Trevor Wei Kiat Tan

Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets

The paper evaluates how different censoring strategies affect cortical parcellations and TMS targets in rs-fMRI, finding that lenient censoring yields better results than strict censoring.

Network-based Near-Scalp Personalized Brain Stimulation Targets

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

Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?

This study compares brain age models and direct models for predicting Alzheimer's Disease outcomes, finding that direct models perform better despite brain age models having larger training data.

Longer scans boost prediction and cut costs in brain-wide association studies

The paper shows longer scans improve prediction accuracy in brain-wide association studies while reducing costs, with 30-minute scans being the most cost-effective.

Relationship Between Prediction Accuracy and Feature Importance Reliability: an Empirical and Theoretical Study

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.