Harun Kocataş

This study identifies reliable and functionally relevant brain regions for tDCS to enhance novel-word learning and assesses test-retest reliability of behavioral and imaging outcomes.

The paper introduces a multimodal fMRI framework to guide tDCS target selection by integrating intrinsic connectivity and task-evoked activity patterns.

OSCAR detects condition-related deviations in brain connectivity using a normative modeling framework, identifying task-evoked and clinical network reorganization with high accuracy.

This study identifies functional brain networks involved in object-location learning using fMRI and shows how connectivity within these networks changes with learning performance.

The paper develops a deep learning model to automate electrode detection in tDCS-fMRI studies, outperforming manual and semi-automated methods in precision and reducing human bias.