Guilherme Maia de Oliveira Wood

The paper shows a shared neural architecture for finger movement encoding in the human sensorimotor cortex across individuals using fMRI and Procrustean transformation.

The paper investigates how training and transfer effects in motor learning are encoded differently in the sensorimotor cortex, revealing distinct neural mechanisms and shared information structures across individuals.

This study uses fMRI and vector autoregressive models to analyze information spillover between brain regions during resting and working memory states, finding that resting memory has significantly higher neural dependencies.

The paper estimates how much fMRI connectivity can be reliably detected given within-subject time course reliability and finds that less than a quarter of observed connectivity is reliably detectable.

This study shows that believing one can control brain activity during fake neurofeedback training activates similar brain regions as real neurofeedback, suggesting such activity alone does not prove learning.

The paper evaluates different fc-MRI pipelines for detecting brain connectivity, finding that Savitzky Golay filters improve true connectivity detection compared to SPM pipelines.