T quantitative susceptibility mapping (QSM) and R2* at multiple sites using a harmonized protocol, finding improved consistency compared to 3T.

This study demonstrates line-scanning fMRI as a high-resolution non-invasive method to map functional properties of human cortical layers during visual stimulation.

The paper uses fMRI data to show that internal visual models in early cortex are categorical and scene-specific, and that line drawings effectively describe their structure.