Tong He

This paper introduces a goal-specific MRI harmonization framework that improves downstream prediction performance by integrating application-specific constraints during harmonization.

The paper introduces a framework called meta-matching to improve prediction performance in small datasets by leveraging correlations with large-scale phenotypic data.

This paper predicts Alzheimer's disease progression using a minimal recurrent neural network, showing better performance than baseline methods when handling missing data with model-based imputation.

This study identifies convergent molecular, cellular, and neural signatures of major depressive disorder across multiple datasets and modalities.

This study compares deep neural networks and kernel regression in predicting behavior and demographics from resting-state functional connectivity data, finding comparable performance with kernel regression being more computationally efficient.

This paper evaluates methods for mapping between MNI152/Colin27 and fsaverage coordinate systems, finding that registration fusion with ANTs performs best for new subjects.