A heterogeneous accelerator platform for multi-subject voxel-based brain network analysis

Yu Wang, Mo Xu, Ling Ren, Xiaorui Zhang, Di Wu, Yong He, Ningyi Xu, Huazhong Yang
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引用次数: 5

Abstract

The research on understanding the human brain has attracted more and more attention. A promising method is to model the brain as a network based on modern imaging technologies and then to apply graph theory algorithms for analysis. In this work, we examine the computing bottleneck of this method, and propose a CPU-GPU heterogeneous platform to accelerate the process. We construct a statistical brain network from a sample of 198 people and get characteristics such as nodal degree and modularity. This is the first study of voxel-based brain networks on large samples. We also illustrate that domain-specific hardware platform can have a significant impact on neuroscience studies.
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基于多主体体素的脑网络分析异构加速平台
了解人类大脑的研究越来越受到人们的关注。一种很有前途的方法是基于现代成像技术将大脑建模为一个网络,然后应用图论算法进行分析。在这项工作中,我们研究了这种方法的计算瓶颈,并提出了一个CPU-GPU异构平台来加速这一过程。我们从198人的样本中构建了一个统计脑网络,得到了节点度和模块性等特征。这是首次在大样本上对基于体素的大脑网络进行研究。我们还说明了特定领域的硬件平台可以对神经科学研究产生重大影响。
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