Sleep-EEG Predictors of Functional Outcome after TBI

TBI 后功能结果的睡眠脑电图预测因子

• 批准号: 9136515
• 负责人: MO MODARRES
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9136515
• 关键词: Activities of Daily Living; Address; Animal Model; Animals; Biological Markers; Brain; Brain Injuries; Clinical Markers; Collaborations; Control Animal; Coupling; Data; Databases; Development; Electroencephalography; Employment; Family; Frequencies; Functional disorder; Future; Human; Individual; Individual Differences; Investigation; Measures; Medical center; Meta-Analysis; Methods; Modeling; Neurology; Neuropsychology; Neurorehabilitation; Outcome; Patients; Pattern; Publishing; Qualifying; REM Sleep; Recording of previous events; Recovery of Function; Rehabilitation therapy; Research Personnel; Resources; Risk; Sleep; Sleep disturbances; Structure; Testing; Time; Traumatic Brain Injury; Traumatic Brain Injury recovery; Triage; Veterans; Work; awake; base; cohort; community reintegration; disability; follow-up; functional improvement; functional outcomes; improved; innovation; neural circuit; neuropsychiatry; non rapid eye movement; novel; outcome forecast; outcome prediction; prevent; public health relevance; signal processing; socioeconomics; treatment program; treatment response

 DESCRIPTION (provided by applicant): Traumatic brain injury (TBI), particularly moderate to severe TBI, is a major cause of physical and neuropsychiatric disability in Veterans, preventing community reintegration and return to employment. Early prognostication of outcome from TBI is a critical need which is currently lacking, and is necessary to optimally allocate scarce resources from existing VA treatment programs, and to better inform patients and their families about the prognosis. Current predictors of long-term functional outcome after TBI are generally based on demographic/socioeconomic and clinical markers, and have shown only moderate predictive ability; they have not been sufficiently precise to direct therapy decisions in individuals. To address the critical need for sensitive/specific functional outcome predictors after TBI, we propose to test novel predictors of functional outcome after TBI, which are based on quantitative analysis of electroencephalography (EEG) during sleep. The structure of the analysis is based on measures of cross- frequency couplings (CFC) between EEG frequency bands, reflecting coordination between neural circuits that generate the underlying rhythmic and oscillatory pattern of EEG. In our preliminary animal studies, we have identified several EEG neuromarkers that were highly sensitive to the TBI group. Next, we examined these same neuromarkers and their correlation with functional outcomes (Functional Independence Measure (FIM) and Disability Rating Scale (DRS)) in a small cohort (n=7) of Veterans with moderate-to-severe TBI who received neuro-rehabilitation. We found that one particular sleep-based EEG neuromarker, delta-gamma cross-frequency coupling, very strongly and significantly predicted functional improvement after rehabilitation (DRS regression model: R2=0.95, F=86, p < 0.0002; FIM regression model: R2 =0.91, F= 50, p < 0.001). Importantly, we found the same strong predictive capability at a follow-up time point one-year later, not only indicating robust internal consistency, but also highlighting the potential to identify a valuable, ultra-long term predictor f outcome, (FIM regression model: R2 =0.89, F= 40, p < 0.002) . The main objective of this SPiRE project is to comprehensively evaluate the ability of EEG CFC-based neuromarker to distinguish TBI from healthy control, and to predict Functional Outcomes of Veterans with moderate-severe TBI. This will be accomplished by computing the delta-gamma neuromarkers using an existing database of recorded sleep-EEG studies from 45 healthy individuals, and 80 Veterans with moderate- severe TBI, in conjunction with their measures of functional outcome (DRS and FIM) at baseline, at discharge from neuro-rehabilitation, and at one year post discharge. We will also explore the development of potentially improved neuromarkers by generalizing our analyses to include computation of EEG cross frequency couplings between an expanded pairs of frequency bands including theta, alpha, delta and gamma, and for 3 states of awake, non-REM, and REM sleep. Expected Outcomes: This study will evaluate novel objective neuromarkers that can predict functional recovery and response to treatment in moderate-severe TBI patients.

 描述（由申请人提供）： 创伤性脑损伤（TBI），特别是中度至重度TBI，是退伍军人身体和神经精神残疾的主要原因，阻碍了社区重返社会和重返就业。TBI结果的早期诊断是目前缺乏的关键需求，并且对于从现有VA治疗计划中优化分配稀缺资源以及更好地告知患者及其家属预后是必要的。目前TBI后长期功能结局的预测因素通常基于人口统计学/社会经济学和临床标志物，并且仅显示出中等预测能力;它们还不够精确，无法指导个体的治疗决策。 为了解决TBI后敏感/特异性功能结果预测的关键需求，我们建议测试TBI后功能结果的新预测因子，其基于睡眠期间脑电图（EEG）的定量分析。分析的结构基于EEG频带之间的交叉频率耦合（CFC）的测量，其反映了产生EEG的潜在节律和振荡模式的神经回路之间的协调。在我们的初步动物研究中，我们已经确定了几个脑电图神经标记物，是高度敏感的TBI组。接下来，我们在接受神经康复治疗的中重度TBI退伍军人小队列（n=7）中检查了这些相同的神经标志物及其与功能结局（功能独立性测量（Functional Independence Measure，FINS）和残疾评定量表（Disability Rating Scale，DRS））的相关性。我们发现，一种特殊的基于睡眠的EEG神经标志物，δ-γ交叉频率耦合，非常强烈和显著地预测康复后的功能改善（DRS回归模型：R2=0.95，F=86，p < 0.0002; R2 =0.91，F= 50，p < 0.001）。重要的是，我们在一年后的随访时间点发现了同样强大的预测能力，不仅表明了强大的内部预测能力， 一致性，但也突出了鉴定结果的有价值的超长期预测因子的潜力（回归模型：R2 =0.89，F= 40，p < 0.002）。 该SPiRE项目的主要目的是全面评估EEG CFC神经标志物区分TBI和健康对照的能力，并预测中重度TBI退伍军人的功能结局。这将通过使用来自45名健康个体和80名患有中重度TBI的退伍军人的记录睡眠EEG研究的现有数据库，结合他们在基线、神经康复出院和出院后一年的功能结局（DRS和DRS）测量值，计算δ-γ神经标志物来实现。我们还将通过概括我们的分析来探索潜在改进的神经标记物的开发，以包括对包括θ、α、δ和γ在内的扩展频带对之间的EEG交叉频率耦合的计算，以及清醒、非REM和REM睡眠的3种状态。 预期结果：这项研究将评估新的客观神经标志物，可以预测功能恢复和治疗反应的中重度TBI患者。