Brain tumor patients scheduled for tumor resection often face significant uncertainty, as the outcome of neurosurgery is difficult to predict at the individual patient level. Recently, simulation of the activity of neural populations connected according to the white matter fibers, producing personalized brain network models, has been introduced as a promising tool for this purpose. The Virtual Brain provides a robust open source framework to implement these models. However, brain network models first have to be validated, before they can be used to predict brain dynamics. In prior work, we optimized individual brain network model parameters to maximize the fit with empirical brain activity. In this study, we extend this line of research by examining the stability of fitted parameters before and after tumor resection, and compare it with baseline parameter variability using data from healthy control subjects. Based on these findings, we perform the first "virtual neurosurgery", mimicking patient's actual surgery by removing white matter fibers in the resection mask and simulating again neural activity on this new connectome. We find that brain network model parameters are relatively stable over time in brain tumor patients who underwent tumor resection, compared with baseline variability in healthy control subjects. Concerning the virtual neurosurgery analyses, use of the pre-surgery model implemented on the virtually resected structural connectome resulted in improved similarity with post-surgical empirical functional connectivity in some patients, but negligible improvement in others. These findings reveal interesting avenues for increasing interactions between computational neuroscience and neuro-oncology, as well as important limitations that warrant further investigation.

中文翻译：

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使用虚拟大脑模拟肿瘤切除后的大脑动力学

计划进行肿瘤切除术的脑肿瘤患者通常面临很大的不确定性，因为神经外科手术的结果很难在个体患者层面进行预测。最近，根据白质纤维连接的神经群体活动的模拟，产生个性化的大脑网络模型，已被引入作为用于此目的的有前途的工具。Virtual Brain 提供了一个强大的开源框架来实现这些模型。然而，大脑网络模型首先必须经过验证，然后才能用于预测大脑动力学。在之前的工作中，我们优化了个体大脑网络模型参数，以最大化与经验大脑活动的拟合。在这项研究中，我们通过检查肿瘤切除前后拟合参数的稳定性来扩展这一研究领域，并使用健康对照受试者的数据将其与基线参数变异性进行比较。基于这些发现，我们进行了第一个“虚拟神经外科手术”，通过去除切除面罩中的白质纤维并再次模拟这个新连接组上的神经活动来模拟患者的实际手术。我们发现，与健康对照受试者的基线变异性相比，接受肿瘤切除术的脑肿瘤患者的脑网络模型参数随着时间的推移相对稳定。关于虚拟神经外科分析，使用在虚拟切除的结构连接组上实施的术前模型导致某些患者与术后经验功能连接的相似性提高，但其他患者的改善可忽略不计。