Deciphering principles of network dynamics underlying depression symptom severity from multi-day intracranial recordings in patients with major depression

从重度抑郁症患者多日颅内记录中解读抑郁症症状严重程度的网络动态原理

• 批准号: 10321656
• 负责人: ANDREW D KRYSTAL
• 金额: $ 20.19万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321656
• 关键词: Absence Epilepsy; Acute; Address; Affect; Amygdaloid structure; Ancillary Study; Area; Biological Markers; Brain; Chronic; Clinical Trials; Consensus; Data; Data Set; Deep Brain Stimulation; Development; Diagnosis; Diagnostic; Disease; Electric Stimulation; Electrodes; Electroencephalography; Epilepsy; Etiology; Functional disorder; Goals; Hamilton Rating Scale for Depression; Heterogeneity; Image; Implant; Individual; Information Theory; Machine Learning; Major Depressive Disorder; Maps; Measures; Mental Depression; Methods; Moods; Parents; Participant; Patients; Pattern; Periodicity; Pharmaceutical Preparations; Physiologic pulse; Population; Property; Publishing; Research; Resistance; Resolution; Rest; Severities; Site; Structure; Symptoms; Techniques; Testing; Therapeutic; Time; Work; base; biomarker development; comorbid depression; depressive symptoms; diagnostic biomarker; disability; experimental study; innovation; insight; neural network; neuroregulation; novel; novel strategies; patient subsets; relating to nervous system; response; targeted treatment; temporal measurement; therapeutic target; tool; trait; translational potential; treatment-resistant depression

PROJECT SUMMARY/ABSTRACT Major depressive disorder (MDD) is common and causes significant disability world-wide. While typically responsive to medications and therapy, there remain a subset of patients who are treatment resistant. Novel approaches are critical to treat these patients. MDD is likely caused by dysfunction in distributed neural networks, a perspective consistent with the etiological and diagnostic heterogeneity of this disorder. While imaging and electroencephalography (EEG) have helped identify MDD circuitry, no consensus has been reached on the identification of diagnostic biomarkers. Furthermore, the dynamics of MDD circuitry in relation to symptom severity is unknown. Characterization of circuit signatures that define MDD symptom severity states and the extent to which these circuits are modifiable using electrical stimulation are critical for therapeutic advancement. Intracranial EEG (iEEG) offers a high spatial and temporal resolution method to study depression networks. For the first time, we have an unparalleled opportunity to study such circuits in MDD patients participating in a clinical trial of personalized responsive neurostimulation for treatment resistant depression (PRESIDIO). In stage 1 of this trial, participants are implanted with 160 electrodes from 10 sub-chronic intracranial leads across 10 brain sites for 10 days. The goal of this parent study stage is to optimize brain-site targeting for deep brain stimulation. In this proposal, we will leverage the opportunity to study MDD circuit principles from cortical and deep brain structures over a multi-day time period. In an ancillary study to this parent clinical trial, we propose a set of experiments that establish basic principles of network dynamics underlying MDD from direct neural recordings. This proposal is organized around the principal concept that brain circuit dysfunction is reflected in abnormal signatures of functional connectivity and rhythmic local-field activity. This concept is supported by our pilot work where we found evidence of distinct MDD networks characterized by functional connectivity and spectral activity. Furthermore, in the first parent trial participant we successfully mapped MDD circuits at the individual level and found that gamma power in the amygdala could successfully decode mood state (AUC = 86%). This proposal builds on these preliminary findings in two aims. In Aim 1, we will characterize state-dependent functional connectivity and spectral activity in relation to symptom severity. In Aim 2, we will examine the manner and time course in which targeted electrical stimulation acutely modifies circuits. Together, this research will yield the first characterization of connectivity and activity dynamics in MDD over a multi-day period from direct neural recordings. This rare insight into MDD circuity provided by this novel dataset establishes proof-of-concept principles for biomarker development and therapeutic target selection that could critically advance personalized MDD treatments.

项目总结/摘要 重度抑郁症（MDD）是一种常见的疾病，在世界范围内会导致严重的残疾。尽管通常 尽管对药物和治疗有反应，但仍有一部分患者对治疗有抗性。小说 方法对于治疗这些患者至关重要。MDD可能是由分布式神经网络的功能障碍引起的， 这一观点与这种疾病的病因学和诊断异质性相一致。在成像和 虽然脑电图（EEG）有助于识别MDD电路，但尚未就MDD电路达成共识。 诊断生物标志物的鉴定。此外，与症状相关的MDD电路的动态 严重程度未知。定义MDD症状严重性状态的电路特征的表征以及 使用电刺激可改变这些电路的程度对于治疗进展是关键的。 颅内脑电（iEEG）提供了一种高空间和时间分辨率的方法来研究抑郁网络。 这是第一次，我们有一个无与伦比的机会来研究这种电路在MDD患者参加了一个 个性化反应性神经刺激治疗难治性抑郁症的临床试验（普雷西迪奥）。在阶段 在该试验的第1部分，参与者植入了来自10个亚慢性颅内电极导线的160个电极， 大脑部位10天本母研究阶段的目标是优化脑深部靶向部位 刺激.在本提案中，我们将利用机会从皮层和大脑皮层研究MDD电路原理， 大脑深层结构的变化 在这项母临床试验的辅助研究中，我们提出了一组建立基本原则的实验 从直接神经记录中了解MDD背后的网络动态。本提案围绕 脑回路功能障碍反映在功能连接的异常信号中， 有节奏的局部场活动这个概念得到了我们的试点工作的支持，我们发现了不同MDD的证据 以功能连接和光谱活动为特征的网络。此外，在第一次父母试验中， 参与者，我们成功地映射了MDD电路在个人层面，并发现伽玛功率在 杏仁核能成功解码情绪状态（AUC = 86%）。这项建议是以这些初步调查结果为基础的 两个目标。在目标1中，我们将描述状态依赖的功能连接性和光谱活性， 症状的严重程度。在目标2中，我们将检查有针对性的电气攻击的方式和时间过程 刺激会剧烈地改变神经回路总之，这项研究将首次对连通性进行表征 和活动动力学的MDD在一个多天的时间从直接神经记录。这种对MDD的罕见见解 该新数据集提供的电路建立了生物标志物开发的概念验证原则， 治疗靶点选择，可以极大地推进个性化MDD治疗。