Christopher Lee Asplund

This paper develops a tree-based algorithm to identify personalized TMS targets for depression and anxiety by optimizing functional connectivity and scalp proximity.

This study validates time-resolved fMRI for detecting sub-second timing differences in neural responses by testing how stimulus duration and intensity affect hemodynamic signals.

This study investigates the relationship between prediction accuracy and feature importance reliability in neuroimaging data, showing that feature importance reliability can be high with sufficient sample size and is positively correlated with prediction accuracy.

The paper constructs two connectome-based predictive models (CPMs) to predict performance in attention tasks, finding that networks associated with better visual attentional blink performance predict worse performance in other attention tasks.