Advertisement
Published in:
2019 | OriginalPaper | Chapter
Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation
Authors : Shuo Zhou, Christopher R. Cox, Haiping Lu
Published in: Machine Learning in Medical Imaging
Publisher: Springer International Publishing
Activate our intelligent search to find suitable subject content or patents.
Select sections of text to find matching patents with Artificial Intelligence. powered by
Select sections of text to find additional relevant content using AI-assisted search. powered by
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.