Identifying brain networks to predict treatment resistance and post-surgical outcome: An ENIGMA-Epilepsy initiative

摘要 癫痫是一种毁灭性的神经疾病，影响着全球超过5000万人。 大约三分之一的患者对抗癫痫药物(ASM)没有反应，需要额外的 诊断性检查，包括考虑手术。结构神经成像在脑血管成像中发挥着关键作用。 对癫痫的诊断评估，在许多患者中识别与癫痫共同定位的可见病灶 全神贯注。然而，多达40%的患者有外观正常的核磁共振成像，而且这个数字还在增长。结果, 人们对识别细微的大脑网络异常越来越感兴趣，这可能有助于描绘 癫痫网络和帮助预测治疗反应(即对ASM和外科手术的反应 结果)。不幸的是，可靠地识别哪些患者会对药物敏感的方法- 耐药，以及哪些患者将获得成功的手术结果与不成功的手术结果是缺乏的。 在这一领域取得进展的一个主要障碍是获得定量成像，包括结构mri。 (SMRI)和弥散加权成像(DMRI)、临床和遗传数据 可以评估不同治疗结果的患者样本。在过去，样本量有 不足以发现局灶性或全身性脑部疾病患者的细微但可靠的脑异常 真正与癫痫有关的癫痫，而不是与小或小或 受地理限制的样本。 一个新的大规模数据倡议，ENIGMA4-癫痫，加上技术进步， 实现更好的数据协调现在正在消除这些障碍，并允许我们将多个站点 SMRI/dMRI、临床和遗传数据，以预测重要的临床结果，并使结果具有普遍性 全球癫痫患者社区。在这笔赠款中，我们将利用通过谜-癫痫-a收集的数据 由来自14个国家的24个癫痫中心组成的联合体(超过2250名患者和1727名健康对照 SMRI/dMRI数据集)和人类癫痫项目(HEP)。我们将包括新的网络模型(即， 个体化连接)和多基因风险评分(PR)来测试是否结合成像， 临床和遗传风险可以准确预测两种临床结果：耐药和手术后 癫痫发作结果。我们的科学前提是，基于MRI的全脑网络属性评估，在 结合临床数据和来自遗传数据的PR，能够预测(I)药物反应 最近诊断的癫痫病例和(Ii)耐药癫痫患者的手术后结果。 该R01通过引入高功率设计，满足了NIH对更多可重复性研究的呼吁 能够捕获具有不同临床特征和治疗结果的患者的变异性。 这笔赠款也直接与NINDS的2020年癫痫基准(IIIB)保持一致，该基准鼓励 能够预测癫痫治疗反应的遗传、临床和影像生物标记物的鉴定。

ABSTRACT Epilepsy is a devastating neurological illness that affects over 50 million people worldwide. Approximately one-third of patients do not respond to anti-seizure medication (ASM) and require additional diagnostic work-up, including consideration for surgery. Structural neuroimaging plays a pivotal role in the diagnostic evaluation of epilepsy, identifying visible lesions in many patients that co-localize with the seizure focus. However, up to 40% of patients have normal-appearing MRIs and this number is growing. As a result, there is increased interest in identifying subtle brain network abnormalities that could help to delineate the epileptogenic network and aid in the prediction of treatment response (i.e., response to ASMs and surgical outcomes). Unfortunately, methods for reliably identifying which patients will be drug-responsive versus drug- resistant, and which patients will achieve successful versus unsuccessful surgical outcomes are lacking. A major barrier to progress in this field has been obtaining quantitative imaging, including structural MRI (sMRI) and diffusion-weighted imaging (dMRI), clinical, and genetic data on large, geographically diverse samples of patients in whom different treatment outcomes can be evaluated. In the past, sample sizes have been insufficient to detect subtle, but reliable, brain abnormalities in patients with focal or generalized epilepsies that are genuinely associated with epilepsy and not with vicissitudes related to small or geographically restricted samples. A new, large-scale data initiative, ENIGMA4-Epilepsy, coupled with technological advancements that enable improved data harmonization are now lifting these barriers and allowing us to combine multi-site sMRI/dMRI, clinical, genetic data to predict important clinical outcomes, and making the results generalizable to a global epilepsy community. In this grant, we will leverage data collected through ENIGMA-Epilepsy—a consortium of 24 epilepsy centers from 14 countries (more than 2,250 patient and 1,727 healthy control sMRI/dMRI datasets) and the Human Epilepsy Project (HEP). We will include new network models (i.e., individualized connectomes) and polygenic risk scores (PRS) to test whether a combination of imaging, clinical, and genetic risk can accurately predict two clinical outcomes: drug-resistance and post-operative seizure outcome. Our scientific premise is that MRI-based assessment of whole-brain network properties, in combination with clinical data and PRS derived from genetic data, are able to predict (i) drug response in recently diagnosed epilepsy cases and (ii) postsurgical outcomes in individuals with drug-resistant epilepsy. This R01 addresses NIH's call for more reproducible studies by introducing a highly-powered design capable of capturing variability across patients with diverse clinical characteristics and treatment outcomes. This grant is also directly aligned with NINDS's 2020 Epilepsy Benchmarks (IIIB), which encourage the identification of genetic, clinical, and imaging biomarkers capable of predicting treatment response in epilepsy.