Multi-modal machine learning detection and tracking of traumatic brain injury neurodegeneration and its differentiation from Alzheimer's disease

摘要 我们SBIR第二阶段工作的目标是开发一种诊断工具，使用脑成像和其他生物标志物来识别 慢性创伤性脑病（CTE）和以前的阶段在生活的个人，并区分这些与 阿尔茨海默病（AD）和其他痴呆症。CTE是一种毁灭性的神经退行性疾病， 经历过重复性头部撞击（RHI），导致认知障碍症状，导致痴呆症和情绪 以及可能导致暴力或自杀的行为障碍。虽然CTE在退役的NFL球员中最为人所知， 和“醉酒”拳击手，暴露于重复的头部撞击发生在足球，曲棍球，军事战斗，家庭暴力， 老年人和其他人反复福尔斯跌倒，有超过3亿人处于潜在风险中。目前，虽然临床 创伤性脑病综合征（TES）的诊断已经发展到提示可能的CTE，CTE只能是 在尸检时被诊断出来，在生活中可能被误诊为AD或其他痴呆症。没有治疗方法， 检测早期进展阶段的手段，可以支持介入治疗的发展。神经影像 生物标志物及其与流体生物标志物的组合具有通过以下方式解决CTE诊断需求的潜力： 检测大脑连接、容量、功能和化学物质的变化，这些变化包括CTE的进行性、级联样 恶化在我们的第一阶段SBIR工作中，我们将机器学习方法应用于体积（T1）和扩散张量 (DTI)克利夫兰诊所职业拳击手大脑健康研究中的拳击手磁共振成像（MRI）扫描 （PFBHS）。我们证明了一个渐进的模式的影响和分化的人与TES和可能的CTE，模式 萎缩的区别创伤性脑损伤（TBI）的影响与AD相关认知功能障碍患者的影响 损伤和与tau的初步关系。我们的第二阶段目标是扩大这项工作，包括不同的人群， RHI、受试者内纵向数据分析以及纳入功能成像和液体生物标志物，以实现 可广泛应用市售工具，其可以（a）检测和区分CTE与AD，以及（B）检测和分级 TBI的早期进展效应。我们将使用多模态MRI、tau PET、临床MRI和临床MRI的独特综合数据集。 （a）PFBHS组中的719名拳击手、混合武术家、武术家和对照组， 其中165人至少有3次影像学检查;（B）240名前职业和大学足球运动员和对照（诊断- CTE）;（c）219名大学接触性体育运动员和对照组（CARE）;（d）600名患有TBI和/或Post的越南退伍军人 创伤性应激障碍和对照（ADNI-DOD）;以及（e）来自我们的超过30，000个MRI和MRI的参考集的个体。 来自个体的PET扫描代表与以下相关的认知正常和认知受损状态的谱 AD和其他痴呆症。在我们第一阶段成功的基础上，我们将开发扩展的Canonical Variate和深度 使用成像和流体生物标志物学习分类器，其可以应用于临床以评估具有以下病史的人： RHI我们的专家顾问提供的关于临床实用性和可解释性的输入将用于指导报告设计。这些 旨在为支持CTE鉴别诊断和治疗的商业产品和服务提供基础 发展

ABSTRACT The goal or our SBIR Phase II work is to develop a diagnostic tool using brain imaging and other biomarkers to identify Chronic Traumatic Encephalopathy (CTE) and preceding stages in living individuals, and to differentiate these from Alzheimer’s disease (AD) and other dementias. CTE is a devastating neurodegenerative disorder found in individuals who have experienced repetitive head impact (RHI), causing symptoms of cognitive impairment that lead to dementia, and mood and behavioral disturbances that may lead to violence or suicide. While CTE has been most publicized in retired NFL players and “punch drunk” boxers, exposure to repetitive head impact occurs in soccer, hockey, military combat, domestic violence, repeated falls in elderly, and other persons, with over 300,000,000 individuals at potential risk. Currently, although a clinical diagnosis of Traumatic Encephalopathy Syndrome (TES) has been developed to suggest probable CTE, CTE can only be diagnosed at autopsy and can be misdiagnosed during life as AD or other dementias. There are no treatments and no means to detect earlier, progressive stages that could support the development of interventional treatments. Neuroimaging biomarkers and their combination with fluid biomarkers have the potential to address the need for a CTE diagnostic by detecting changes in brain connectivity, volume, function, and chemistries that comprise CTE’s progressive, cascade-like deterioration. In our Phase I SBIR work, we applied machine learning methods to the volumetric (T1) and diffusion tensor (DTI) magnetic resonance imaging (MRI) scans of fighters in the Cleveland Clinic Professional Fighters Brain Health Study (PFBHS). We demonstrated a progressive pattern of effects and differentiation of persons with TES and likely CTE, patterns of atrophy differentiating the effects of traumatic brain injury (TBI) from those in patients with AD related cognitive impairment, and preliminary relationships to tau. Our Phase II Aims expand this work to include different populations with RHI, within-subject longitudinal data analyses, and inclusion of functional imaging and fluid biomarkers toward achieving a broadly applicable commercially available tool that can (a) detect and differentiate CTE from AD and (b) detect and stage earlier progressive effects of TBI. We will use a uniquely comprehensive data set of multi-modality MRI, tau PET, clinical endpoints, and fluid biomarkers from (a) 719 boxers, mixed martial artists, martial artists, and controls in the PFBHS set, of whom 165 have at least 3 imaging visits; (b) 240 former professional and college football players and controls (DIAGNOSE- CTE); (c) 219 collegiate contact sports athletes and controls (CARE); (d) 600 Vietnam veterans with TBI and/or Post Traumatic Stress Disorder and controls (ADNI-DOD); and (e) individuals from our reference set of over 30,000 MRI and PET scans from individuals representing a spectrum of cognitively normal and cognitively impaired states associated with AD and other dementias. Building on our success from Phase I, we will develop expanded Canonical Variate and deep learning classifiers using imaging and fluid biomarkers that can be applied in the clinic to evaluate persons with a history of RHI. Input regarding clinical utility and interpretability from our expert Advisors will be used to guide report design. These Aims provide the foundation for commercial products and services supporting CTE differential diagnosis and treatment development.