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: 8732156
• 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 2016-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8732156
• 关键词: Accounting; 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; Life; Link; Machine Learning; Magnetic Resonance Imaging; Measures; Military Personnel; Mission; Morbidity - disease rate; Myelin; Nature; Nerve; Nervous System Trauma; Neurons; 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; Services; Severities; Site; Skeleton; Staging; Swelling; System; Training; Training Support; Trauma; Traumatic Brain Injury; Validation; Veterans; War; Weight; Work; base; cognitive testing; cohort; combat; cost; daily functioning; depressive symptoms; design; disability; experience; functional outcomes; health administration; improved; indexing; member; mild traumatic brain injury; neuroimaging; neuropathology; neuropsychological; operation; outcome forecast; post-traumatic stress; public health relevance; rehabilitation service; rehabilitation strategy; statistics; tool; validation studies; white matter; white matter change; white matter injury

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 for 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 who 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 technique to 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.

轻度创伤性脑损伤(MTBI)是阿富汗和伊拉克战争的标志性损伤。近期 统计数据显示，60%的爆炸伤导致脑外伤，约20%的退伍军人返回OEF/OIF 已经持续了脑损伤，其中大多数被归类为脑损伤。尽管mTBI的许多后遗症在一年内解决 几个月后，相当一部分患者会经历多年的困难。慢性重型颅脑损伤的诊断 Stage是退伍军人健康管理局经常转介的。常规的MRI和CT通常是 民用和军用mTBI后几个月内正常，难以准确诊断和确定 康复策略。 弥散张量成像(DTI)可以用来表征和量化活体中的WM通路 大脑。特定于脑损伤，导致纤维丢失或组织紊乱的病理过程，与 髓鞘及下游神经末梢破坏，神经元肿胀或收缩，或 细胞外空间减少，可能会影响定量标量指标，如平均扩散率(MD)、分数 各向异性(FA)、径向扩散系数(RD)和/或轴向扩散系数(AD)。最近的研究报告说，FA是 慢性平民创伤性脑损伤减少。来自军事队列的证据也表明DTI发生了重要变化 几个大脑区域的指标。 机器学习(ML)算法对疾病引起的分布式变化特别敏感，例如 在几个结构和功能研究中观察到的。这类特定的算法特别是 旨在识别时间或空间数据中的模式，以区分不同的组。而几个ML 算法已经存在，一种被称为支持向量机(SVM)的特定多变量算法已经被 成功地应用于阿尔茨海默病的研究，以及最近在一组脑损伤患者中的研究 通过使用DTI数据。此外，将主成分分析(PCA)引入到支持向量机中 在多个部位和磁共振显示了阿尔茨海默病患者WM降解的强健自动检测 扫描仪平台。这种跨平台评估的能力对多中心成像特别有吸引力 在VHA系统中执行的研究。目前，生物标志物的自动化检测在我国还很少见。 我国退伍军人多发性脑损伤的诊断和预后这项工作将量身定做成像和检测 一种策略，不仅可以更客观地识别退伍军人的mTBI，而且可以预测 认知结果有助于促进适当的康复策略。 目标1将包括对70名受试者和对照组的回顾性研究，以训练支持向量机算法 区分脑外伤病理和未受伤的军事控制组，后者也部署在 OIF/OEF/OND冲突。DTI骨架将使用基于区域的空间统计(TBSS)软件进行处理 并将被用作支持向量机算法的输入。使用该数据，成本函数等参数将 下决心对算法进行优化。我们将测量精确度、敏感性和特异性 算法，并使用交叉验证方法。最后，对于第一个目标，我们将使用敏感度分析 识别区域的技术算法在确定是否发生mTBI时权重更大。这将是 找出易受伤害的路径。在目标2中，我们将在DTI扫描上使用支持向量机分类器来输出 MTBI的可能性指数。将使用回归分析将这些指数与结果衡量标准联系起来。在……里面 结论：这项工作不仅为更好地诊断和表征脊髓损伤提供了一个可靠的工具，而且为更好地诊断和鉴定脊髓损伤提供了一个可靠的工具 通过改进mTBI的特征，分层制定更个性化的康复策略。