Decoding specific task states from human fMRI ( functional magnetic resonance imaging ) signals constitutes a major goal of neuroimaging. Previous studies of task-state classification have focused largely on processed fMRI characteristics (e.g. functional connectivity patterns, activity patterns) or a single fMRI volume. Here, we assembled a novel deep neural network (DNN) for classifying directly from 4-D fMRI time series, retaining both spatial and temporal information of fMRI signals . We trained and tested the DNN classifier by task fMRI data from human connectome project (HCP) S1200 dataset . Reading just a single block of fMRI time series of each task for each individual, the novel classification method identified the seven tasks with 91.75\% accuracy in test. Then we extracted task heatmaps of each time series by a guided backpropagation approach, and found that the heatmaps had a high similarity with the group average result of traditional contrast of parameter estimate (COPE). Furthermore, the DNN could be applied to small dataset. The features from the intermediate layer of the DNN were trained and used to train a support vector machine (SVM) to classify four conditions within 300 blocks of task-fMRI scans, data amount comparable to a usual neuroimaging research, and obtained a 95\% accuracy. Our reported findings were the first to demonstrate the ability of DNN to classify and extract heatmaps from a 4-D fMRI time series, as well as its ability to be transfer trained by a small dataset. This approach should enable decoding and mapping subject's task status and furthermore help with classification and diagnosis of neuropsychiatric diseases .
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 knowledge and 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 .
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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