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

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技术引入临床实践的领域：1）计算语言学和语言学，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 为确保培训和研究取得成功，挑选了一些指导和咨询小组。 本研究是对两个不同研究组的前瞻性、纵向、观察性、队列研究。 第一组是一个高度选择和良好的特点研究队列的健康对照，阿尔茨海默氏症 疾病和MCI受试者（A组）。在A组中，机器学习的性能和可重复性 算法将被改进，以区分阿尔茨海默病和MCI从健康对照使用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驱动组 A组中CSA测量值的差异也是如此。该算法的临床应用将在 不同NCD患者的基于临床的队列（B组），以减少既往研究中可能存在的谱偏倚 问题研究作为两组的子目标，可能进一步改善算法结果， 此外，亦会研究各项持续性自我评估措施。总体目标是开发直观，可靠， 通过将其与已建立的神经精神病学和影像学相关联， 标记物，确定它们在临床人群中的功效，并确定它们如何随时间变化。作为 因此，本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物 并解释为什么这些标志物在患者群体之间存在差异，这两个都是朝着 设计新的和易于实施的工具，用于筛查阿尔茨海默病等非传染性疾病。

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 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.