Ghazaleh Soleimani

This study demonstrates that real-time fMRI-guided tACS can optimize brain stimulation to modulate frontoparietal connectivity and improve working memory accuracy.

This paper uses machine learning on fMRI data to predict craving intensity in methamphetamine users, achieving significant results with PCA and linear regression.

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.