Madeleine E. Sharp

The paper quantifies numerical variability in MRI analyses of Parkinson's disease and shows it can significantly affect statistical conclusions and p-values.

The paper investigates whether slow spindle trains during daytime naps support declarative memory consolidation, finding that slow spindles clustered in trains correlate with better memory performance.

This study examines how different versions of FreeSurfer affect structural brain measure estimation in Parkinson's disease and shows significant software-induced variability impacting clinical outcomes.

This study investigates the relationship between sleep spindles and memory consolidation in Parkinson's disease patients, finding that lower spindle density and reduced temporal clustering are associated with worse memory consolidation.

This study replicates findings from Hanganu et al. (2014) on Parkinson's disease neuroimaging biomarkers using PPMI data, finding partial replication of cortical thinning and cognitive decline associations.

This study attempts to reproduce and replicate a machine learning model for predicting Parkinson's disease progression using MRI data, finding lower performance than the original study.

This study investigates motor memory in Parkinson's patients and finds it is preserved, not impaired, and not influenced by dopamine state during initial learning.

This study investigates whether Parkinson's disease patients can use reward information to guide attention allocation and finds they cannot, with dopamine replacement having no effect.

This study investigates how dopamine affects dynamic striatal-cortical connectivity during learning in Parkinson’s disease patients, finding dopamine-dependent changes in network flexibility and connectivity.