Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
PLoS One. 2013 Jul 26;8(7):e69798. doi: 10.1371/journal.pone.0069798. Print 2013.
Neurological function in patients with slowly growing brain tumors can be preserved even after extensive tumor resection. However, the global process of cortical reshaping and cerebral redistribution cannot be understood without taking into account the white matter tracts. The aim of this study was to predict the functional consequences of tumor-induced white matter damage by computer simulation. A computational model was proposed, incorporating two cortical patches and the white matter connections of the uncinate fasciculus. Tumor-induced structural changes were modeled such that different aspects of the connectivity were altered, mimicking the biological heterogeneity of gliomas. The network performance was quantified by comparing memory pattern recall and the plastic compensatory capacity of the network was analyzed. The model predicts an optimal level of synaptic conductance boost that compensates for tumor-induced connectivity loss. Tumor density appears to change the optimal plasticity regime, but tumor size does not. Compensatory conductance values that are too high lead to performance loss in the network and eventually to epileptic activity. Tumors of different configurations show differences in memory recall performance with slightly lower plasticity values for dense tumors compared to more diffuse tumors. Simulation results also suggest an optimal noise level that is capable of increasing the recall performance in tumor-induced white matter damage. In conclusion, the model presented here is able to capture the influence of different tumor-related parameters on memory pattern recall decline and provides a new way to study the functional consequences of white matter invasion by slowly growing brain tumors.
即使在广泛的肿瘤切除后，生长缓慢的脑肿瘤患者的神经功能也可以保留。然而，如果不考虑白质束，就无法理解皮质重塑和大脑重新分配的全局过程。本研究的目的是通过计算机模拟预测肿瘤引起的白质损伤的功能后果。提出了一种计算模型，该模型包含两个皮质斑块和钩束的白质连接。通过建模模拟肿瘤引起的结构变化，改变了连接的不同方面，模拟了神经胶质瘤的生物学异质性。通过比较记忆模式的回忆来量化网络性能，并分析网络的可塑补偿能力。该模型预测了突触电导增强的最佳水平，以补偿肿瘤引起的连接丢失。肿瘤密度似乎改变了最佳的可塑性状态，但肿瘤大小没有。补偿性电导值过高会导致网络性能下降，并最终导致癫痫活动。具有不同配置的肿瘤在记忆回忆性能方面存在差异，与更弥散的肿瘤相比，密集肿瘤的可塑性值略低。模拟结果还表明存在最佳噪声水平，能够提高肿瘤诱导的白质损伤中的记忆性能。总之，本文提出的模型能够捕捉到不同肿瘤相关参数对记忆模式回忆下降的影响，并为研究缓慢生长的脑肿瘤对白质的侵袭的功能后果提供了一种新方法。
