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

识别大脑网络以预测治疗抵抗和术后结果：ENIGMA-癫痫计划

• 批准号: 10626074
• 负责人: CARRIE R MCDONALD
• 金额: $ 61.75万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-15 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10626074
• 关键词: Address; Affect; Anticonvulsants; Benchmarking; Bilateral; Brain; Characteristics; Classification; Clinical; Clinical Data; Communities; Complement; Country; Coupled; Data; Data Set; Databases; Development; Diagnosis; Diagnostic; Diffusion Magnetic Resonance Imaging; Disease; Drug resistance; Ensure; Epilepsy; Evaluation; Failure; Freedom; Frontal Lobe Epilepsy; Generalized Epilepsy; Genetic; Genetic Markers; Genetic Risk; Genetic Variation; Geography; Grant; Health; Heterogeneity; Human; Image; Individual; Infrastructure; Intractable Epilepsy; Lesion; Lifting; Lobe; Longitudinal Studies; Machine Learning; Magnetic Resonance Imaging; Meta-Analysis; Methods; Modeling; National Institute of Neurological Disorders and Stroke; Neurologic; Newly Diagnosed; Operative Surgical Procedures; Outcome; Partial Epilepsies; Patient-Focused Outcomes; Patients; Pattern; Persons; Pharmaceutical Preparations; Play; Postoperative Period; Prediction of Response to Therapy; Property; Reproducibility; Reproducibility of Results; Research; Resources; Risk Factors; Role; Sample Size; Sampling; Seizures; Series; Site; Syndrome; Technology; Temporal Lobe Epilepsy; Testing; Treatment outcome; United States National Institutes of Health; Visual; brain abnormalities; clinical biomarkers; clinical risk; cohort; comorbidity; connectome; data harmonization; design; determinants of treatment resistance; drug response prediction; frontal lobe; genetic risk factor; human old age (65+); imaging biomarker; improved; individual patient; insight; interest; large datasets; large scale data; nervous system disorder; network models; neuroimaging; patient response; polygenic risk score; predicting response; quantitative imaging; response; surgery outcome; treatment planning

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.

抽象的 癫痫是一种毁灭性的神经系统疾病，影响着全世界超过 5000 万人。 大约三分之一的患者对抗癫痫药物 (ASM) 没有反应，需要额外的治疗 诊断检查，包括考虑手术，在手术中起着关键作用。 癫痫的诊断评估，识别许多患者中与癫痫发作共存的可见病变 然而，高达 40% 的患者 MRI 表现正常，而且这个数字还在不断增长。 人们对识别微妙的大脑网络异常越来越感兴趣，这有助于描绘 致癫痫网络并有助于预测治疗反应（即对 ASM 和手术的反应） 不幸的是，无法可靠地识别哪些患者对药物有反应，哪些患者对药物有反应。 目前尚不清楚哪些患者会获得成功的手术结果，哪些患者会获得失败的手术结果。 该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，涉及大量、地域多样的数据 可以评估不同治疗结果的患者样本 在过去，样本量已经改变。 不足以检测局灶性或全身性患者的微妙但可靠的大脑异常 与癫痫真正相关的癫痫病，而不是与小或小的疾病相关的变迁。 受地理限制的样本。 一项新的大规模数据计划 ENIGMA4-Epilepsy 以及技术进步 实现改进的数据协调现在正在消除这些障碍，并使我们能够结合多站点 sMRI/dMRI、临床、遗传数据可预测重要的临床结果，并使结果具有普遍性 在这笔赠款中，我们将利用通过 ENIGMA-Epilepsy 收集的数据。 由来自 14 个国家的 24 个癫痫中心组成的联盟（超过 2,250 名患者和 1,727 名健康对照者） 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， 个体化连接组）和多基因风险评分（PRS）来测试影像学、 临床和遗传风险可以准确预测两种临床结果：耐药性和术后 我们的科学前提是基于 MRI 的全脑网络特性评估。 结合临床数据和源自遗传数据的 PRS，能够预测 (i) 药物反应 最近诊断的癫痫病例和 (ii) 耐药性癫痫患者的术后结果。 该 R01 通过引入高性能设计来满足 NIH 对更具可重复性的研究的呼吁 能够捕获具有不同临床特征和治疗结果的患者的变异性。 这笔赠款还与 NINDS 的 2020 年癫痫基准 (IIIB) 直接一致，该基准鼓励 鉴定能够预测癫痫治疗反应的遗传、临床和影像生物标志物。

项目成果

Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy.

• DOI: 10.1093/brain/awab417
• 发表时间: 2022-05-24
• 期刊: Brain : a journal of neurology

The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets.

• DOI: 10.1038/s41592-021-01186-4
• 发表时间: 2021-07
• 期刊: Nature methods
• 影响因子: 48
• 作者: Larivière S;Paquola C;Park BY;Royer J;Wang Y;Benkarim O;Vos de Wael R;Valk SL;Thomopoulos SI;Kirschner M;Lewis LB;Evans AC;Sisodiya SM;McDonald CR;Thompson PM;Bernhardt BC
• 通讯作者: Bernhardt BC

Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study.

• DOI: 10.1016/j.nicl.2021.102765
• 发表时间: 2021
• 期刊: NeuroImage. Clinical
• 作者: Gleichgerrcht E;Munsell BC;Alhusaini S;Alvim MKM;Bargalló N;Bender B;Bernasconi A;Bernasconi N;Bernhardt B;Blackmon K;Caligiuri ME;Cendes F;Concha L;Desmond PM;Devinsky O;Doherty CP;Domin M;Duncan JS;Focke NK;Gambardella A;Gong B;Guerrini R;Hatton SN;Kälviäinen R;Keller SS;Kochunov P;Kotikalapudi R;Kreilkamp BAK;Labate A;Langner S;Larivière S;Lenge M;Lui E;Martin P;Mascalchi M;Meletti S;O'Brien TJ;Pardoe HR;Pariente JC;Xian Rao J;Richardson MP;Rodríguez-Cruces R;Rüber T;Sinclair B;Soltanian-Zadeh H;Stein DJ;Striano P;Taylor PN;Thomas RH;Elisabetta Vaudano A;Vivash L;von Podewills F;Vos SB;Weber B;Yao Y;Lin Yasuda C;Zhang J;Thompson PM;Sisodiya SM;McDonald CR;Bonilha L;ENIGMA-Epilepsy Working Group
• 通讯作者: ENIGMA-Epilepsy Working Group

A worldwide ENIGMA study on epilepsy-related gray and white matter compromise across the adult lifespan

一项关于成人一生中与癫痫相关的灰质和白质损害的全球 ENIGMA 研究

• DOI: 10.1101/2024.03.02.583073
• 发表时间: 2024
• 作者: Chen J