Keywords: Machine Learning/Artificial Intelligence, fMRI (task based)Deep learning, especially convolutional neural networks (CNN), has been shown to be able to identify the non-linear relation between functional magnetic resonance imaging (fMRI) and task performance. CNN can generate an interpretable result called saliency map highlighting regions that are important for task performance. It can uncover other neural processes that linear modeling cannot due to the high dimensionality nature of the fMRI. The CNN result can be presented as a saliency Previously, we developed a pipeline to produce the saliency map for working memory tasks. In this work, we further evaluated the repeatability of our pipeline.
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