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: 10381740
• 负责人: Anoopum Satyawan Gupta
• 金额: $ 53.78万
• 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/10381740
• 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; Disease model; 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; base; behavior test; behavioral phenotyping; diagnostic tool; digital; disease classification; drug development; effective therapy; 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 source; 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