A computational approach for quantifying motor behaviors in spinocerebellar ataxias to improve early detection of motor signs and precisely estimate disease severity and disease change

一种量化脊髓小脑共济失调运动行为的计算方法，以改善运动体征的早期检测并精确估计疾病严重程度和疾病变化

• 批准号: 10609864
• 负责人: Anoopum Satyawan Gupta
• 金额: $ 53.49万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-15 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10609864
• 关键词: Affect; Age of Onset; Algorithms; Assessment tool; Ataxia; Behavior; Behavior assessment; Behavioral; Biological Markers; Cellular Phone; Characteristics; Classification; Clinic; Clinic Visits; Clinical; Clinical Trials; Clinical assessments; Collection; Computer Vision Systems; Computers; Computing Methodologies; Data; Data Collection; Data Set; Detection; Development; Disease; Disease Progression; Early Diagnosis; Early identification; Education; Eye Movements; Face; Generations; Genes; Geographic Locations; Goals; Grain; Home; Human; Individual; Knowledge; Label; Learning Disorders; Length; Machine Learning; Measurement; Measures; Methodology; Methods; Modality; Modeling; Motor; Movement; Mus; Neurodegenerative Disorders; Neurology; Neurosciences; Onset of illness; Outcome Measure; Parkinson Disease; Parkinsonian Disorders; Patient Outcomes Assessments; Patient Recruitments; Patients; Pattern; Pharmaceutical Preparations; Sample Size; Severities; Severity of illness; Signal Transduction; Socioeconomic Status; Speech; Spinocerebellar Ataxias; System; Technology; Testing; Time; Training; Trinucleotide Repeats; Vision; arm; arm function; arm movement; behavior test; behavioral phenotyping; diagnostic tool; digital; disease classification; drug development; effective therapy; efficacy evaluation; experience; handheld mobile device; illness length; improved; insight; machine learning model; member; microphone; motor behavior; motor control; motor deficit; multimodality; nervous system disorder; novel; novel strategies; novel therapeutics; oculomotor; open data; open source; patient retention; personalized predictions; point of care; primary outcome; prognostic; prognostication; response; sensor; signal processing; tool; treatment response

ABSTRACT The spinocerebellar ataxias (SCA) are debilitating neurodegenerative diseases that impact a range of human behaviors including arm function, speech, and vision. Tools that can quantify motor deficits in a granular and objective manner are needed to support early recognition of clinical disease onset, more sensitively determine efficacy of a therapy, and make personalized predictions about disease progression. Such tools are needed for upcoming disease modifying clinical trials in SCAs, in order to reduce sample size and trial duration and better understand how a given therapy modifies human behaviors. Powered off of the currently available primary outcome measures for these rare ataxias, clinical trials are likely to face patient recruitment and retention challenges, especially with multiple co-occurring clinical trials. These challenges may impede or slow our ability to successfully discover therapies for our patients. We have recently made substantial progress in capturing multimodal behavioral signals from speech, eye movement, and arm motor function using everyday technologies: a microphone, iPhone camera, and computer mouse. Our initial data indicate that these scalable technologies have strong potential to extend current clinical assessments in ataxia and that our novel machine learning approach for generating disease severity estimates performs better than the traditional regression model approach. Our algorithms are able to quantitatively identify signs of ataxia and parkinsonism in SCA individuals' speech and arm movement, even when absent on clinical assessment. Furthermore, our novel severity estimation algorithm enabled measurement of disease progression more sensitively than clinical scales. We propose to substantially expand longitudinal data collection and further develop our novel analytic approaches to train more powerful models for characterizing and quantifying human motor behavior. The technologies developed have the potential to facilitate clinical trials aimed at bringing disease modifying therapies to individuals with SCA. While the focus of this project is on SCA, the novel methodological approaches and data generated are applicable to other neurodegenerative diseases affecting movement and speech. Furthermore, this project will bring new insight into how motor abnormalities initially arise and progress. The overall goal of this project is to develop widely available systems for improving early detection of clinical disease onset, severity assessment, and prognostication of spinocerebellar ataxias while simultaneously learning how these disorders impact fine-grained motor behavior.

摘要 脊髓小脑共济失调（SCA）是一种使人衰弱的神经退行性疾病， 人类行为，包括手臂功能、言语和视觉。可以量化运动缺陷的工具， 需要颗粒和客观的方式来支持临床疾病发作的早期识别， 灵敏地确定治疗的疗效，并对疾病进展进行个性化预测。 为了减少样本量，即将进行的SCA疾病改变临床试验需要这些工具 以及试验持续时间，更好地了解特定疗法如何改变人类行为。关闭电源 目前，这些罕见共济失调的主要结局指标，临床试验可能面临患者 招募和保留的挑战，特别是多个同时发生的临床试验。这些挑战可能 阻碍或减缓我们为患者成功发现治疗方法的能力。 我们最近在从语音中捕获多模态行为信号方面取得了实质性进展， 眼睛运动和手臂运动功能，使用日常技术：麦克风，iPhone摄像头， 电脑鼠标我们的初步数据表明，这些可扩展的技术具有很强的扩展潜力， 目前共济失调的临床评估以及我们用于产生疾病的新型机器学习方法 严重性估计比传统的回归模型方法表现得更好。我们的算法能够 定量识别SCA个体的言语和手臂运动中的共济失调和帕金森综合征的迹象， 在临床评估时缺席。此外，我们的新的严重性估计算法， 疾病进展的测量比临床量表更敏感。我们建议大幅扩大 纵向数据收集，并进一步发展我们的新的分析方法，以训练更强大的模型， 表征和量化人类运动行为。开发的技术有可能 促进临床试验，旨在为SCA患者提供疾病改善疗法。虽然重点是 该项目基于SCA，新的方法论方法和生成的数据也适用于其他项目 影响运动和语言的神经退行性疾病。此外，该项目将带来新的见解 运动异常是如何产生和发展的 该项目的总体目标是开发可广泛使用的系统，以改善早期发现 脊髓小脑共济失调的临床发病、严重程度评估和诊断， 同时了解这些疾病如何影响精细的运动行为。