Gustavo Santo Pedro Pamplona

This study predicts children's literacy from task-based fMRI by comparing task types and contrasts, finding active tasks and multisensory learning tasks yield better predictive performance.

The paper reanalyzes passive finger movement fMRI data using univariate and multivariate methods to show that multivariate analysis better identifies brain regions encoding kinesthesia and proprioception, with larger neural representation for amplitude and direction than velocity.

This paper analyzes fMRI neurofeedback data to identify brain regions and networks involved in feedback processing and regulation recalibration during training.

This paper reviews methods for predicting human intelligence from neuroimaging data, finding differences in performance between general and fluid intelligence predictions.

This study evaluates the long-term effects of network-based fMRI neurofeedback training on sustained attention, finding lasting neural changes but transient behavioral improvements.

This study uses machine learning to analyze 608 participants and identifies two factors improving neurofeedback performance: pre-training no-feedback runs and training patients over healthy participants.

This study investigates whether pre-training brain activity can predict success in real-time fMRI neurofeedback learning across 24 studies and 401 participants, finding a weak correlation but no consistent predictors.