Automated Assessment of White Matter Integrity in TBI Using Machine Learning

使用机器学习自动评估 TBI 中白质完整性

• 批准号: 9281656
• 负责人: Brian Allen Taylor
• 金额: --
• 依托单位: MICHAEL E DEBAKEY VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9281656
• 关键词: Address; Affect; Afghanistan; Algorithms; Alzheimer' s Disease; Anisotropy; Behavioral; Biological Markers; Blast Cell; Blast Injuries; Brain; Brain Injuries; Brain region; Chronic; Classification; Cognitive; Computer software; Conflict (Psychology); Data; Detection; Development; Diagnosis; Diffuse; Diffusion Magnetic Resonance Imaging; Disease; Extracellular Space; Fiber; Freedom; Goals; Head and neck structure; Image; Individual; Injury; Intervention; Iraq; Joints; Link; Machine Learning; Magnetic Resonance Imaging; Measures; Military Personnel; Mission; Morbidity - disease rate; Myelin; Nature; Nerve; Nervous System Trauma; Neurons; Neuropsychology; Outcome; Outcome Measure; Output; Participant; Pathologic Processes; Pathology; Pathway interactions; Patients; Pattern; Performance; Population; Principal Component Analysis; Probability; Procedures; Process; Questionnaires; Radial; Recording of previous events; Regression Analysis; Reporting; Retrospective Studies; Sampling; Scanning; Sensitivity and Specificity; Severities; Site; Skeleton; Swelling; System; Training; Training Support; Trauma; Traumatic Brain Injury; Validation; Veterans; War; Weight; Work; accurate diagnosis; base; cognitive testing; cohort; combat; cost; daily functioning; depressive symptoms; design; disability; experience; functional outcomes; health administration; imaging detection; imaging study; improved; indexing; mild traumatic brain injury; mind control; neuroimaging; neuropathology; operation; outcome forecast; post-traumatic stress; public health relevance; rehabilitation service; rehabilitation strategy; service member; statistics; stress symptom; tool; validation studies; white matter; white matter change; white matter injury

DESCRIPTION (provided by applicant): Mild traumatic brain injury (mTBI) is the signature injury of the wars in Afghanistan and Iraq. Recent statistics indicate that 60% of blast injuries result in TBI and approximately 20% of returning OEF/OIF Veterans have sustained a TBI, with the majority classified as mTBI. Although many sequelae of mTBI resolve within a few months, a substantial portion of patients experience difficulties for years. Diagnosis of mTBI in the chronic stage is a frequent referral fo the Veterans Health Administration. Conventional MRI and CT are typically normal months after civilian and military mTBI making it difficult to accurately diagnose and to determine rehabilitation strategies. Diffusion tensor imaging (DTI) can be used to characterize and quantify WM pathways in the living brain. Specific to brain injury, pathological processes causing loss or disorganization of fibers associated with breakdown of myelin and downstream nerve terminals, neuronal swelling or shrinkage, and increased or decreased extracellular space, could affect the quantitative scalar metrics like mean diffusivity (MD), fractional anisotropy (FA), radial diffusivity (RD), and/or axial diffusivity (AD). Recent studies have reported that FA was reduced in chronic civilian mTBI. Evidence from military cohorts also suggests important changes in DTI metrics across several brain regions. Machine learning (ML) algorithms are particularly sensitive to distributed changes caused by disease as observed in several structural and functional studies. This particular class of algorithms is specifically designed to identify patterns in temporal or spatial data to distinguish between groups. While several ML algorithms exists, one particular multivariate algorithm known as a Support Vector Machine (SVM) has been successfully applied to Alzheimer's Disease studies as well as a recent study in a group of TBI patients through the use of DTI data. In addition, the incorporation of principal component analysis (PCA) to SVM showed robust automated detection of WM degradation in Alzheimer's Disease over several sites and MR scanner platforms. This ability to evaluate this across platforms is particularly attractive to multi-center imaging studies that are performed in the VHA system. At present, the automated detection of biomarkers is scarce in the diagnosis and prognosis of mTBI in our Veteran population. This work will tailor an imaging and detection strategy that can possibly be used to not only identify Veterans with mTBI more objectively but also predict cognitive outcome to help facilitate appropriate rehabilitation strategies. Aim 1 will consist of a retrospective study of 70 subjects and controls to train the SVM algorithm to differentiate between mTBI pathology and uninjured military controls that were also deployed in the OIF/OEF/OND conflicts. DTI skeletons will be processed using Tract-Based Spatial Statistics (TBSS) software and will be used as inputs into the SVM algorithm. Using this data, parameters such as the cost function will be determined to optimize the algorithm. We will measure the accuracy, sensitivity and specificity of the algorithm by using a cross-validation approach. Finally for this first aim, we will use a sensitivity analysis techniqueto identify regions the algorithm weights more in determining if an mTBI has taken place. This will identify pathways that are vulnerable to injury. In Aim 2, we will use the SVM classifier on DTI scans to output possibility indices of mTBI. Regression analysis will be used to relate these indices to outcome measures. In conclusion, this work will provide a robust tool to not only better diagnose and characterize mTBI but also stratify more personalized rehabilitation strategies through the improved characterization of mTBI.

