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

癫痫抑郁症的分布式网络：基于计算电路的生物标志物开发方法

• 批准号: 10016854
• 负责人: Katherine W Scangos
• 金额: $ 20.02万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10016854
• 关键词: Address; Adult; Advisory Committees; Affect; Amygdaloid structure; Antiepileptic Agents; Anxiety Disorders; Atrophic; Attention; Automobile Driving; Belief; Biological Markers; Biometry; Brain; Complex; Computer Models; Data; Data Set; Electroencephalography; Epilepsy; Etiology; Excision; Functional disorder; General Population; Goals; Hippocampus (Brain); Incidence; Inflammation; Intractable Epilepsy; Lead; Link; Machine Learning; Major Depressive Disorder; Measures; Mental Depression; Mental Health Services; Mentors; Methods; Modeling; Montgomery and Asberg depression rating scale; Mood Disorders; Moods; Neurology; Operative Surgical Procedures; Outcome; Partial Epilepsies; Patients; Pharmaceutical Preparations; Prediction of Response to Therapy; Prevalence; Psychiatry; Publishing; Quality of life; Refractory Disease; Research; Research Personnel; Rest; Seizures; Severities; Stress; Structure; Techniques; Temporal Lobe Epilepsy; Testing; Time; Training; Treatment outcome; Work; anxiety spectrum disorders; base; biomarker development; career; comorbid depression; comorbidity; computational neuroscience; computerized tools; depressive symptoms; experience; improved; insight; medication compliance; network dysfunction; neural circuit; neural network; neural patterning; neurophysiology; neurosurgery; novel; personalized medicine; potential biomarker; predictive marker; relating to nervous system; response; signal processing; statistics; suicide rate; temporal measurement

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.

项目总结/摘要 成年癫痫患者的抑郁症和其他精神疾病的患病率增加， 病态癫痫患者的抑郁与更差的结局和生活质量相关。然而， 诊断不足和未得到治疗，进一步关注这一合并症至关重要。我的职业目标是 成为一名学术神经科学家和临床医生，专注于了解共同神经网络的基础。 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，并且通常受到不同的沉淀物（即压力， 炎症、癫痫）。在这个提议中，我们的模型是，这些大脑回路中的一组神经特征将 被许多患有抑郁症的患者所分享。证据表明癫痫与 抑郁症包括在癫痫发作之前、期间、之后和之间出现抑郁症状， 有证据表明，抑郁症和癫痫同时发作的病例， 当边缘系统结构参与癫痫发作时，抑郁症的发生，以及抑郁症评分可能 药物难治性癫痫手术切除后降低。颅内脑电图（iEEG） 在手术前记录期间捕获的数据为研究抑郁症提供了一种特别有前途的方法 网络在成人癫痫，提供高的时间分辨率和空间精度。尽管面临巨大 虽然iEEG的潜力，但迄今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障碍。这些研究对于更好地 了解共病抑郁症的病因，并可能导致新的个性化治疗。 在我们的试点工作中，我们确定了一组皮质边缘回路内的功率谱测量值， 与抑郁症相关，因此是共病抑郁症的潜在生物标志物。我们还发现了证据 这支持了测试神经特征是否能预测治疗结果的基础。这一建议建立 基于这些初步发现来验证我们的模型，并检验一组神经特征是共享的这一假设。 在一些患有癫痫MDD的受试者中， 发作间期iEEG记录。目的1阐明静息态神经回路异常与脑缺血的关系 和抑郁症目标2测试移除回路的功能障碍区域是否可以改善抑郁症， 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，我需要在计算神经科学方面进行更严格的训练， 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， 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 必要的经验，成为一个完全独立的调查员谁带来的计算工具， 精神卫生研究服务的方法和癫痫的新型个性化治疗范例。