Diagnostic Machine Learning Algorithm to Identify MEG Features of Mild TBI and Comorbid PTSD

用于识别轻度 TBI 和共病 PTSD 的 MEG 特征的诊断机器学习算法

• 批准号: 10651625
• 负责人: MINGXIONG HUANG
• 金额: --
• 依托单位: VA SAN DIEGO HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10651625
• 关键词: Address; Algorithms; Amygdaloid structure; Animals; Area; Attention; Behavior Disorders; Behavioral; Brain; Chronic; Classification; Clinical; Cognition; Cognition Disorders; Cognitive; Cognitive deficits; Control Groups; Data; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Diffuse Axonal Injury; Diffusion; Electromagnetics; Emotional; Emotions; Functional disorder; General Population; Goals; Grant; Hyperactivity; Imaging technology; Impaired cognition; Individual; Injury; Interneurons; Machine Learning; Magnetic Resonance Imaging; Magnetoencephalography; Mental disorders; Neurobiology; Neurologic; Neurologic Symptoms; Neurons; Neuropsychology; Parietal; Parvalbumins; Patients; Post-Concussion Syndrome; Post-Traumatic Stress Disorders; Prefrontal Cortex; Quality of life; Recovery; Research; Rest; Risk; Role; Scientist; Sensitivity and Specificity; Short-Term Memory; Signal Transduction; Slow-Wave Sleep; Source; Symptoms; Techniques; Validation; Veterans; X-Ray Computed Tomography; artificial neural network; clinical application; clinical care; cognitive function; comorbidity; diagnostic accuracy; diagnostic algorithm; diagnostic criteria; executive function; gray matter; imaging biomarker; improved; injured; interest; machine learning algorithm; mild traumatic brain injury; neuroimaging; neurophysiology; neuropsychiatry; novel; novel strategies; processing speed; response; success; white matter

Mild traumatic brain injury (mTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral deficits in OEF/OIF/OND Veterans and the general public. However, the underlying pathophysiology and recovery mechanisms, especially those associated with cognitive functioning in mTBI, are not completely understood. The neuronal mechanisms for the increased risk of PTSD after an mTBI are even less clear. Conventional MRI and CT images are generally negative even in patients with persistent post-concussive symptoms (PCS) and/or PTSD symptoms. Diffusion-based MRI techniques have been developed to identify abnormalities in white-matter tracts, owing to the major role of diffuse axonal injury (DAI) in mTBI. Yet even sophisticated diffusion-based MRI techniques are not sufficiently sensitive for reliable clinical applications. Recent animal studies indicate that gray matter is also vulnerable to DAI, which leads to abnormal electromagnetic signals from the injured regions. In this regard, support is mounting for the sensitivity of resting-state magnetoencephalography (rs-MEG) source imaging markers for detecting neuronal abnormalities in mTBI. We demonstrated that rs-MEG delta-wave (1-4 Hz) markers were very sensitive in distinguishing mTBI patients with persistent PCS from neurologically intact individuals. We also found that rs-MEG gamma- band (30-80 Hz) markers show marked hyperactivity in mTBI, possibly due to injury of GABA-ergic parvalbumin-positive (PV+) interneurons. In addition, we found that task-evoked MEG (te-MEG) recordings during working memory (WM) task detected abnormal signals throughout the brain in mTBI that were related to poorer cognitive functioning. A main goal of this application is to develop highly sensitive diagnostic algorithms to differentiate Veterans with mTBI from those with comorbid mTBI and PTSD, and those healthy control Veterans. The new approaches will use artificial neural network based machine-learning techniques to integrate rs-MEG and te-MEG imaging makers. We will study three groups of Veterans (N=75 per group): 1) individuals with mTBI and persistent PCS (mTBI-only group); 2) individuals with comorbid mTBI and PTSD who have persistent PCS and PTSD symptoms; 3) healthy controls (HC). Aim 1 will establish a machine- learning based MEG diagnostic algorithm for mTBI that optimally integrates three MEG regional imaging markers (i.e., delta-band and gamma-band rs-MEG; WM evoked MEG) to differentiate Veterans with mTBI (mTBI-only and comorbid mTBI-PTSD) from HC Veterans with >90% accuracy. We predict that sensitive features for mTBI classification will include abnormal increases in rs-MEG delta- and gamma-band activity in prefrontal and posterior-parietal areas and aberrant WM evoked activity in the mTBI-only and comorbid groups relative to the HC group. Aim 2 will develop a machine-learning MEG algorithm that integrates rs-MEG activity and te-MEG responses evoked by a negative emotion processing picture (NEPP) task to differentiate Veterans with mTBI-only from those with comorbid mTBI-PTSD with > 90% accuracy. We predict that comorbid mTBI- PTSD group will show increases in rs-MEG (beta-band) and NEPP te-MEG activity from amygdala and decreases in activity from ventromedial prefrontal cortex (vmPFC), dorsolateral PFC (dlPFC), and precuneus over the mTBI-only group. Aim 3 will examine the correlates of abnormal MEG-based neurophysiological features in mTBI-only and comorbid mTBI-PTSD with clinical symptoms, cognitive impairments, and real-world quality of life. We predict that rs-MEG and WM te-MEG in specific prefrontal and posterior parietal areas will correlate with PCS symptoms and cognitive deficits. In the comorbid mTBI-PTSD group, PTSD symptoms will correlate with abnormal rs-MEG and NEPP te-MEG hyperactivity in the amygdala, and abnormal rs-MEG hypoactivity in the vmPFC, dlPFC, and precuneus. The success of this project will significantly improve neuroimaging-based techniques that can effectively aid in the diagnosis of mTBI and better characterize the relationships among neurobiological, neuropsychological, and neuropsychiatric effects of mTBI and PTSD.

