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

摘要

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.