Distributed networks underlying depression in epilepsy: a computational circuit-based approach to biomarker development

项目摘要/摘要 成年癫痫患者严重抑郁和其他精神疾病的患病率增加 病态。癫痫患者的抑郁与较差的预后和生活质量有关。然而，它仍在继续 未得到充分诊断和治疗，并进一步注意这一共病是至关重要的。我的职业目标是 成为一名学术神经学家和临床医生，专注于了解共同疾病背后的神经网络 癫痫患者的病态情绪和焦虑谱障碍。 特定的大脑回路可能是抑郁症的基础，并通常受到不同沉淀物的影响(即应激， 炎症、癫痫)。在这个提议中，我们的模型是，这些大脑回路上的一组神经功能将 在许多患有抑郁症的患者中共享。癫痫之间有很强联系的证据 抑郁症包括癫痫发作前、发作中、发作后和发作之间出现的抑郁症状， 有证据表明抑郁症和癫痫同时发作，发作间歇期的发生率增加 边缘结构参与癫痫发作时的抑郁，以及抑郁评分可能 药物难治性癫痫手术切除后较低。脑电(IEEG) 在手术前记录期间捕捉到的数据为研究抑郁症提供了一个特别有希望的方法 成人癫痫网络，提供高时间分辨率和空间精度。尽管有巨大的 IEEG的潜力，到目前为止还没有研究检查网络的神经生理特征 癫痫患者的情绪障碍和焦虑症。这样的研究对于更好地 了解共病抑郁的病因，并可能导致新的个性化治疗。 在我们的试点工作中，我们在皮质边缘回路中识别了一组功率谱测量，它们似乎是 与抑郁症有关，因此是共病抑郁症的潜在生物标记物。我们还找到了证据 这支持了测试神经特征是否会预测治疗结果的基础。这项建议建立了 根据这些初步发现来验证我们的模型，并测试一组神经特征共享的假设 在癫痫中患有MDD的一些受试者中，使用机器学习技术可以检测到 发作间歇期的iEEG记录。目的1论证静息状态神经回路异常之间的关系 和抑郁症。目的2测试移除回路的功能障碍区域是否可以改善抑郁和 术前静息状态iEEG是否能预测这种改善。 为了实现这些研究目标，我将需要在复杂的计算神经科学方面进行更严格的培训 数据集、高级信号处理和生物统计学。我的培训计划和精心挑选的指导和 横跨精神病学、神经外科、神经病学和统计学领域的咨询团队将使我能够获得 成为一名完全独立的调查员所需的经验，他们带来了计算工具 提供精神健康研究服务的途径和癫痫个性化治疗的新范例。

PROJECT SUMMARY/ABSTRACT Adult patients with epilepsy have an increased prevalence of major depression and other psychiatric co- morbidities. Depression in epilepsy is associated with worse outcome and quality of life. However, it continues to be underdiagnosed and untreated and further attention to this comorbidity is critical. My career goal is to become an academic neuroscientist and clinician focused on understanding the neural networks underlying co- morbid mood and anxiety spectrum disorders in patients with epilepsy. Specific brain circuits may underlie depression and be commonly affected by different precipitants (i.e. stress, inflammation, epilepsy). In this proposal, our model is that a set of neural features across these brain circuits will be shared across many patients with co-morbid depression. Evidence for a strong relationship between epilepsy and depression includes the presence of depression symptoms before, during, after, and in between seizures, evidence of cases of concurrent onset of depression and epilepsy, an increased incidence of interictal depression when limbic structures are involved in seizure occurrence, and evidence that depression scores may be lower after surgical resection for medication refractory epilepsy. Intracranial electroencephalography (iEEG) captured during the pre-surgical recording period offers a particularly promising method to study depression networks in adult epilepsy, offering both high temporal resolution and spatial precision. Despite the enormous potential of iEEG, there are no studies to date that examine the neurophysiological signatures of network dysfunction in mood and anxiety disorders in patients with epilepsy. Such studies are critical in order to better understand the etiology of co-morbid depression and could lead to novel personalized therapies. In our pilot work, we identify a set of power spectral measures within a corticolimbic circuit that appear to be linked to depression and are, therefore, a potential biomarker of co-morbid depression. We also found evidence that supports the basis for testing whether neural features will predict treatment outcome. This proposal builds on these preliminary findings to validate our model and test the hypothesis that a set of neural features is shared across some subjects with MDD in epilepsy and is detectable with machine-learning techniques applied to interictal iEEG recordings. Aim 1 demonstrates the relationship between resting state neural circuit abnormality and depression. Aim 2 tests whether removing the dysfunctional region of the circuit improves depression and whether the presurgical resting state iEEG predicts that improvement. To address these research goals, I will need more rigorous training in computational neuroscience for complex datasets, advanced signal processing, and biostatistics. My training plan and carefully selected mentoring and advisory team across fields of psychiatry, neurosurgery, neurology and statistics will allow me to obtain the necessary experiences to become a fully independent investigator who brings the tools of computational approaches to the service of mental health research and novel personalized treatment paradigms in epilepsy.