Identifying EEG indices of neural systems underlying risk for MDD

描述（由申请人提供）：重度抑郁症（MDD）是一种不幸的常见疾病，给经济和个人带来了沉重的负担。考虑到抑郁症的普遍性及其衰弱过程，开发对重度抑郁症及相关疾病的发展、维持和治疗具有预测价值的生物标志物具有很高的科学意义。将神经系统缺陷与特定心理过程（在重度抑郁症中功能失调）联系起来的生物标志物特别有价值，因为它们可以揭示风险-症状途径，这可能是未来治疗和预防的目标。尽管神经成像在MDD中产生了令人印象深刻的回报，但成像程序，如功能磁共振成像（fMRI）并不适合研究MDD的前瞻性风险，因为fMRI的成本相对较高，而且前瞻性研究需要大量的样本。一种具有成本效益和前景的策略是将成本较低且更广泛可用的脑电（EEG）脑活动指数与MDD相关的特定神经系统联系起来，然后在研究和临床环境中使用这些EEG生物标志物来评估风险。未来在大样本中使用具有成本效益的脑电图的前瞻性研究将与fMRI方法确定的已建立的神经系统有明确的联系。此外，这些易于评估的生物标志物可以促进发病前风险评估，促进早期诊断，并为高危人群提供个性化的治疗和预防方法。考虑到这些目标，[并受到抑郁易感性的认知神经情绪调节框架的激励]，我们建议同时收集静息状态（RS） fMRI和64通道EEG数据[来自从未抑郁和以前抑郁的年轻人]，以确定表面记录的EEG与RSfMRI评估的区域连接之间的关联。我们将采用尖端的方法来检查RSfMRI网络和脑电图数据，包括独立成分分析和多元向量方法。我们将研究现有脑电图MDD文献所激发的脑电图特征，如额叶脑电图不对称，并进行更广泛的探索性分析，以确定哪些脑电图特征是静息状态网络连接的指数方面，这些方面在MDD中已被确定为失调。然后，我们可以评估这些脑电图特征是否能区分有终身重度抑郁症病史的个体和没有终生重度抑郁症病史的个体——这可能是重度抑郁症的风险指标——使用[目前的样本和]我们现有的306个样本（143个有重度抑郁症病史），所有这些人都提供了静息脑电图数据。除了RSfMRI外，还将收集高分辨率T1结构图像以及弥散张量图像（DTI），以提供EEG和RSfMRI连接的结构相关性，可以以高度探索性的方式进行检查。在此应用程序中，我们提供了显示该方法可行性的试点数据，但与R21机制一致，我们认为我们的探索性方法是该提案的优势。

DESCRIPTION (provided by applicant): Major Depressive Disorder (MDD) is unfortunately common with substantial burden of disease, economically and personally. Given the prevalence of depression and its debilitating course, [developing biomarkers that that have predictive value for the development, maintenance, and treatment of MDD and related disorders is of high scientific significance. Biomarkers that link deficits in neural systems to specific psychological processes that are dysfunctional in MDD] are especially valuable because they can reveal risk-to-symptom pathways that may be future targets for treatments and preventions. Although neuroimaging in MDD has generated impressive returns, imaging procedures such as functional magnetic resonance imaging (fMRI) are not well- suited for studying prospective of risk for MDD, given the relatively high cost of fMRI and the large samples required for prospective studies. A cost-effective and promising strategy would be to link less costly and more widely-available electroencephalographic (EEG) indices of brain activity to specific neural systems involved in MDD, [and subsequently to use these EEG biomarkers in assessing risk in research and clinical settings. Future prospective research using cost-effective EEG in large samples would have a clear link to established neural systems identified with fMRI approaches. Moreover, such easily-assessed biomarkers can promote premorbid risk assessment, facilitate early diagnosis, and lead to individually-tailored treatment and] prevention approaches for high-risk populations. With these goals in mind, [and motivated by a cognitive-neural emotion- regulation framework of depressive vulnerability,] we propose to collect simultaneous resting-state (RS) fMRI and 64-channel EEG data [from never-depressed and previously-depressed young adults], to identify associations between surface-recorded EEG and regional connectivity assessed via RSfMRI. We will apply cutting-edge approaches to the examination of RSfMRI networks and EEG data, including independent components analysis and multivariate vector approaches. We will examine EEG features motivated by extant EEG MDD literature, such as frontal EEG asymmetry, and also conduct broader exploratory analyses, to identify which EEG features index aspects of resting state network connectivity that have previously been identified as dysregulated in MDD. We can then assess whether these EEG features differentiate individuals with a lifetime history of MDD from those without - which would be expected of a risk indicator for MDD - using [the present sample and also] our extant sample of 306 individuals (143 with a history of MDD), all of whom have provided resting EEG data. In addition to the RSfMRI, high resolution T1 structural images as well as diffusion tensor images (DTI) will be collected to provide structural correlates of EEG and RSfMRI connectivity that can be examined in a highly exploratory manner. In this application we provide pilot data showing the feasibility o this approach, but consistent with the R21 mechanism, we consider our exploratory approach to be a strength of this proposal.