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

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
依托单位国家:
美国
项目类别:
财政年份:
2021
资助国家:
美国
项目状态:
已结题
起止时间:
2021-01-01 至 2023-12-31

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中文摘要
翻译
项目摘要/摘要 严重抑郁障碍(MDD)在世界范围内很常见,并导致严重的残疾。虽然通常 对药物和治疗的反应,仍然有一部分患者对治疗有抵抗力。小说 治疗这些患者的方法至关重要。MDD很可能是由分布式神经网络的功能障碍引起的, 与这种疾病的病因学和诊断异质性一致的观点。同时进行成像和 脑电(EEG)有助于识别MDD电路,但尚未就 诊断生物标志物的鉴定。此外,MDD回路的动态与症状的关系 严重程度不得而知。定义MDD症状严重程度状态和 这些电路可以使用电刺激进行修改的程度对治疗的进步至关重要。 颅内脑电(IEEG)为研究抑郁症网络提供了一种高空间和时间分辨率的方法。 第一次,我们有一个无与伦比的机会来研究MDD患者的这种回路 个体化反应性神经刺激治疗难治性抑郁症的临床试验在舞台上 在这项试验中,参与者被植入了来自10个亚慢性颅内导联的160个电极,横跨10个 大脑部位10天。这个家长研究阶段的目标是优化大脑深部的脑部定位 刺激。在本计划中,我们将利用这个机会从Cortical和 大脑深层结构在多天的时间段内。 在这项母体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 从直接的神经记录中了解MDD背后的网络动力学。这项提案是围绕 大脑回路功能障碍的主要概念反映在功能连接的异常信号和 有节奏的局域活动。这个概念得到了我们的试点工作的支持,我们在试点工作中发现了明显的MDD证据 以功能连通性和光谱活性为特征的网络。此外,在第一次家长审判中, 参与者我们成功地绘制了个体水平的MDD电路,并发现 杏仁核能够成功解码情绪状态(AUC=86%)。这项建议建立在这些初步调查结果的基础上 有两个目标。在目标1中,我们将表征依赖于状态的函数连通性和光谱活性之间的关系 到症状严重程度。在目标2中,我们将研究目标电气的方式和时间进程 刺激强烈地改变了电路。总而言之,这项研究将首次对连通性进行表征 以及来自直接神经记录的MDD在多天期间的活动动态。这是对MDD的罕见洞察 这一新的数据集提供的迂回为生物标记物的开发和 治疗靶点的选择可以关键地推进个性化的MDD治疗。
英文摘要
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.
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