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Dimensionality Reduction Methods for Brain Imaging Data Analysis
ACM Computing Surveys ( IF 16.6 ) Pub Date : 2021-05-04 , DOI: 10.1145/3448302
Yunbo Tang 1 , Dan Chen 1 , Xiaoli Li 2
Affiliation  

The past century has witnessed the grand success of brain imaging technologies, such as electroencephalography and magnetic resonance imaging, in probing cognitive states and pathological brain dynamics for neuroscience research and neurology practices. Human brain is “the most complex object in the universe,” and brain imaging data ( BID ) are routinely of multiple/many attributes and highly non-stationary. These are determined by the nature of BID as the recordings of the evolving processes of the brain(s) under examination in various views. Driven by the increasingly high demands for precision, efficiency, and reliability in neuro-science and engineering tasks, dimensionality reduction has become a priority issue in BID analysis to handle the notoriously high dimensionality and large scale of big BID sets as well as the enormously complicated interdependencies among data elements. This has become particularly urgent and challenging in this big data era. Dimensionality reduction theories and methods manifest unrivaled potential in revealing key insights to BID via offering the low-dimensional/tiny representations/features, which may preserve critical characterizations of massive neuronal activities and brain functional and/or malfunctional states of interest. This study surveys the most salient work along this direction conforming to a 3-dimensional taxonomy with respect to (1) the scale of BID , of which the design with this consideration is important for the potential applications; (2) the order of BID , in which a higher order denotes more BID attributes manipulatable by the method; and (3) linearity , in which the method’s degree of linearity largely determines the “fidelity” in BID exploration. This study defines criteria for qualitative evaluations of these works in terms of effectiveness, interpretability, efficiency, and scalability. The classifications and evaluations based on the taxonomy provide comprehensive guides to (1) how existing research and development efforts are distributed and (2) their performance, features, and potential in influential applications especially when involving big data. In the end, this study crystallizes the open technical issues and proposes research challenges that must be solved to enable further researches in this area of great potential.

中文翻译:

脑成像数据分析的降维方法

上个世纪见证了脑成像技术(如脑电图和磁共振成像)在探索认知状态和病理性脑动力学以用于神经科学研究和神经学实践方面的巨大成功。人脑是“宇宙中最复杂的物体”,而大脑成像数据(出价) 通常具有多个/多个属性并且高度非平稳。这些是由性质决定的出价作为在各种观点下被检查的大脑进化过程的记录。在神经科学和工程任务中对精度、效率和可靠性的要求越来越高的驱动下,降维已成为一个优先考虑的问题。出价分析以处理众所周知的高维和大规模的大出价集合以及数据元素之间极其复杂的相互依赖关系。在这个大数据时代,这变得尤为紧迫和具有挑战性。降维理论和方法在揭示关键见解方面表现出无与伦比的潜力出价通过提供低维/微小的表示/特征,可以保留大量神经元活动和感兴趣的大脑功能和/或故障状态的关键特征。本研究调查了沿着这个方向的最突出的工作,符合关于 (1) 的 3 维分类法规模出价,其中考虑到这一点的设计对于潜在的应用很重要;(2)命令出价, 其中阶数越高表示越出价该方法可操作的属性;(3)线性度,其中方法的线性程度很大程度上决定了“保真度”出价勘探。本研究根据有效性、可解释性、效率和可扩展性定义了对这些工作进行定性评估的标准。基于分类的分类和评估为 (1) 现有研究和开发工作的分布方式以及 (2) 它们在有影响力的应用中的性能、特征和潜力提供了全面的指导,尤其是在涉及大数据时。最后,本研究明确了开放的技术问题,并提出了必须解决的研究挑战,以便在这一具有巨大潜力的领域进行进一步研究。
更新日期:2021-05-04
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