描述（由申请人提供）： 轻度创伤性脑损伤（MTBI）是阿富汗和伊拉克战争的标志性伤害。最近的统计数据表明，60％的爆炸损伤导致TBI和约20％的返回的OEF/OIF退伍军人持续了TBI，其中大多数被归类为MTBI。尽管MTBI的许多后遗症在几个月内就解决了，但大部分患者多年来遇到困难。在慢性阶段，MTBI的诊断是退伍军人卫生管理局的经常转诊。常规的MRI和CT通常是在平民和军事MTBI之后的正常月份，因此难以准确诊断并确定康复策略。 扩散张量成像（DTI）可用于表征和量化活大脑中的WM途径。特定于脑损伤，导致与髓磷脂和下游神经终末分解相关的纤维损失或混乱的病理过程，神经元肿胀或收缩或收缩，细胞外空间增加或减少，可能会影响定量标量指标，例如平均分散率（MD），分数各向异性（MD） （FA），径向扩散（RD）和/或轴向扩散率（AD）。最近的研究报告说，在慢性平民MTBI中，FA减少了。军事队列的证据还表明，多个大脑区域的DTI指标的重要变化。 机器学习（ML）算法对在几个结构和功能研究中观察到的疾病引起的分布变化特别敏感。这种特定类别的算法是专门设计的，用于识别时间或空间数据中的模式以区分组。尽管存在几种ML算法，但一种被称为支持载体机（SVM）的特定多元算法已成功地应用于阿尔茨海默氏病研究中，以及通过使用DTI数据在一组TBI患者中进行的一项研究。此外，融合 对SVM的主要成分分析（PCA）在多个地点和MR扫描仪平台上显示出强大的自动检测Alzheimer病中WM降解。这种跨平台评估这一点的能力对VHA系统中执行的多中心成像研究特别有吸引力。目前，在我们的退伍军人人口中，MTBI的诊断和预后中，生物标志物的自动检测很少。这项工作将量身定制成像和检测策略，该策略不仅可以用来更客观地识别MTBI的退伍军人，而且还可以预测认知结果以帮助促进适当的康复策略。 AIM 1将包括对70名受试者和控制的回顾性研究，以训练SVM算法以区分MTBI病理学和也已部署在OIF/OEF/OND冲突中的未受伤的军事控制。 DTI骨架将使用基于道的空间统计（TBSS）软件处理，并将用作SVM算法的输入。使用此数据，将确定诸如成本函数之类的参数以优化算法。我们将使用交叉验证方法来衡量算法的准确性，灵敏度和特异性。最后，对于第一个目标，我们将使用灵敏度分析技术确定算法权重在确定是否发生MTBI时。这将确定容易受伤的途径。在AIM 2中，我们将在DTI扫描上使用SVM分类器来输出MTBI的可能性指数。回归分析将用于将这些指数与结果度量相关联。总之，这项工作将提供一个强大的工具，不仅可以更好地诊断和表征MTBI，而且还通过改进的MTBI表征来对更个性化的康复策略进行分层。