IEEE Trans Med Imaging. 2019 Apr;38(4):1058-1068. doi: 10.1109/TMI.2018.2877576. Epub 2018 Oct 23.
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 paper, 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 data sets of Human Connectome Project. Extensive experiment results demonstrate that the proposed DSRNN model can accurately identify the brain's state in different task fMRI data sets and significantly outperforms other auto-correlation methods or non-temporal approaches in the dynamic brain state recognition accuracy. In general, the proposed DSRNN offers a new methodology for basic neuroscience and clinical research.
大脑活动是不同感觉反应的动态组合，因此大脑活动/状态随时间不断变化。然而，在任务 fMRI 数据中，大脑的快速时间尺度上的动态功能状态识别很少被探索。在本文中，我们提出了一种新的 5 层深度稀疏递归神经网络（DSRNN）模型，以准确识别整个扫描会话中的大脑状态。具体来说，DSRNN 模型包括输入层、一个全连接层、两个递归层和一个 softmax 输出层。所提出的框架已在七个人类连接组计划的任务 fMRI 数据集上进行了测试。广泛的实验结果表明，所提出的 DSRNN 模型可以准确识别不同任务 fMRI 数据集中的大脑状态，并且在动态大脑状态识别准确性方面明显优于其他自相关方法或非时间方法。总的来说，所提出的 DSRNN 为基础神经科学和临床研究提供了一种新的方法。
