Consciousness Level and Recovery Outcome Prediction Using High-Order Brain Functional Connectivity Network.

Xiuyi Jia, Han Zhang, Ehsan Adeli, Dinggang Shen
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引用次数: 5

Abstract

Based on the neuroimaging data from a large set of acquired brain injury patients, we investigate the feasibility of using machine learning for automatic prediction of individual consciousness level. Rather than using the traditional Pearson's correlation-based brain functional network, which measures only the simple temporal synchronization of the BOLD signals from each pair of brain regions, we construct a high-order brain functional network that is capable of characterizing topographical information-based high-level functional associations among brain regions. In such a high-order brain network, each node represents the community of a brain region, described by a set of this region's low-order functional associations with other brain regions, and each edge characterizes topographical similarity between a pair of such communities. Experimental results show that the high-order brain functional network enables a significant better classification for consciousness level and recovery outcome prediction.

Abstract Image

Abstract Image

利用高阶脑功能连接网络预测意识水平和恢复结果。
基于大量获得性脑损伤患者的神经影像学数据,我们研究了使用机器学习自动预测个体意识水平的可行性。传统的基于Pearson相关的脑功能网络仅测量来自每对脑区域的BOLD信号的简单时间同步,而不是使用传统的基于Pearson相关的脑功能网络,我们构建了一个高阶脑功能网络,能够表征脑区域之间基于地形信息的高级功能关联。在这样的高阶大脑网络中,每个节点代表一个大脑区域的社区,由该区域与其他大脑区域的一组低阶功能关联来描述,每个边缘表征一对这样的社区之间的地形相似性。实验结果表明,高阶脑功能网络在意识水平分类和恢复结果预测方面具有较好的效果。
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