1 Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia .
2 Department of Bioengineering, University of California , Riverside, Riverside, California.
Brain Connect. 2018 May;8(4):197-204. doi: 10.1089/brain.2017.0561. Epub 2018 Apr 25.
Individual identification based on brain function has gained traction in literature. Investigating individual differences in brain function can provide additional insights into the brain. In this work, we introduce a recurrent neural network-based model for identifying individuals based on only a short segment of resting-state functional magnetic resonance imaging data. In addition, we demonstrate how the global signal and differences in atlases affect individual identifiability. Furthermore, we investigate neural network features that exhibit the uniqueness of each individual. The results indicate that our model is able to identify individuals based on neural features and provides additional information regarding brain dynamics.
基于大脑功能的个体识别在文献中得到了关注。研究大脑功能的个体差异可以为大脑提供更多的见解。在这项工作中，我们引入了一种基于循环神经网络的模型，仅使用短段静息态功能磁共振成像数据就能识别个体。此外，我们还展示了全局信号和图谱差异如何影响个体可识别性。此外，我们还研究了表现出每个个体独特性的神经网络特征。结果表明，我们的模型能够基于神经特征识别个体，并提供有关大脑动态的附加信息。
