Dystonia Coalition- CRP 3

摘要 肌张力障碍是一种慢性的、使人衰弱的疾病。没有治愈方法，目前可用的 治疗仅针对症状，并且仅部分成功。开发新疗法的努力取决于 衡量症状严重程度的能力。传统上，严重性是用人工评分来衡量的。 被称为临床评定量表的工具。然而，由于这些评级量表本质上是主观的， 它们从一个评级到下一个评级表现出可变性-即使是对于同一评级者。这限制了他们的能力， 准确捕获严重性并检测严重性随时间的变化。视频处理软件的进展 提供了用部分自动化的量化手段补充临床评级量表的可能性， 客观的症状。我们的初步测试的计算机化，视频为基础的系统与肌张力障碍患者 视频表明他们可以量化最常见类型的肌张力障碍的几个临床重要特征。 我们的长期目标是最大限度地利用这些方法来帮助监测进展，评估 治疗反应，并优化肌张力障碍的治疗。 在这个项目中，我们将开发和验证这种方法来客观地测量运动症状 肌张力障碍的三种常见形式：颈部肌张力障碍（CD）、眼睑痉挛（BSP）和喉肌张力障碍 （LD）。对于CD和BSP，该系统使用称为计算机表情识别的高级软件 CERT），以从患者面部的视频记录中测量各种异常运动。cert 已广泛用于神经正常人群，并且对高度可变的视频具有鲁棒性 记录条件。虽然这种方法以前从未应用于LD，但我们将开发一个 类似的系统，以测量在检查期间在视频上捕获的声带中的异常活动。 喉癌对于三种形式的肌张力障碍中的每一种，我们的目标是：a）测试系统捕获特定肌张力障碍的能力 肌张力障碍的特征，B）评价这些特征的组合如何与总体临床评估相关 c）将系统的严重性评估与患者报告的严重性评估进行比较， 寿命，以及d）确定系统是否可以检测与肉毒杆菌毒素相关的严重性的变化 注射，最常见的治疗这些形式的局灶性肌张力障碍。 完成后，我们将开发和验证新的方法来衡量最严重的 普通肌张力障碍该方法将有助于肌张力障碍的基础和临床研究。基本 例如，遗传学、蛋白质组学和脑成像的研究依赖于严重性评估， 将患者分为不同的组和/或确定患者的神经生物学方面如何与症状相关 严重性。肌张力障碍的自然进展及其对新疗法的反应的临床研究也 依赖于严重性评估。因此，该项目将有助于扩大基础和临床研究的影响 并有助于加速肌张力障碍新疗法的开发。

Abstract The dystonias are chronic, debilitating disorders. There are no cures, and the currently available treatments target only the symptoms and are only partly successful. Efforts to develop new treatments depend on the ability to measure the severity of the symptoms. Traditionally, severity is measured with human-scored instruments known as clinical rating scales. However, because these rating scales are inherently subjective, they exhibit variability from one rating to the next – even for the same rater. This limits their ability to accurately capture severity and detect changes in severity over time. Advances in video processing software offer the possibility to supplement clinical rating scales with a partially automated means of quantifying symptoms objectively. Our preliminary tests of computerized, video-based systems with dystonia patient videos suggest they can quantify several clinically important features of the most common types of dystonia. Our long-term goal is to maximize how these approaches can aid in monitoring progression, assessing treatment response, and optimizing treatment for dystonia. In this project, we will develop and validate this approach to objectively measure the motor symptoms of three common forms of dystonia: cervical dystonia (CD), blepharospasm (BSP), and laryngeal dystonia (LD). For CD and BSP, the system uses advanced software known as the Computer Expression Recognition Toolbox (CERT) to measure various abnormal movements from video recordings of the patient’s face. CERT has been used extensively in neurologically normal populations and it is robust to highly variable video recording conditions. Although this type of approach has never before been applied to LD, we will develop a similar system to measure abnormal activity in the vocal cords captured on video during examination of the larynx. For each of the three forms of dystonia, our aims are to: a) test how well the system captures specific features of the dystonia, b) evaluate how combinations of those features relate to global clinical assessments of severity, c) compare the system’s assessment of severity with patient-reported assessments of quality of life, and d) determine whether the system can detect changes in severity associated with botulinum toxin injections, the most common treatment for these forms of focal dystonia. Upon completion, we will have developed and validated new methods to measure severity of the most common dystonias. The methods will be useful for both basic and clinical research in dystonia. Basic research in genetics, proteomics, and brain imaging, for example, relies on severity assessments for assigning patients to different groups and/or determining how aspects of the patient’s neurobiology relate to symptom severity. Clinical research on the natural progression of dystonia and its response to new treatments also relies on severity assessments. Thus this project will help amplify the impact of both basic and clinical research and help accelerate the development of new treatments for dystonia.