Goh S Y Matthew, Irimia Andrei, Torgerson Carinna M, Horn John D Van
Department of Neurology, Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California Los Angeles, CA, USA.
Front Neuroinform. 2014 Feb 26;8:19. doi: 10.3389/fninf.2014.00019. eCollection 2014.
Throughout the past few decades, the ability to treat and rehabilitate traumatic brain injury (TBI) patients has become critically reliant upon the use of neuroimaging to acquire adequate knowledge of injury-related effects upon brain function and recovery. As a result, the need for TBI neuroimaging analysis methods has increased in recent years due to the recognition that spatiotemporal computational analyses of TBI evolution are useful for capturing the effects of TBI dynamics. At the same time, however, the advent of such methods has brought about the need to analyze, manage, and integrate TBI neuroimaging data using informatically inspired approaches which can take full advantage of their large dimensionality and informational complexity. Given this perspective, we here discuss the neuroinformatics challenges for TBI neuroimaging analysis in the context of structural, connectivity, and functional paradigms. Within each of these, the availability of a wide range of neuroimaging modalities can be leveraged to fully understand the heterogeneity of TBI pathology; consequently, large-scale computer hardware resources and next-generation processing software are often required for efficient data storage, management, and analysis of TBI neuroimaging data. However, each of these paradigms poses challenges in the context of informatics such that the ability to address them is critical for augmenting current capabilities to perform neuroimaging analysis of TBI and to improve therapeutic efficacy.
在过去几十年里，治疗和康复创伤性脑损伤（TBI）患者的能力已严重依赖于使用神经成像技术，以便充分了解损伤对脑功能和恢复的相关影响。因此，近年来对TBI神经成像分析方法的需求有所增加，因为人们认识到对TBI演变进行时空计算分析有助于捕捉TBI动态变化的影响。然而，与此同时，这些方法的出现带来了使用受信息学启发的方法来分析、管理和整合TBI神经成像数据的需求，这些方法能够充分利用其高维度和信息复杂性。从这个角度出发，我们在此讨论在结构、连接性和功能范式背景下TBI神经成像分析面临的神经信息学挑战。在这些范式中的每一种中，可以利用广泛的神经成像模态来全面了解TBI病理学的异质性；因此，高效存储、管理和分析TBI神经成像数据通常需要大规模计算机硬件资源和下一代处理软件。然而，这些范式中的每一种在信息学背景下都带来了挑战，因此应对这些挑战的能力对于增强当前进行TBI神经成像分析的能力以及提高治疗效果至关重要。
