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

多模态机器学习检测和跟踪创伤性脑损伤神经变性及其与阿尔茨海默病的鉴别

• 批准号: 10709652
• 负责人: ANA S LUKIC
• 金额: $ 91.35万
• 依托单位: ADM DIAGNOSTICS, INC.
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10709652
• 关键词: Accounting; Achievement; Address; Affect; Alzheimer' s Disease; Alzheimer' s disease patient; Artificial Intelligence; Atrophic; Automation; Autopsy; Behavior; Behavioral; Biological Markers; Blood; Blood flow; Brain; Brain Diseases; Brain imaging; Cerebral Ventricles; Chemistry; Classification; Clinic; Clinical; Cognitive; Collection; Data; Data Analyses; Data Set; Databases; Dementia; Deposition; Detection; Deterioration; Diagnosis; Diagnostic; Differential Diagnosis; Diffusion; Domestic Violence; Early Diagnosis; Elderly; Evaluation Studies; Exposure to; Feedback; Foundations; Frontotemporal Dementia; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Hockey; Image; Impaired cognition; Individual; Life; Liquid substance; MRI Scans; Machine Learning; Magnetic Resonance Imaging; Manufactured football; Measurement; Measures; Methods; Military Personnel; Modality; Modeling; Moods; Nerve Degeneration; Neurobehavioral Manifestations; Neurodegenerative Disorders; Neurologist; Participant; Pathology; Pattern; Persons; Phase; Population; Populations at Risk; Positron-Emission Tomography; Post-Traumatic Stress Disorders; Probability; Protocols documentation; Recording of previous events; Reporting; Rest; Risk; Sampling; Services; Sleep; Small Business Innovation Research Grant; Soccer; Source; Structure; Suicide; Symptoms; Syndrome; Testing; Thinking; Training; Traumatic Brain Injury; Validation; Veterans; Vietnam; Violence; Visit; Work; arterial spin labeling; artist; biomarker validation; brain health; brain volume; chronic traumatic encephalopathy; clinical diagnosis; clinical effect; college; combat; combat veteran; contact sports; data acquisition; data integrity; deep learning; design; diagnostic tool; diverse data; experience; falls; fighting; head impact; imaging biomarker; insight; learning classifier; machine learning classifier; machine learning method; meetings; multimodal data; multimodality; neuroimaging; neuroimaging marker; nonalzheimer dementia; rate of change; success; support tools; tau Proteins; tau aggregation; tau mutation; therapy development; tool; white matter

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.

抽象的 目标或我们的SBIR第二阶段工作是使用脑成像和其他生物标志物开发诊断工具以识别 慢性创伤性脑病（CTE）和活人的先前阶段，并将其与众不同 阿尔茨海默氏病（AD）和其他痴呆症。 CTE是在个体中发现的一种毁灭性神经退行性障碍 经历了重复的头部影响（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干扰。虽然CTE在退休的NFL球员中宣传最多 和“拳打”拳击手，接触重复的头部影响在足球，曲棍球，军事战斗，家庭暴力，家庭暴力， 重复落在老年人和其他人中，有超过3亿个人有潜在的风险。目前，虽然临床 已经开发出创伤性脑病综合征（TES）的诊断是为了提示有问题的CTE，CTE只能是 在尸检时被诊断出，可以在生活中被诊断为AD或其他痴呆症。没有治疗，也没有 方法可以检测早期的渐进阶段，可以支持介入治疗的发展。神经影像学 生物标志物及其与流体生物标志物的结合有可能通过 检测完成CTE渐进的级联样本的大脑连通性，音量，功能和化学的变化 恶化。在我们的I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）在克利夫兰诊所专业战斗机脑健康研究中对战斗机的磁共振成像（MRI）扫描 （PFBHS）。我们证明了TES和可能CTE的人的效果和差异模式的渐进模式 萎缩区分了创伤性脑损伤（TBI）与AD相关认知患者的影响 与tau的障碍和初步关系。我们的第二阶段目标扩展了这项工作，以包括不同的人群 RHI，受试者内纵向数据分析，并包含功能成像和流体生物标志物以实现A 广泛适用的市售工具，可以（a）检测和区分CTE与AD和（b）检测和阶段 TBI的早期渐进效应。我们将使用多模式MRI，TAU PET，临床的独特全面数据集 （a）719名拳击手，混合武术家，武术家和PFBHS套装中的终点点和流体生物标志物 165人至少有3次访问； （b）240前职业和大学橄榄球运动员和控件（诊断 - CTE）; （C）219个大学与体育运动员和对照组联系（CARE）； （d）600名带有TBI和/或POST的越南退伍军人 创伤应力障碍和控制（ADNI-DOD）； （e）我们参考的个人超过30,000 MRI和 来自代表一系列认知正常和认知受损状态的个人的PET扫描 AD和其他痴呆症。以第一阶段的成功为基础，我们将开发扩展的规范变化和深度 使用成像和流体生物标志物的学习分类器可以在诊所中应用，以评估有历史的人 Rhi。有关临床实用性和我们专家顾问的解释性的输入将用于指导报告设计。这些 目的为支持CTE差异诊断和治疗的商业产品和服务奠定了基础 发展。