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

摘要 神经认知障碍(NCDs)的早期和准确诊断对于计划、治疗和 研究转介，但需要时间和专业知识往往是初级保健提供者无法获得的。演讲和 在几种非传染性疾病的病程早期，语言经常受损。之前的研究已经证明 计算机语音分析(CSA)的诊断潜力及其在健康对照组和对照组之间的差异 轻度认知障碍(MCI)和阿尔茨海默病等疾病。然而，有几个 使CSA成为诊断可行的筛查工具必须采取的其他步骤。这项建议包括 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技术应用于临床的领域：1)计算语言学和副语言学，2) 疾病的纵向标志，以及3)新的传播技术的设计。作为这次培训的一部分， 学术和专业技能，包括研究中的道德，也将得到扩大。独一无二的资质 为确保这一培训和研究的成功，已经挑选了指导和咨询小组。 这项研究是对两个不同的研究组进行的前瞻性、纵向、观察性、队列调查。 第一组是经过高度挑选并具有良好特征的健康对照阿尔茨海默氏症研究队列 疾病和MCI受试者(A组)。在A组，机器学习的性能和重复性 将改进算法，使用CSA将阿尔茨海默病和MCI与健康对照组区分开来。 多元回归和基于体素的形态测量将用于更好地了解可能驱动群体的因素 A组CSA指标差异也有统计学意义。该算法的临床应用将在 一项基于临床的不同非传染性疾病患者队列(B组)以减少先前可能存在的频谱偏差 学习。作为两组的子目标，可能进一步改进算法结果 还将审查纵向CSA措施。总体目标是开发直观、可靠和 通过将基于CSA的临床测量与已建立的神经精神病学和影像学相关联来实现可重复性的 标记物，确定它们在临床人群中的疗效，并确定它们随时间的变化。作为一名 结果，这项研究将验证特定的言语特征作为神经认知疾病的有用诊断标志 并解释为什么这些标记物在患者组之间不同，这两个都是迈向 在阿尔茨海默病等非传染性疾病筛查中设计新的、易于实施的工具。

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 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 this training and research. This 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) to 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.