Multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. However, the number of dimensions of fMRI signals are too large (hundreds of thousands of voxels in one volume) to be efficiently learnt by the MVPA. Researchers thus need to reduce the features to tens or hundreds of dimensions through feature selection/extraction that requires expert knowledgeand may lead to a selection bias . In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N=1034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7\%. We also showed the general applicability of the DNN for transfer learning to small datasets (N=43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89 \% and 94.7\% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2\% and 68.6\% obtained by the MVPA. A visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.
Support vector machine (SVM) based multivariate pattern analysis (MVPA) has delivered promising performance in decoding specific task states based on functional magnetic resonance imaging (fMRI) of the human brain. Conventionally, the SVM-MVPA requires careful feature selection/extraction according to expert knowledge . In this study, we propose a deep neural network (DNN) for directly decoding multiple brain task states from fMRI signals of the brain without any burden for feature handcrafts. We trained and tested the DNN classifier using task fMRI data from the Human Connectome Project's S1200 dataset (N=1034). In tests to verify its performance, the proposed classification method identified seven tasks with an average accuracy of 93.7\%. We also showed the general applicability of the DNN for transfer learning to small datasets (N=43), a situation encountered in typical neuroscience research. The proposed method achieved an average accuracy of 89.0 \% and 94.7\% on a working memory task and a motor classification task, respectively, higher than the accuracy of 69.2\% and 68.6\% obtained by the SVM-MVPA. A network visualization analysis showed that the DNN automatically detected features from areas of the brain related to each task. Without incurring the burden of handcrafting the features, the proposed deep decoding method can classify brain task states highly accurately, and is a powerful tool for fMRI researchers.
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Multivariate
Support vector machine (SVM) based multivariate
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However, the number of dimensions of fMRI signals are too large (hundreds of thousands of voxels in one volume) to be efficiently learnt by the MVPA. Researchers thus need to reduce the features to tens or hundreds of dimensions through
Conventionally, the SVM-MVPA requires careful
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that requires expert knowledgeand may lead to a selection bias
according to expert knowledge
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89
89.0
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MVPA. A
SVM-MVPA. A network