Jirsa V K, Proix T, Perdikis D, Woodman M M, Wang H, Gonzalez-Martinez J, Bernard C, Bénar C, Guye M, Chauvel P, Bartolomei F
Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, Marseille, France.
Neuroimage. 2017 Jan 15;145(Pt B):377-388. doi: 10.1016/j.neuroimage.2016.04.049. Epub 2016 Jul 28.
Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.
个体差异对治疗方法和治疗途径的结果有明显影响。因此，根据个体患者定制医疗保健方案应能改善治疗效果。我们提出了一种基于从个体患者的非侵入性结构数据得出的个性化脑网络模型的脑干预新方法。以一名双颞叶癫痫患者为例，我们逐步展示如何开发虚拟癫痫患者（VEP）脑模型，并整合患者特定信息，如脑连接性、致痫区和MRI病变。利用高性能计算，我们系统地进行参数空间探索，根据患者的经验性立体定向脑电图（SEEG）数据对脑模型进行拟合和验证，并展示如何制定针对治疗和干预的新型个性化策略。
