Although deep learning algorithms are a novel method for neuroimage analysis, at times they are used as a “black box” for classification task. Therefore, it is crucial to develop methods to comprehend the abstract features used for prediction. We have developed an interpretable deep learning algorithm to predict working memory scores from fMRI data; wherein prediction performance was compared to Kernel Ridge Regression, a traditional machine learning approach. Across all metrics of evaluation, our method outperformed KRR. Moreover, our method was able to create averaged saliency maps highlighting regions most predictive of working memory scores.
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