Automated Assessment of White Matter Integrity in TBI Using Machine Learning

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损伤。最近的统计数据表明，60%的爆炸伤导致了脑外伤，大约20%的退伍军人遭受了脑外伤，其中大多数被归类为mTBI。虽然mTBI的许多后遗症在几个月内就会消失，但相当一部分患者会经历多年的困难。慢性阶段mTBI的诊断是退伍军人健康管理局经常转诊的。常规MRI和CT在民用和军用mTBI后几个月通常是正常的，这使得准确诊断和确定康复策略变得困难。弥散张量成像(DTI)可以用来表征和量化活体大脑中的WM通路。对于脑损伤而言，导致髓鞘及其下游神经末梢破裂、神经元肿胀或收缩以及细胞外间隙增加或减少相关的纤维丢失或紊乱的病理过程，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)、径向扩散率(RD)和/或轴向扩散率(AD)。最近的研究报道，在慢性平民mTBI中，FA减少。来自军事队列的证据还表明，大脑几个区域的DTI指标发生了重要变化。机器学习(ML)算法对疾病引起的分布式变化特别敏感，正如在几个结构和功能研究中所观察到的那样。这类特定的算法专门用于识别时间或空间数据中的模式，以区分不同的组。虽然存在几种最大似然算法，但一种被称为支持向量机(SVM)的特定多变量算法已经成功地应用于阿尔茨海默病的研究，以及最近通过使用DTI数据对一组脑外伤患者进行的研究。此外，公司成立后 从主成分分析(PCA)到支持向量机的分析显示，在多个站点和磁共振扫描仪平台上，对阿尔茨海默病患者的WM退化进行了稳健的自动检测。这种跨平台评估的能力对于在VHA系统中进行的多中心成像研究特别有吸引力。目前，在我国退伍军人群体中，生物标志物的自动检测在mTBI的诊断和预后方面缺乏。这项工作将量身定做一种成像和检测策略，不仅可以更客观地识别患有mTBI的退伍军人，还可以预测认知结果，以帮助促进适当的康复策略。目标1将包括对70名受试者和对照的回顾性研究，以训练支持向量机算法区分mTBI病理和也部署在OIF/OEF/OND冲突中的未受伤的军事对照。DTI骨架将使用基于区域的空间统计(TBSS)软件进行处理，并将用作支持向量机算法的输入。使用这些数据，将确定诸如成本函数之类的参数来优化算法。我们将使用交叉验证的方法来衡量算法的准确性、敏感性和特异度。最后，对于第一个目标，我们将使用灵敏度分析技术来识别区域，该算法在确定是否发生mTBI时加权更多。这将确定易受伤害的路径。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。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，而且可以通过改进mTBI的表征来分层更个性化的康复策略。

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.