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Identifying EEG indices of neural systems underlying risk for MDD

识别 MDD 潜在风险的神经系统脑电图指数

基本信息

批准号：
8813629
负责人：
JOHN J. ALLEN
金额：
$ 15.21万
依托单位：
UNIVERSITY OF ARIZONA
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-03-01 至 2017-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8813629
关键词：
Address Affective Biological Markers Brain Categories Clinical Cognitive Data Depressed mood Development Diagnostic Diffusion Magnetic Resonance Imaging Dimensions Disease Early Diagnosis Emotions Exploratory/Developmental Grant Functional Magnetic Resonance Imaging Future Goals Health Image Imaging Techniques Impairment Individual Lateral Lead Limbic System Link Literature Maintenance Major Depressive Disorder Measures Mental Depression Mind Modeling Nature Pathway interactions Pattern Personal Satisfaction Pilot Projects Population Predictive Value Prevalence Prevention Prevention approach Process Prospective Studies Psychopathology Recording of previous events Recurrence Research Resolution Rest Risk Risk Assessment Sampling Scanning Seeds Source Specificity Surface Symptoms System Transcend Weight Work base brain electrical activity burden of illness candidate marker clinical application clinical practice cognitive control cost cost effective depressive symptoms economic cost emotion regulation high risk improved independent component analysis indexing individualized medicine neuroimaging prospective psychologic relating to nervous system theories trait vector young adult

项目摘要

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.

描述(申请人提供)：严重抑郁障碍(MDD)不幸的是常见的沉重的疾病负担，经济和个人。鉴于抑郁症的流行及其衰弱过程，[开发对MDD及相关疾病的发展、维持和治疗具有预测价值的生物标记物具有很高的科学意义。将神经系统缺陷与MDD中功能失调的特定心理过程联系起来的生物标记物]特别有价值，因为它们可以揭示风险到症状的途径，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，但鉴于功能磁共振成像(FMRI)相对较高的成本和前瞻性研究所需的大样本，功能磁共振成像(FMRI)等成像程序不太适合研究MDD的风险前景。一种成本效益高、前景看好的战略是将成本较低、可获得更广泛的脑电活动的脑电(EEG)指数与涉及MDD的特定神经系统联系起来，[并随后在研究和临床环境中使用这些脑电生物标记物来评估风险。未来在大样本中使用具有成本效益的脑电进行的前瞻性研究将与通过功能磁共振方法确定的已建立的神经系统有明确的联系。此外，这种易于评估的生物标志物可以促进病前风险评估，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)功能磁共振成像和通道脑电数据[来自从未患抑郁症和既往患有抑郁症的年轻人]，以确定表面记录的脑电与通过RSfMRI评估的区域连通性之间的关联。我们将把最先进的方法应用到RSfMRI网络和EEG数据的研究中，包括独立分量分析和多变量向量方法。我们将考察现有EEG MDD文献所激发的EEG特征，例如额部EEG不对称，并进行更广泛的探索性分析，以确定哪些EEG特征索引了先前在MDD中被识别为调节失调的静息状态网络连接的方面。然后，我们可以评估这些脑电特征是否区分了有MDD终生病史的人和没有MDD病史的人--这有望成为MDD的风险指标--使用[目前的样本]以及我们现有的306人样本(143人有MDD病史)，他们都提供了静态脑电数据。除了RSfMRI，还将收集高分辨率T1结构图像以及扩散张量图像(DTI)，以提供EEG和RSfMRI连接的结构关联，可以以高度探索性的方式进行检查。在本申请中，我们提供的试点数据显示了这种方法的可行性，但与R21机制一致，我们认为我们的探索性方法是这一提议的优势。