Optimized Deep Brain Stimulation for Epileptic Encephalopathies

在英国，有60万患者患有癫痫。对于30%的患者，癫痫发作无法通过可用的抗癫痫药物控制。遗传性癫痫的难治性病例的发生率可能更高，60%的SYNGAP 1相关失神发作病例对药物治疗仍有抵抗力。SYNGAP 1基因编码神经元突触连接的强大调节因子。SYNGAP 1致病性突变是神经发育障碍、智力残疾和癫痫的重要预测因子。另一个重要的预测因子是SCN 2A基因的突变。SCN 2A编码电压门控钠通道，该通道对于突触电位的产生和传播至关重要。SCN 2A相关癫痫的耐药性也在60%左右。SYNGAP 1和SCN 2A障碍都被归类为癫痫性脑病，其中认知和行为缺陷被认为会随着不受控制的癫痫发作而恶化。因此，迫切需要开发阻断患者癫痫发作的新型治疗策略。脑内电刺激或脑深部电刺激（DBS）是一种经批准的治疗顽固性癫痫的方法。尽管如此，它还没有被测试在遗传性癫痫或治疗失神发作。此外，DBS可以显著优化，因为刺激参数，例如每个电脉冲的强度、频率和持续时间，通常在没有明确理由的情况下选择。在导致DBS批准用于癫痫的主要临床试验中，中位癫痫发作减少了56%，尽管81例患者中只有6例完全无癫痫发作。因此，DBS可能会阻止癫痫性脑病（如SYNGAP 1和SCN 2A疾病）的发作，并且刺激参数可以识别以获得最大疗效。为了测试这一点，可以利用新的大鼠模型，包括SYNGAP 1疾病模型，其中基因的关键GAP结构域被删除，以及SCN 2A杂合敲除模型。对于缺失（Syngap+/A-GAP）杂合子的动物表现出认知、社交和睡眠异常，以及EEG电极之间的连接性降低和自发性失神发作的高比率。初步研究表明，SCN 2A杂合子动物（Scn 2a +/-）也表现出自发失神发作的高发生率，将对该模型的行为和癫痫发作表型进行进一步研究。该项目将确定具有优化刺激参数的脑深部电刺激（DBS）是否可以阻断癫痫性脑病大鼠模型的癫痫发作，并测试改进的DBS是否可以使患者受益。将根据遗传性癫痫啮齿动物模型（如Syngap+/A-GAP和Scn 2a +/-大鼠）的EEG和多部位记录确定阻断失神发作的优化DBS参数。Gonzalez-Sulser实验室提供的记录，来自被认为介导失神癫痫发作的丘脑-皮质回路，将被分析，并开发算法来估计癫痫发作前后大脑功能连接的低维表示，结合机器学习和网络控制理论，将被用来探索刺激参数空间。使用确定的优化参数，将在癫痫性脑病大鼠模型中进行DBS，以确定DBS是否可以阻断失神发作并检查其临床潜力。然后，将在人类EEG中验证EEG刺激参数。我们将确定Zuberi实验室提供的SYNGAP 1患者EEG数据中的失神发作是否显示出与Syngap+/A-GAP大鼠相似的活动动力学，以及是否可以在患者中使用优化的刺激参数。我们的目标是对SCN 2A患者EEG数据进行类似的评价。

In the UK, 600,000 patients have epilepsy. For 30% of these patients, seizures are not controlled by available anti-seizure medication. The rate of refractory cases can be higher for genetic epilepsies, with 60% of SYNGAP1-associated absence seizures cases remaining resistant to drug treatment. The SYNGAP1 gene encodes a powerful regulator of neuronal synaptic connectivity. SYNGAP1 pathogenic mutations are an important predictor of neurodevelopmental disorders, intellectual disability, and epilepsy. Another important predictor are mutations in the SCN2A gene. SCN2A encodes voltage-gated sodium channels critical for actional potential generation and propagation. Drug resistance for SCN2A-associated epilepsy is also around 60%. Both SYNGAP1 and SCN2A disorder are classified as an epileptic encephalopathy, in which cognitive and behavioural deficits are thought to worsen with uncontrolled seizures. Therefore, there is a critical need to develop novel therapeutic strategies that block seizures in patients. Electrical stimulation within the brain, or deep brain stimulation (DBS), is an approved treatment for intractable epilepsies. Nonetheless, it has not been tested in genetic epilepsies or to treat absence seizures. Furthermore, DBS could be significantly optimized as stimulation parameters such as the strength, frequency and duration of each electrical pulse are often chosen without clear rationale. In the main clinical trial leading to DBS approval for epilepsy, there was a 56% reduction in median seizures, although only 6 of 81 patients achieved total seizure freedom. Thus, DBS could potentially stop seizures in epileptic encephalopathies such as SYNGAP1 and SCN2A disorders, and stimulation parameters may be identifiable for maximum efficacy. To test this, new rat models can be utilised, including a model of SYNGAP1 disorder in which the critical GAP domain of the gene is deleted and a SCN2A heterozygous knockout model. Animals heterozygous for the deletion (Syngap+/A-GAP) demonstrate cognitive, social, and sleep abnormalities, as well as decreased connectivity between EEG electrodes and a high rate of spontaneous absence seizures. Preliminary investigation indicates that SCN2A heterozygous animals (Scn2a+/-) also demonstrate high rates of spontaneous absence seizures, and there will be further investigation into the behavioural and seizure phenotypes of this model. This project will determine whether deep brain stimulation (DBS) with optimised stimulation parameters can block seizures in rat models of epileptic encephalopathy and test whether improved DBS could benefit patients. Optimised DBS parameters to block absence seizures will be determined from EEG and multi-site recordings from genetic epilepsy rodent models such as Syngap+/A-GAP and Scn2a+/- rats. The recordings available in the Gonzalez-Sulser lab, from thalamo-cortical circuits thought to mediate absence seizures, will be analysed, and algorithms developed to estimate a low-dimensional representation of brain functional connectivity around seizure onset, which in combination with machine learning and network control theory, will be utilized to explore the stimulation parameter space. Using the optimised parameters identified, DBS will be performed in the rat models of epileptic encephalopathy to determine whether DBS can block absence seizures and examine its clinical potential. Then, EEG stimulation parameters will be validated in human EEG. We will determine whether absence seizures in SYNGAP1 patient EEG data, available in the Zuberi lab, display similar activity dynamics as Syngap+/A-GAP rats and, whether optimized stimulation parameters could be utilized in patients. We aim that SCN2A patient EEG data will be similarly evaluated.