Recognizing Brain States Using Deep Sparse Recurrent Neural Network - Wang, Han (IEEE Trans Med Imaging, 2018)
Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain’s dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this work, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers and a softmax output layer. The proposed framework has been tested on seven task fMRI datasets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain’s state in different task fMRI datasets and significantly outperforms other auto-correlation methods or non-temporal approaches in dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research.