A Wasserstein Recipe for Replicable Machine Learning on Functional Neuroimages

Advances in neuroimaging have dramatically expanded our ability to probe the neurobiological bases of behavior in-vivo. Leveraging a growing repository of publicly available neuroimaging data, there is a surging interest for utilizing machine learning (ML) approaches to explore new questions in neuroscience. Despite the impressive achievements of current deep learning models, there remains an under-acknowledged risk: the variability in cognitive states may undermine the experimental replicability of the ML models, leading to potentially misleading findings in the realm of neuroscience. To address this challenge, we first dissect the critical (but often missed) challenge of ensuring the replicability of predictions despite task-irrelevant functional fluctuations. We then formulate the solution as a domain adaptation, where we design a dual-branch Transformer with minimizing Wasserstein distance. We evaluate the cognitive task recognition accuracy and consistency of test and retest functional neuroimages (serial imaging measures of the same cognitive task over a short period of time) of the Human Connectome Project. Our model demonstrates significant improvements in both replicability and accuracy of task recognition, showing the great potential of reliable deep models for solving real-world neuroscience problems.