Stavros Skouras

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.

The paper investigates how amyloid and tau levels in CSF affect eigenvector centrality during hippocampal down-regulation using real-time fMRI neurofeedback.

This study links APOE genotype and hippocampal structure to neurofeedback performance in preclinical Alzheimer's disease risk individuals.

This study examines how psychiatric history and age affect neurofeedback performance on default mode network regulation using real-time fMRI data.

This paper identifies disease-stage-specific patterns of functional degradation and compensation in Alzheimer’s disease using eigenvector centrality mapping and CSF biomarkers.