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: 10238999
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10238999
• 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; psychiatric comorbidity; 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.

项目摘要/摘要 成年癫痫患者的严重抑郁症和其他精神病患者的患病率增加 病态。癫痫的抑郁症与较差的结果和生活质量有关。但是，它继续 被诊断和未经治疗的诊断不足，并且对合并症的进一步关注至关重要。我的职业目标是 成为一名学术神经科学家和临床医生，专注于了解共同共同的神经网络 癫痫患者的病态情绪和焦虑症障碍。 特定的脑回路可能是抑郁症的基础，通常会受到不同的降水剂的影响（即应力， 炎症，癫痫）。在此提案中，我们的模型是这些大脑电路中的一组神经特征将 在许多合并抑郁症患者中分享。癫痫之间有牢固关系的证据 抑郁症包括在癫痫发作之前，期间，之后和之间存在抑郁症状的存在， 抑郁和癫痫发作的病例的证据，发病率增加 当癫痫发作的边缘结构涉及抑郁症时，抑郁得分的证据可能 手术切除后的药物难治性癫痫后要较低。颅内脑电图（IEEG） 在外科录制期间捕获的捕获期提供了一种特别有前途的研究抑郁症的方法 成人癫痫的网络，提供高时间分辨率和空间精度。尽管很大 IEEG的潜力，迄今为止尚无研究检查网络的神经生理特征 癫痫患者的情绪和焦虑症功能障碍。这样的研究至关重要 了解联合抑郁症的病因，并可能导致新型的个性化疗法。 在我们的飞行员工作中，我们确定了一组Porticolimbic电路中的一组功率光谱措施 与抑郁症有关，因此是合并抑郁症的潜在生物标志物。我们还找到了证据 这支持测试神经特征是否可以预测治疗结果的基础。该建议建立 在这些初步发现中，以验证我们的模型并检验以下假设：一组神经特征是共享的 在某些患有MDD癫痫的受试者中，可以使用机器学习技术检测到 临时IEEG录音。 AIM 1证明了静止状态神经回路异常之间的关系 和抑郁。 AIM 2测试是否去除电路功能失调区域是否可以改善抑郁症和 术前休息状态IEEG是否预测了这一改进。 为了解决这些研究目标，我将需要在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选择的指导和 跨精神病学领域，神经外科，神经病学和统计的咨询团队将使我获得 成为完全独立的调查员的必要经验，他带来了计算的工具 在癫痫中为心理健康研究和新型个性化治疗范式服务的方法。

项目成果

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder.

闭环神经刺激用于生物标志物驱动的重度抑郁症的个性化治疗。

• DOI: 10.3791/65177
• 发表时间: 2023
• 期刊: Journal of visualized experiments : JoVE
• 作者: Sellers,KristinK;Khambhati,AnkitN;Stapper,Noah;Fan,JolineM;Rao,VikramR;Scangos,KatherineW;Chang,EdwardF;Krystal,AndrewD
• 通讯作者: Krystal,AndrewD