Jerzy Bodurka

This study investigates neural responses to gastrointestinal sensations using a vibrating capsule, showing improved perceptual accuracy and correlated neural activity.

This study evaluates the impact of real-time fMRI denoising on brain activity and functional connectivity estimation, finding that certain denoising steps significantly reduce noise.

This paper develops a method to select electrode montages for transcranial electrical stimulation using fMRI and computational head models, showing improved connectivity and craving reduction in a specific montage.

This paper describes a closed-loop tES-fMRI protocol for brain modulation, demonstrating feasibility, safety, and the ability to distinguish physiological changes from artifacts.

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 paper predicts sex from resting-state fMRI using machine learning across multiple datasets, achieving high accuracy with low-frequency BOLD signal features.

This study examines the relationship between EEG microstate dynamics and fMRI signals, finding that activity regressors are mostly anticorrelated with fMRI in specific brain networks.

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 identifies a functional connectivity locus associated with rumination symptoms and optimizes a neurofeedback signal for real-time fMRI intervention.

The paper describes neuroimaging processing methods for the ABCD Study to extract brain phenotypes from multimodal imaging data.