Computational Speech Analysis in Alzheimer's Disease and Other Neurocognitive Disorders

摘要： 神经认知障碍（NCD）的早期和准确诊断对于计划，治疗和治疗至关重要。 研究转诊，但需要时间和专业知识往往无法提供给初级保健提供者。言论和 在几种非传染性疾病的病程早期，语言往往受损。先前的研究表明， 计算机语音分析（CSA）的诊断潜力，健康对照组和 轻度认知障碍（MCI）和阿尔茨海默病等疾病。但有几 必须采取额外的步骤，使CSA成为诊断上可行的筛查工具。该提案包括 职业发展计划，为申请人提供以下方面的培训、指导和经验 将CSA技术应用于临床实践的领域：1）计算语言学和计算机语言学，2） 疾病的纵向标记，和3）设计新的传播技术。作为培训的一部分， 还将扩大学术和专业技能，包括研究道德。唯一有资格 已经选定了指导和咨询小组，以确保拟议的培训取得成功， research. 这项研究是对两项不同研究的前瞻性、纵向、观察性、队列研究。 组第一组是高度选择和良好表征的健康对照研究队列， 阿尔茨海默病和MCI受试者（A组）。在A组中， 机器学习算法将得到改进，以区分阿尔茨海默病和MCI与健康对照 使用CSA。多元回归和基于体素的形态测量将被用来更好地了解什么可能 在A组中，CSA测量也存在组间差异。该算法的临床应用将在 在不同NCD患者（B组）的临床队列中进行评估，以减少谱偏倚 可能存在于先前的研究中。作为两组的子目标，可能进一步改进算法 还将检查纵向CSA测量的结果。总体目标是培养直觉， 通过将其与已建立的神经精神病学相关联， 和成像标记物，确定它们在临床人群中的功效，并确定它们如何改变 时间因此，这项研究将验证特定的言语特征作为有用的诊断标记， 神经认知疾病，并解释为什么这些标志物在患者群体之间存在差异，这两个都是主要的 采取步骤，设计新的和易于实施的工具，以筛查阿尔茨海默氏症等非传染性疾病 疾病

Abstract: Early and accurate diagnosis of neurocognitive disorders (NCDs) is critical for planning, treatment, and research referral, but demands time and expertise often unavailable to primary care providers. Speech and language are often impaired early in the disease course of several NCDs. Previous research has demonstrated the diagnostic potential of computer speech analysis (CSA), with differences between healthy controls and disorders such as mild cognitive impairment (MCI) and Alzheimer's disease. However, there are several additional steps that must be taken to make CSA a diagnostically viable screening tool. This proposal includes a career development plan providing the applicant with training, mentorship, and experience in the following areas in order to bring CSA techniques into clinical practice: 1) computational linguistics and paralinguistics, 2) longitudinal markers of disease, and 3) design of novel technology for dissemination. As part of this training, academic and professional skills, including ethics in research, will also be expanded. Uniquely qualified mentorship and advisory teams have been selected to ensure the success of the proposed training and research. The proposed study is a prospective, longitudinal, observational, cohort investigation of two distinct research groups. The first group is a highly selected and well-characterized research cohort of healthy control, Alzheimer's disease, and MCI subjects (Group A). In Group A, the performance and reproducibility of a machine learning algorithm will be improved to distinguish Alzheimer's disease and MCI from healthy controls using CSA. Multiple regression and voxel-based morphometry will be used to better understand what may drive group differences in CSA measures in Group A as well. Clinical applications of this algorithm will then be assessed in a clinic-based cohort of patients with different NCDs (Group B) in order reduce spectrum bias likely present in prior studies. As sub-aims in both groups, possible further improvement of the algorithmic outcomes with longitudinal CSA measures will also be examined. The overall objective is to develop intuitive, reliable and reproducible CSA-based clinical measures by correlating them with established neuropsychiatric and imaging markers, determining their efficacy in clinical populations, and determining how they change over time. As a result, this research will validate specific speech traits as useful diagnostic markers of neurocognitive disease and explain why those markers differ between patient groups, both of which are major steps towards the design of novel and easily implemented tools in the screening of NCDs such as Alzheimer's disease.