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 活体个体的慢性创伤性脑病 (CTE) 和前期阶段，并将其与 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，见于以下人群： 经历过重复性头部撞击 (RHI)，导致认知障碍症状，进而导致痴呆和情绪下降 以及可能导致暴力或自杀的行为障碍。虽然 CTE 在退役 NFL 球员中最为广为人知 和“醉拳”拳击手，在足球、曲棍球、军事战斗、家庭暴力、 老年人和其他人反复跌倒，超过 3 亿人面临潜在风险。目前，虽然临床 创伤性脑病综合征 (TES) 的诊断已发展为提示可能的 CTE，CTE 只能是 尸检时确诊，并且在生前可能被误诊为 AD 或其他痴呆症。没有治疗方法，也没有 意味着检测可以支持介入治疗发展的早期进展阶段。神经影像学 生物标志物及其与液体生物标志物的组合有可能通过以下方式满足 CTE 诊断的需求： 检测大脑连接、体积、功能和化学成分的变化，这些变化构成 CTE 的渐进式、级联式 恶化。在第一阶段 SBIR 工作中，我们将机器学习方法应用于体积 (T1) 和扩散张量 克利夫兰诊所职业拳手大脑健康研究中拳手的 (DTI) 磁共振成像 (MRI) 扫描 （PFBHS）。我们展示了 TES 和可能的 CTE 患者的影响和分化的渐进模式，模式 萎缩的影响将创伤性脑损伤 (TBI) 的影响与 AD 相关认知患者的影响区分开来 损伤以及与 tau 的初步关系。我们的第二阶段目标将这项工作扩展到包括不同人群 RHI、受试者内纵向数据分析以及功能成像和液体生物标志物的纳入，以实现 广泛适用的商用工具，可以 (a) 检测并区分 CTE 与 AD，以及 (b) 检测和分期 TBI 的早期渐进效应。我们将使用独特的综合数据集，包括多模态 MRI、tau PET、临床 来自 (a) PFBHS 组中 719 名拳击手、混合武术家、武术家和对照者的终点和液体生物标志物 其中 165 人至少接受过 3 次影像检查； (b) 240 名前职业和大学橄榄球运动员和控制人员（诊断- 热膨胀系数）； (c) 219 名大学接触运动运动员和控制人员（CARE）； (d) 600 名患有 TBI 和/或 Post 的越南退伍军人 创伤性应激障碍和控制（ADNI-DOD）； (e) 来自我们超过 30,000 个 MRI 参考集的个体 对代表一系列认知正常和认知受损状态的个体进行 PET 扫描 AD 和其他痴呆症。基于第一阶段的成功，我们将开发扩展的 Canonical Variate 和深度 使用成像和液体生物标志物学习分类器，可应用于临床以评估有病史的人 RHI。我们的专家顾问提供的有关临床实用性和可解释性的意见将用于指导报告设计。这些 目标为支持 CTE 鉴别诊断和治疗的商业产品和服务奠定基础 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.