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: 9888520
• 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 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9888520
• 关键词: Acute; 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; Hyperactive behavior; Imaging technology; Impaired cognition; Individual; Injury; Interneurons; Machine Learning; Magnetic Resonance Imaging; Magnetoencephalography; Mental disorders; Network-based; 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; TBI Patients; Techniques; Validation; Veterans; X-Ray Computed Tomography; artificial neural network; base; clinical application; clinical care; cognitive function; comorbidity; diagnostic accuracy; 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)是导致持续的身体、认知、情绪和 退伍军人和普通公众的行为缺陷。然而，潜在的病理生理学 而康复机制，特别是与mTBI认知功能相关的机制，并不完全 明白了。MTBI后PTSD风险增加的神经机制甚至不清楚。 常规MRI和CT图像一般为阴性，即使是持续性脑震荡患者也是如此 症状(PCS)和/或PTSD症状。基于扩散的磁共振成像技术已经被开发来识别 脑白质束异常，这是由于弥漫性轴索损伤(DAI)在mTBI中的主要作用。然而，即使 对于可靠的临床应用，复杂的基于扩散的磁共振成像技术不够敏感。 最近的动物研究表明，灰质也容易受到DAI的影响，这会导致异常 来自受伤区域的电磁信号。在这方面，越来越多的人支持 静息状态脑磁图(RS-MEG)源成像标记物检测神经元异常 单位：mTBI。我们证明了RS-MEG增量波(1-4赫兹)标记物在区分 MTBI患者的持续性PCS来自神经功能完整的个体。我们还发现RS-Meg伽马- 频段(30-80赫兹)标记物显示mTBI明显的过度活动，可能与GABA能神经元的损伤有关 小白蛋白阳性(PV+)中间神经元。此外，我们发现任务诱发的脑磁图(TE-MEG)记录 在工作记忆(WM)任务中，在mTBI中检测到整个大脑的异常信号与 认知功能较差。这个应用程序的一个主要目标是开发高度敏感的诊断算法 退伍军人合并创伤后应激障碍与合并创伤后应激障碍及健康对照的鉴别 退伍军人。新的方法将使用基于人工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75人)：1) 患有mTBI和持续性PCS的个体(仅mTBI组)；2)mTBI和创伤后应激障碍共病的个体 有持续性PCS和PTSD症状的人；3)健康对照组(HC)。目标一号将建立一台机器- 基于学习的优化集成三种脑磁图的脑磁图诊断算法 用于区分退伍军人和脑外伤的标志物(即增量频段和伽马频段RS-MEG；WM诱发的脑磁图) (仅MTBI和合并MTBI-PTSD)来自HC退伍军人，准确率为90%。我们预测，敏感的 MTBI分类的特征将包括 单纯mTBI组和合并mTBI组的前额叶和后顶叶区域以及异常的WM诱发活动 相对于HC组。Aim 2将开发一种机器学习的MEG算法，该算法集成了RS-MEG活动 以及由消极情绪处理图片(NEPP)任务诱发的TE-MEG反应以区分退伍军人 MTBI-仅来自合并mTBI-PTSD的患者，准确率为90%。我们预测并存的结核分枝杆菌- PTSD组杏仁核和杏仁核的Rs-MEG(β带)和NEPP TE-MEG活性增加 前额叶腹内侧皮质(VmPFC)、背外侧前额叶(DlPFC)和楔前叶的活动减少 在仅限mTBI的组上。目标3将检查基于脑磁图的异常神经生理学的相关性 伴有临床症状、认知障碍和真实世界的单纯mTBI-PTSD患者的特征 生活质量。我们预测在特定的前额叶和后顶叶区域的RS-MEG和WM-TE-MEG将 与PCS症状和认知缺陷相关。在合并创伤后应激障碍组中，创伤后应激障碍症状将 脑磁图异常与杏仁核异常脑磁图及脑电活动异常的关系 VmPFC、dlPFC和楔前叶活动不足。这个项目的成功将大大提高 基于神经成像的技术，可以有效地辅助诊断mTBI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病学效应之间的关系。