Yury Koush

EEG-to-fMRI Prediction for Neurofeedback: Evaluating Regularized Regression and Clustering Approaches

The paper evaluates regularized regression and clustering approaches to predict fMRI activity from EEG data, finding that clustering improves predictability but generalization remains limited.

Long-term effects of network-based fMRI neurofeedback training for sustained attention

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

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

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.

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

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.

Distinct effects of amyloid and tau deposition on eigenvector centrality during hippocampal down-regulation: a real-time fMRI virtual reality closed-loop neurofeedback study with CSF biomarkers

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

The effect of APOE genotype and streamline density volume, on hippocampal CA1 down-regulation: a real-time fMRI virtual reality neurofeedback study

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