Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

Abstract

In neural decoding, there has been a growing interest in machine learning on functional magnetic resonance imaging (fMRI). However, the size discrepancy between the whole-brain feature space and the training set poses serious challenges. Simply increasing the number of training examples is infeasible and costly. In this paper, we propose a domain adaptation framework for whole-brain fMRI (DawfMRI) to improve whole-brain neural decoding on target data leveraging source data. DawfMRI consists of two steps: (1) source and target feature adaptation, and (2) source and target classifier adaptation. We evaluate its four possible variations, using a collection of fMRI datasets from OpenfMRI. The results demonstrated that appropriate choices of source domain can help improve neural decoding accuracy for challenging classification tasks. The best-case improvement is $$10.47\%$$ (from $$77.26\%$$ to $$87.73\%$$ ). Moreover, visualising and interpreting voxel weights revealed that the adaptation can provide additional insights into neural decoding.