Soddu Andrea, Vanhaudenhuyse Audrey, Demertzi Athena, Bruno Marie-Aurélie, Tshibanda Luaba, Di Haibo, Mélanie Boly, Papa Michele, Laureys Steven, Noirhomme Quentin
Coma Science Group, Cyclotron Research Center, University of Liège, Belgium.
Funct Neurol. 2011 Jan-Mar;26(1):37-43.
Recent advances in the study of spontaneous brain activity have demonstrated activity patterns that emerge with no task performance or sensory stimulation; these discoveries hold promise for the study of higher-order associative network functionality. Additionally, such advances are argued to be relevant in pathological states, such as disorders of consciousness (DOC), i.e., coma, vegetative and minimally conscious states. Recent studies on resting state activity in DOC, measured with functional magnetic resonance imaging (fMRI) techniques, show that functional connectivity is disrupted in the task-negative or the default mode network. However, the two main approaches employed in the analysis of resting state functional connectivity data (i.e., hypothesis-driven seed-voxel and data-driven independent component analysis) present multiple methodological difficulties, especially in non-collaborative DOC patients. Improvements in motion artifact removal and spatial normalization are needed before fMRI resting state data can be used as proper biomarkers in severe brain injury. However, we anticipate that such developments will boost clinical resting state fMRI studies, allowing for easy and fast acquisitions and ultimately improve the diagnosis and prognosis in the absence of DOC patients' active collaboration in data acquisition.
近期关于自发性脑活动的研究进展已揭示出在无任务执行或感觉刺激情况下出现的活动模式；这些发现为高阶联想网络功能的研究带来了希望。此外，此类进展被认为与病理状态相关，如意识障碍（DOC），即昏迷、植物状态和微意识状态。近期利用功能磁共振成像（fMRI）技术对DOC患者静息状态活动的研究表明，在任务负性或默认模式网络中功能连接被破坏。然而，在分析静息状态功能连接数据时采用的两种主要方法（即假设驱动的种子体素法和数据驱动的独立成分分析法）存在多种方法学难题，尤其是在非合作性DOC患者中。在fMRI静息状态数据能够作为严重脑损伤的合适生物标志物使用之前，需要改进运动伪影去除和空间标准化。然而，我们预计此类进展将推动临床静息状态fMRI研究，实现轻松快速采集，并最终在DOC患者未积极配合数据采集的情况下改善诊断和预后。
