Department of Medicine St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.
Centre for Clinical Neurosciences and Neurological Research, St Vincent's Hospital Melbourne, Melbourne, Australia.
Nat Commun. 2022 Feb 22;13(1):994. doi: 10.1038/s41467-022-28640-x.
Modelling the interactions that arise from neural dynamics in seizure genesis is challenging but important in the effort to improve the success of epilepsy surgery. Dynamical network models developed from physiological evidence offer insights into rapidly evolving brain networks in the epileptic seizure. A limitation of previous studies in this field is the dependence on invasive cortical recordings with constrained spatial sampling of brain regions that might be involved in seizure dynamics. Here, we propose virtual intracranial electroencephalography (ViEEG), which combines non-invasive ictal magnetoencephalographic imaging (MEG), dynamical network models and a virtual resection technique. In this proof-of-concept study, we show that ViEEG signals reconstructed from MEG alone preserve critical temporospatial characteristics for dynamical approaches to identify brain areas involved in seizure generation. We show the non-invasive ViEEG approach may have some advantage over intracranial electroencephalography (iEEG). Future work may be designed to test the potential of the virtual iEEG approach for use in surgical management of epilepsy.
对癫痫发作中神经动力学引起的相互作用进行建模具有挑战性，但对于提高癫痫手术成功率非常重要。从生理证据中开发的动态网络模型为癫痫发作中快速演变的大脑网络提供了深入了解。该领域以前研究的一个局限性是依赖于具有受限空间采样的侵入性皮质记录，这些区域可能涉及癫痫动力学。在这里，我们提出了虚拟颅内脑电图（ViEEG），它结合了非侵入性发作期磁共振成像（MEG）、动态网络模型和虚拟切除技术。在这项概念验证研究中，我们表明，仅从 MEG 重建的 ViEEG 信号保留了用于识别参与癫痫发作的大脑区域的动态方法的关键时空特征。我们表明，非侵入性 ViEEG 方法可能比颅内脑电图（iEEG）具有一些优势。未来的工作可能旨在测试虚拟 iEEG 方法在癫痫手术管理中的应用潜力。
