Olufunsho K. Faseyitan

This paper develops a machine learning model to predict post-stroke aphasia speech performance using multimodal data, achieving high accuracy with explainable features.

This study investigates how damage to primary somatosensory cortex affects motor networks in humans, showing altered activation in subcortical areas and potential reorganization of motor function.

This paper shows that baseline TMS-MEP input-output curve parameters can predict individual responses to cTBS, with MEPmax and S50 being significant predictors.

The paper uses support vector regression to predict individual cognitive control behaviors from functional connectivity between brain networks during tasks and rest.

The paper uses GluCEST MRI to detect glutamate concentration changes in the motor cortex after cTBS, finding decreased ipsilateral signal but no consistent contralateral or sham effects.

This study compares multivariate sparse canonical correlation analysis (SCCAN) with mass-univariate voxel-wise lesion-symptom mapping (VLSM) and finds SCCAN provides more accurate results across various scenarios.