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2019 | OriginalPaper | Buchkapitel

Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation

verfasst von : Shuo Zhou, Christopher R. Cox, Haiping Lu

Erschienen in: Machine Learning in Medical Imaging

Verlag: Springer International Publishing

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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.

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Fußnoten
1
Now known as OpenNeuro. We will use the name OpenfMRI in the rest of this paper.
 
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Metadaten
Titel
Improving Whole-Brain Neural Decoding of fMRI with Domain Adaptation
verfasst von
Shuo Zhou
Christopher R. Cox
Haiping Lu
Copyright-Jahr
2019
DOI
https://doi.org/10.1007/978-3-030-32692-0_31