University Research Priority Program "Dynamics of Healthy Aging", University of Zürich, Zürich, Switzerland; Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.
Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.
Neuroimage. 2019 Jul 15;195:354-361. doi: 10.1016/j.neuroimage.2019.03.012. Epub 2019 Mar 9.
Graph theory has been playing an increasingly important role in understanding the organizational properties of brain networks, subsequently providing new tools for the search of neural correlates of consciousness, particularly in the context of patients recovering from severe brain injury. However, this approach is not without challenges, as it usually relies on arbitrarily fixing a threshold in order to retain the strongest connections proportionally equal across subjects. This method increases the comparability between individuals or groups but it risks the inclusion of false positive and therefore spurious connections, especially in the context of brain disorders. Resting state data acquired in 25 coma patients and 22 healthy subjects was compared. We obtained a representative fixed density of significant connections by first applying a p-value-based threshold on healthy subjects' networks and then choosing a threshold at which all individuals exhibited meaningful connections. The obtained threshold (i.e. 10%) was used to construct graphs in the patient group. The findings showed that coma patients have lower number of significant connections with approximately 50% of them not fulfilling the criteria of the fixed density threshold. The remaining patients with relatively preserved global functional connectivity had sufficient significant connections between regions, but showed signs of major whole-brain network reorganization. These results warrant careful consideration in the construction of functional connectomes in patients with disorders of consciousness and set the scene for future studies investigating potential clinical implications of such an approach.
图论在理解大脑网络的组织性质方面发挥着越来越重要的作用,为寻找意识的神经相关提供了新的工具,特别是在严重脑损伤患者康复的背景下。然而,这种方法并非没有挑战,因为它通常依赖于任意固定一个阈值,以便在受试者之间按比例保留最强的连接。这种方法增加了个体或群体之间的可比性,但存在纳入假阳性和虚假连接的风险,特别是在大脑疾病的背景下。比较了 25 名昏迷患者和 22 名健康受试者的静息态数据。我们通过首先在健康受试者的网络上应用基于 p 值的阈值,然后选择一个使所有个体都显示出有意义连接的阈值,获得了具有代表性的固定密度显著连接。所获得的阈值(即 10%)用于构建患者组的图。研究结果表明,昏迷患者的显著连接数量较少,其中约 50%的患者不符合固定密度阈值的标准。其余的患者具有相对保留的全局功能连接,具有足够的区域间显著连接,但显示出全脑网络重组的迹象。这些结果在构建意识障碍患者的功能连接组时需要仔细考虑,并为未来研究这种方法的潜在临床意义奠定了基础。