[论文解读] Intracranial EEG structure-function coupling predicts surgical outcomes in focal epilepsy

Nishant Sinha, John S. Duncan+10 more|arXiv (Cornell University)|Apr 17, 2022

Functional Brain Connectivity Studies被引用 6

一句话总结

本研究表明，术前基于颅内脑电图（iEEG）的结构-功能耦合可强烈预测局灶性癫痫患者的手术预后。通过分析基于iEEG、MRI和DTI数据的结构与功能脑网络，作者发现更高的结构-功能耦合程度与术后更佳的无发作率相关，在结合临床因素后，预测准确率达到85%，敏感性达87%。

ABSTRACT

Alterations to structural and functional brain networks have been reported across many neurological conditions. However, the relationship between structure and function -- their coupling -- is relatively unexplored, particularly in the context of an intervention. Epilepsy surgery alters the brain structure and networks to control the functional abnormality of seizures. Given that surgery is a structural modification aiming to alter the function, we hypothesized that stronger structure-function coupling preoperatively is associated with a greater chance of post-operative seizure control. We constructed structural and functional brain networks in 39 subjects with medication-resistant focal epilepsy using data from intracranial EEG (pre-surgery), structural MRI (pre-and post-surgery), and diffusion MRI (pre-surgery). We investigated pre-operative structure-function coupling at two spatial scales a) at the global iEEG network level and b) at the resolution of individual iEEG electrode contacts using virtual surgeries. At global network level, seizure-free individuals had stronger structure-function coupling pre-operatively than those that were not seizure-free regardless of the choice of interictal segment or frequency band. At the resolution of individual iEEG contacts, the virtual surgery approach provided complementary information to localize epileptogenic tissues. In predicting seizure outcomes, structure-function coupling measures were more important than clinical attributes, and together they predicted seizure outcomes with an accuracy of 85% and sensitivity of 87%. The underlying assumption that the structural changes induced by surgery translate to the functional level to control seizures is valid when the structure-functional coupling is strong. Mapping the regions that contribute to structure-functional coupling using virtual surgeries may help aid surgical planning.

研究动机与目标

• 探讨术前脑部结构-功能耦合是否可预测局灶性癫痫手术的成功。
• 研究癫痫患者中从MRI/DTI获得的结构脑网络与从iEEG获得的功能网络之间的关系。
• 评估基于结构-功能耦合的虚拟手术切除是否可改善致痫灶的定位。
• 确定结构-功能耦合相较于传统临床变量在预测术后癫痫发作结局方面的预测能力。

提出的方法

• 利用术前和术后扫描的T1加权MRI与弥散张量成像（DTI）数据构建结构脑网络。
• 基于多个频段的发作间期活动，从颅内脑电图（iEEG）记录中构建功能脑网络。
• 在两个空间尺度上量化结构-功能耦合：全局网络水平与单个电极接触点水平。
• 在iEEG网络上执行虚拟切除，以模拟手术结果并评估特定区域对耦合的贡献。
• 使用机器学习模型将结构-功能耦合指标与临床变量结合，用于结果预测。
• 在39例药物难治性局灶性癫痫患者队列中，使用准确率和敏感性指标评估预测性能。

实验结果

研究问题

• RQ1 术前更强的结构-功能耦合是否与局灶性癫痫患者术后更好的无发作结局相关？
• RQ2 基于结构-功能耦合的虚拟手术是否可改善致痫组织的定位？
• RQ3 结构-功能耦合与临床因素相比，在预测手术成功方面表现如何？
• RQ4 分析的空间尺度（全局 vs. 单个电极接触点）是否影响结构-功能耦合的预测能力？
• RQ5 结构-功能耦合强度是否可作为癫痫手术结局的可靠生物标志物？

主要发现

• 无发作患者在所有测试的发作间期段和频段中，其术前结构-功能耦合强度显著高于非无发作患者。
• 在iEEG全局网络水平的结构-功能耦合，作为手术结局的预测因子，优于任何单一临床属性。
• 虚拟手术模拟显示，对结构-功能耦合贡献度高的区域更可能属于致痫灶。
• 将结构-功能耦合与临床因素结合后，对术后无发作的预测准确率达到85%，敏感性达87%。
• 无论选择哪一段发作间期记录或频段，结构-功能耦合的预测能力均保持一致。
• 本研究支持以下假设：有效的手术结局依赖于术前脑结构与功能之间较强的耦合。