Multi-source brain computing with systematic fusion for smart health
Multi-source brain computing with systematic fusion for smart health
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DOI：
10.1016/j.inffus.2021.03.009
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发表时间：
2021-05-11
影响因子：
18.6
通讯作者：
Wang, Haiyuan
中科院分区：
文献类型：
--
作者：
Kuai, Hongzhi;Zhong, Ning;Wang, Haiyuan
随着人工智能、大数据和功能神经影像技术的进步，脑计算迅速推进了我们对脑智能和脑疾病的理解。我们认为，现有的数据分析方法在处理多个脑大数据源时，对于脑计算来说已经不够了，因为这些方法主要侧重于扁平化策略，不能很好地系统地理解认知、情感和疾病的构成要素，以及它们之间的内部和相互关系。为了解决这个问题，我们在本文中提出了一种通过数据脑驱动的系统融合的新型多源脑计算平台。首先，我们将围绕基于脑信息学的调查过程的一系列行为形式化，并提出一个概念模型来系统地表示功能神经影像数据的内容和背景。然后，我们提出了多方面融合和推理的系统脑计算框架，以了解大脑特异性并给出不确定性量化，以及其对脑健康转化研究的启示和应用。特别是，引入了基于图匹配的任务搜索算法，以帮助系统化的实验设计和多个认知任务的数据采样。该研究通过考虑证据组合的影响来推断和测试多个假设，从而提高了脑计算结果的可解释性和透明度。最后，以KID循环为思维空间，驱动知识（K）、信息（I）和数据（D）的多种来源，在互联的社会网络物理空间中激发永无止境的学习和多维交互。实验结果证明了所提出的系统融合脑计算方法的有效性。
With the progress of artificial intelligence, big data and functional neuroimaging technologies, brain computing has rapidly advanced our understanding of brain intelligence and brain disorders. We argue that existing data analytical methods have become insufficient for brain computing when dealing with multiple brain big data sources, because such methods mainly focus on flattening strategies and fail to work well for systematic understanding of the constituent elements of cognition, emotion and disease, as well as the intraand inter-relations within and among themselves. To address this problem, we present in this paper a novel multi-source brain computing platform by Data-Brain driven systematic fusion. First, we formalize a series of behaviors surrounding the Brain Informatics-based investigation process, and present a conceptual model to systematically represent content and context of functional neuroimaging data. Then, we propose the systematic brain computing framework with multi-aspect fusion and inference to understand brain specificity and give uncertainty quantification, as well as its inspiration and applications for translational studies on brain health. In particular, a graph matching-based task search algorithm is introduced to help systematic experimental design and data sampling with multiple cognitive tasks. The study increases the interpretability and transparency of brain computing findings by inferring and testing multiple hypotheses taking into consideration the effect of evidence combination. Finally, multiple sources of knowledge (K), information (I) and data (D) are driven by a KID loop as the thinking space to inspire never-ending learning and multi-dimensional interactions in the connected social-cyber-physical spaces. Experimental results have demonstrated the efficacy of the proposed brain computing method with systematic fusion.