Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.
Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain.
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DOI：
10.1523/eneuro.0083-18.2018
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发表时间：
2018-05
期刊：
影响因子：
3.4
通讯作者：
Marinazzo D
中科院分区：
文献类型：
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作者：
Aerts H;Schirner M;Jeurissen B;Van Roost D;Achten E;Ritter P;Marinazzo D
脑肿瘤切除的术前计划旨在勾勒出病变附近有能力的组织，以便在手术中备用。为此，目前采用了非侵入性神经成像技术，如功能磁共振成像和扩散加权成像纤维跟踪。然而，考虑到这些信息往往仍然是不够的，因为复杂的非线性大脑动力学阻碍了对手术干预后功能结果的直接预测。大规模脑网络建模通过将神经成像数据与基于生物物理学的模型相结合来预测集体大脑动力学，从而具有弥合这一差距的潜力。作为这一方向的第一步，必须选择适当的计算模型，然后必须确定适当的模型参数值。为此，我们使用开源神经信息学平台虚拟大脑模拟了25名人脑肿瘤患者和11名人类对照参与者的大规模脑动力学。分别优化了简化的Wong-Wang模型的局部和全局模型参数，并在脑肿瘤患者和对照组之间进行了比较。此外，还评估了模型参数与结构网络结构和认知绩效之间的关系。结果表明：(1)使用单独优化的模型参数时，显著提高了个体功能连接性的预测精度；(2)局部模型参数可以区分直接受肿瘤影响的区域、远离肿瘤的区域和健康大脑中的区域；以及(3)单独优化的模型参数与结构网络拓扑和认知表现之间的有趣关联。
Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics. As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.