Aphasia is a prevalent cognitive syndrome caused by stroke. The rarity of premorbid imaging and heterogeneity of lesion obscures the links between the local effects of the lesion, global anatomic network organization, and aphasia symptoms. We applied a simulated attack approach in humans to examine the effects of 39 stroke lesions (16 females) on anatomic network topology by simulating their effects in a control sample of 36 healthy (15 females) brain networks. We focused on measures of global network organization thought to support overall brain function and resilience in the whole brain and within the left hemisphere. After removing lesion volume from the network topology measures and behavioral scores [the Western Aphasia Battery Aphasia Quotient (WAB-AQ), four behavioral factor scores obtained from a neuropsychological battery, and a factor sum], we compared the behavioral variance accounted for by simulated poststroke connectomes to that observed in the randomly permuted data. Global measures of anatomic network topology in the whole brain and left hemisphere accounted for 10% variance or more of the WAB-AQ and the lexical factor score beyond lesion volume and null permutations. Streamline networks provided more reliable point estimates than FA networks. Edge weights and network efficiency were weighted most highly in predicting the WAB-AQ for FA networks. Overall, our results suggest that global network measures provide modest statistical value beyond lesion volume when predicting overall aphasia severity, but less value in predicting specific behaviors. Variability in estimates could be induced by premorbid ability, deafferentation and diaschisis, and neuroplasticity following stroke.

SIGNIFICANCE STATEMENT Poststroke, the remaining neuroanatomy maintains cognition and supports recovery. However, studies often use small, cross-sectional samples that cannot fully model the interactions between lesions and other variables that affect networks in stroke. Alternate methods are required to account for these effects. "Simulated attack" models are computational approaches that apply virtual damage to the brain and measure their putative consequences. Using a simulated attack model, we estimated how simulated damage to anatomic networks could account for language performance. Overall, our results reveal that global network measures can provide modest statistical value predicting overall aphasia severity, but less value in predicting specific behaviors. These findings suggest that more theoretically precise network models could be necessary to robustly predict individual outcomes in aphasia.

失语症是由中风引起的一种普遍的认知综合征。病前影像学的罕见性和病变的异质性掩盖了病变的局部影响、整体解剖网络组织和失语症症状之间的联系。我们在人类中应用了一种模拟攻击方法，通过在 36 个健康（15 名女性）脑网络的对照样本中模拟它们的影响，来检查 39 种中风病变（16 名女性）对解剖网络拓扑结构的影响。我们专注于全球网络组织的措施，这些措施被认为可以支持整个大脑和左半球的整体大脑功能和弹性。从网络拓扑测量和行为评分中删除病变体积 [西方失语症电池失语商 (WAB-AQ)，从神经心理电池获得的四个行为因素评分，和一个因子总和]，我们将模拟中风后连接组解释的行为方差与随机排列数据中观察到的行为方差进行了比较。全脑和左半球解剖网络拓扑的全局测量占 WAB-AQ 的 10% 或更多方差，以及超出病变体积和零排列的词汇因子评分。Streamline 网络提供了比 FA 网络更可靠的点估计。在预测 FA 网络的 WAB-AQ 时，边权重和网络效率的权重最高。总体而言，我们的结果表明，在预测整体失语严重程度时，全球网络测量提供了超出病灶体积的适度统计价值，但在预测特定行为方面价值较低。估计的变异性可能是由病前能力、传入神经阻滞和神经功能障碍引起的，

重要性声明中风后，剩余的神经解剖结构维持认知并支持恢复。然而，研究通常使用小的横截面样本，这些样本无法完全模拟病变与影响中风网络的其他变量之间的相互作用。需要替代方法来解决这些影响。“模拟攻击”模型是对大脑施加虚拟损伤并测量其假定后果的计算方法。使用模拟攻击模型，我们估计了对解剖网络的模拟损伤如何解释语言性能。总体而言，我们的结果表明，全球网络测量可以提供适度的统计价值来预测整体失语症的严重程度，但在预测特定行为方面的价值较低。