Zhou, S., Cox, C. and Lu, H. orcid.org/0000-0002-0349-2181 (Submitted: 2018) Improving whole-brain neural decoding of fMRI with domain adaptation. bioRxiv. (Submitted)
Abstract
In neural decoding, there has been a growing interest in machine learning on whole-brain functional magnetic resonance imaging (fMRI). However, the size discrepancy between the feature space and the training set poses serious challenges. Simply increasing the number of training examples is infeasible and costly. In this paper, we proposed a domain adaptation framework for whole-brain fMRI (DawfMRI) to improve whole-brain neural decoding on target data leveraging pre-existing source data. DawfMRI consists of three steps: 1) feature extraction from whole-brain fMRI, 2) source and target feature adaptation, and 3) source and target classifier adaptation. We evaluated its eight possible variations, including two non-adaptation and six adaptation algorithms, using a collection of seven task-based fMRI datasets (129 unique subjects and 11 cognitive tasks in total) from the OpenNeuro project. The results demonstrated that appropriate source domain can help improve neural decoding accuracy for challenging classification tasks. The best-case improvement is 8.94% (from 78.64% to 87.58%). Moreover, we discovered a plausible relationship between psychological similarity and adaptation effectiveness. Finally, visualizing and interpreting voxel weights showed that the adaptation can provide additional insights into neural decoding.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The Author(s). For reuse permissions, please contact the Author(s). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Oct 2018 13:01 |
Last Modified: | 02 Oct 2018 12:26 |
Published Version: | https://www.biorxiv.org/content/early/2018/08/07/3... |
Status: | Submitted |
Identification Number: | 10.1101/375030 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:136411 |