Leonard Sasse

cuBNM accelerates brain network simulations using GPUs, enabling large-scale studies of neural processes previously limited by computational costs.

This study compares voxel-wise and region-wise denoising in functional connectivity analyses, finding region-level denoising offers equal or better performance with reduced computational cost.

This study examines how the number of gradients used in Procrustes alignment affects identification accuracy and differential identifiability in functional connectivity analyses across multiple fMRI datasets.

This study compares the predictive power of resting-state fMRI features and individual characteristics for cognitive phenotypes in 20,000 UK Biobank subjects, finding individual characteristics generally perform better.

The paper evaluates how different levels of co-fluctuation in functional connectivity predict individual-level phenotypes using machine learning.