轻度创伤性脑损伤（MTBI）是持续身体，认知，情感和 OEF/OIF/OND退伍军人和公众的行为缺陷。但是，基本的病理生理学 和恢复机制，尤其是与MTBI认知功能相关的机制，不是完全 理解。 MTBI后PTSD风险增加的神经元机制甚至不太清楚。 传统的MRI和CT图像即使在持续脑后的患者中也是负面的 症状（PC）和/或PTSD症状。已经开发了基于扩散的MRI技术来识别 由于MTBI中弥漫性轴突损伤（DAI）的主要作用，白色物质的异常。均匀 基于复杂的扩散MRI技术对可靠的临床应用不足以敏感。 最近的动物研究表明，灰质也容易受到DAI的影响，这导致异常 受伤区域的电磁信号。在这方面，支持的敏感性是 静止状态磁脑摄影（RS-MEG）源成像标记，用于检测神经元异常 在mtbi。我们证明了RS-MEG Delta-Wave（1-4 Hz）标记在区分方面非常敏感 来自神经系统完整个体的MTBI患者。我们还发现RS-MEG Gamma- 频带（30-80 Hz）标记显示MTBI中的明显活跃性，可能是由于GABA-erergic受伤 白蛋白阳性（PV+）中间神经元。此外，我们发现任务诱发的MEG（TE-MEG）录音 在工作记忆期间（WM）任务在MTBI的整个大脑中检测到的异常信号与 认知功能较差。该应用程序的主要目标是开发高度敏感的诊断算法 将退伍军人与MTBI与合并症MTBI和PTSD以及健康对照的人区分开 退伍军人。新方法将使用基于人造神经网络的机器学习技术来 整合RS-MEG和TE-MEG成像制造商。我们将研究三组​​退伍军人（每组n = 75）：1） 具有MTBI和持续PC的人（仅MTBI组）； 2）合并MTBI和PTSD的人 持续的PC和PTSD症状； 3）健康对照（HC）。 AIM 1将建立机器 - 基于学习的MTBI的MEG诊断算法，该算法最佳地集成了三种MEG区域成像 标记物（即Delta-Band和Gamma-Band RS-Meg; Wm eveoked Meg）与MTBI区分退伍军人 （仅MTBI和合并症MTBI-PTSD）来自HC退伍军人的精度> 90％。我们预测敏感 MTBI分类的功能将包括RS-MEG Delta-和Gamma-Band活性的异常增加 前额叶和后侧面区域以及仅MTBI和合并症的异常WM活动 相对于HC组。 AIM 2将开发一种集成RS-MEG活动的机器学习MEG算法 以及由负面情绪处理图片（NEPP）任务引起的TE-MEG回应，以区分退伍军人 仅MTBI的合并症MTBI-PTSD的含量> 90％。我们预测合并的mtbi- PTSD组将显示Rs-Meg（β波段）和NEPP TE-MEG的增加，来自杏仁核和 腹侧前额叶皮层（VMPFC），背外侧PFC（DLPFC）和前后的活性减少 在仅MTBI群体上。 AIM 3将检查基于MEG异常的神经生理学的相关性 临床症状，认知障碍和现实世界中仅使用MTBI和合并症的MTBI-PTSD的功能 生活质量。我们预测，特定前额叶和后顶部地区的RS-MEG和WM TE-MEG将会 与PC症状和认知缺陷相关。在合并症MTBI-PTSD组中，PTSD症状将 与杏仁核中异常RS-MEG和NEPP TE-MEG多动症相关，RS-MEG异常 VMPFC，DLPFC和PRECUNEUS中的低连续性。该项目的成功将大大改善 基于神经影像学的技术，可以有效地有助于MTBI的诊断并更好地表征 MTBI和PTSD的神经生物学，神经心理学和神经精神效应之间的关系。

项目成果

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