[论文解读] Complementary structural and functional abnormalities to localise epileptogenic tissue

Jonathan Horsley, Rhys H. Thomas+11 more|arXiv (Cornell University)|Apr 6, 2023

Advanced Neuroimaging Techniques and ApplicationsMedicine被引用 3

一句话总结

本研究表明，将弥散加权磁共振成像（dMRI）推断的结构性连接异常与颅内脑电图（iEEG）获得的功能性异常相结合，可显著提高药物难治性局灶性癫痫致痫灶的定位精度。对两种模态中最大异常区域的切除使无 seizure 率提高了 15 倍（p=0.008），且联合决策树模型在 43 名患者中正确预测了 84% 的手术结局，表明其在术前规划中具有互补价值。

ABSTRACT

When investigating suitability for surgery, people with drug-refractory focal epilepsy may have intracranial EEG (iEEG) electrodes implanted to localise seizure onset. Diffusion-weighted magnetic resonance imaging (dMRI) may be acquired to identify key white matter tracts for surgical avoidance. Here, we investigate whether structural connectivity abnormalities, inferred from dMRI, may be used in conjunction with functional iEEG abnormalities to aid localisation and resection of the epileptogenic zone (EZ), and improve surgical outcomes in epilepsy. We retrospectively investigated data from 43 patients with epilepsy who had surgery following iEEG. Twenty five patients (58%) were free from disabling seizures (ILAE 1 or 2) at one year. For all patients, T1-weighted and diffusion-weighted MRIs were acquired prior to iEEG implantation. Interictal iEEG functional, and dMRI structural connectivity abnormalities were quantified by comparison to a normative map and healthy controls respectively. First, we explored whether the resection of maximal (dMRI and iEEG) abnormalities related to improved surgical outcomes. Second, we investigated whether the modalities provided complementary information for improved prediction of surgical outcome. Third, we suggest how dMRI abnormalities may be useful to inform the placement of iEEG electrodes as part of the pre-surgical evaluation using a patient case study. Seizure freedom was 15 times more likely in those patients with resection of maximal dMRI and iEEG abnormalities (p=0.008). Both modalities were separately able to distinguish patient outcome groups and when combined, a decision tree correctly separated 36 out of 43 (84%) patients based on surgical outcome. Structural dMRI could be used in pre-surgical evaluations, particularly when localisation of the EZ is uncertain, to inform personalised iEEG implantation and resection.

研究动机与目标

• 探讨 dMRI 获得的结构性连接异常是否可与 iEEG 获得的功能性异常互补，以提高药物难治性局灶性癫痫致痫灶（EZ）的定位精度。
• 评估结合 dMRI 与 iEEG 异常是否可提高对术后结局的预测能力，优于单独使用任一模态。
• 探讨 dMRI 在术前评估期间指导个性化 iEEG 电极放置的实用性。

提出的方法

• 对 43 名癫痫患者术前 T1 加权及弥散加权 MRI 数据进行回顾性分析。
• 利用来自健康对照者的正常 dMRI 图谱量化结构性连接异常。
• 通过将间歇期 iEEG 数据与正常功能图谱比较，量化功能性 iEEG 异常。
• 采用决策树模型结合 dMRI 与 iEEG 异常，以预测手术结局（术后一年无 seizure）。
• 将 dMRI 与 iEEG 最大异常区域的切除范围与手术结局相关联。
• 通过一例患者病例研究，说明 dMRI 异常如何指导 iEEG 电极的放置。

实验结果

研究问题

• RQ1 切除最大结构性（dMRI）与功能性（iEEG）异常是否可改善癫痫患者的手术结局？
• RQ2 dMRI 与 iEEG 异常是否为预测术后无 seizure 提供互补信息？
• RQ3 dMRI 推导的结构性连接异常是否可指导在致痫灶定位不确定情况下的最优 iEEG 电极放置？

主要发现

• 在切除最大 dMRI 与 iEEG 异常的患者中，无 seizure 的可能性提高了 15 倍（p=0.008）。
• dMRI 与 iEEG 异常分别能够有效区分无 seizure 与非无 seizure 结局组别。
• 联合决策树模型基于两种模态数据，成功将 43 名患者中的 36 名（84%）正确分类为手术结局。
• dMRI 异常为术前规划提供了有用且独立的信息，尤其在致痫灶定位不确定时更具价值。
• dMRI 与 iEEG 数据的整合显著提升了预测准确性，优于任一模态单独使用。
• 病例研究显示，dMRI 异常可指导靶向性 iEEG 电极放置，从而更精确地定位致痫灶。