Naren Wulan

DeepResBat improves MRI data harmonization by addressing covariate distribution differences, outperforming existing methods while avoiding false associations.

This paper adapts meta-matching frameworks to translate phenotypic prediction models from large to small MRI datasets, showing significant performance improvements over classical methods.

The paper introduces multilayer meta-matching to improve phenotypic prediction in small datasets by translating models from multiple large datasets, outperforming classical methods and transfer learning.

This study compares parcellation and gradient methods for predicting behavior from resting-state functional connectivity in two datasets, finding that individual-specific hard parcellations perform best in HCP and principal gradients perform similarly in ABCD.