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Electrocorticography and stereo EEG provide distinct measures of brain connectivity: implications for network models

机译:电焦和立体脑电图提供了脑连接的明显措施:对网络模型的影响

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摘要

Brain network models derived from graph theory have the potential to guide functional neurosurgery, and to improve rates of post-operative seizure freedom for patients with epilepsy. A barrier to applying these models clinically is that intracranial EEG electrode implantation strategies vary by centre, region and country, from cortical grid & strip electrodes (Electrocorticography), to purely stereotactic depth electrodes (Stereo EEG), to a mixture of both. To determine whether models derived from one type of study are broadly applicable to others, we investigate the differences in brain networks mapped by electrocorticography and stereo EEG in a cohort of patients who underwent surgery for temporal lobe epilepsy and achieved a favourable outcome. We show that networks derived from electrocorticography and stereo EEG define distinct relationships between resected and spared tissue, which may be driven by sampling bias of temporal depth electrodes in patients with predominantly cortical grids. We propose a method of correcting for the effect of internodal distance that is specific to electrode type and explore how additional methods for spatially correcting for sampling bias affect network models. Ultimately, we find that smaller surgical targets tend to have lower connectivity with respect to the surrounding network, challenging notions that abnormal connectivity in the epileptogenic zone is typically high. Our findings suggest that effectively applying computational models to localize epileptic networks requires accounting for the effects of spatial sampling, particularly when analysing both electrocorticography and stereo EEG recordings in the same cohort, and that future network studies of epilepsy surgery should also account for differences in focality between resection and ablation. We propose that these findings are broadly relevant to intracranial EEG network modelling in epilepsy and an important step in translating them clinically into patient care.
机译:源自图理论的脑网络模型具有导致功能性神经外科的潜力,并提高癫痫患者的术后癫痫发作自由率。临床上施加这些模型的障碍是,颅内EEG电极植入策略因中心,区域和国家,从皮质网格和剥离电极(电加管),纯度立体管深度电极(立体脑电图)到两者的混合物。为了确定源自一种类型的研究是否广泛适用于他人,我们调查脑网络映射的脑网络中的脑网络差异,并在接受颞叶癫痫手术的患者队伍中映射并实现了有利的结果。我们表明源自电加电和立体脑电图的网络限定了切除和刮擦组织之间的不同关系,这可以通过主要皮质网格的患者中的时间深度电极的偏差来驱动。我们提出了一种纠正特定电极类型的专有距离的效果的方法,并探讨用于抽样偏置的空间校正的附加方法如何影响网络模型。最终,我们发现较小的外科手术目标倾向于与周围网络的连接较低,窒息区异常连接的挑战性观念通常高。我们的研究结果表明,有效地将计算模型应用于本地化癫痫网络,需要考虑空间采样的影响,特别是在分析同一伙伴中的电加理和立体声脑电图记录时,癫痫手术的未来网络研究也应考虑差异的差异切除和消融之间。我们建议这些发现与癫痫中的颅内EEG网络建模大致相关,以及在临床上转化为患者护理的重要一步。

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