Kelvin O. Lim

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

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

This study introduces a method to analyze explicitly nonlinear dynamic functional connectivity in resting-state fMRI data and finds significant group differences between schizophrenia patients and healthy controls.

This paper introduces a method to quantify nonlinear functional connectivity in resting-state fMRI data, showing distinct nonlinear patterns in schizophrenia patients compared to healthy controls.