Using Artificial Intelligence to Predict Cognitive Training Response in Amnestic Mild Cognitive Impairment

使用人工智能预测遗忘型轻度认知障碍患者的认知训练反应

基本信息

批准号：
10572105
负责人：
JOSEPH M GULLETT
金额：
$ 16.01万
依托单位：
UNIVERSITY OF FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-05-01 至 2028-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10572105
关键词：
Activities of Daily Living Address Adherence Alzheimer&apos s Disease Alzheimer&apos s disease risk Analysis of Variance Artificial Intelligence Attention Behavior Therapy Biometry Brain Brain region Characteristics Clinical Clinical Trials Cognitive Cognitive Therapy Cognitive aging Complex Control Groups Data Data Set Decision Making Diagnosis Disease Disease Progression Educational Intervention Educational workshop Effectiveness Elderly Florida Foundations Functional Magnetic Resonance Imaging Future Healthcare Home Impairment Individual Infrastructure Intervention Intervention Trial Investigation Investigative Techniques Life Logistic Regressions Machine Learning Magnetic Resonance Imaging Memory Mentored Patient-Oriented Research Career Development Award Mentors Mentorship Methodology Modeling Neuroanatomy Neurobiology Neurocognitive Neurodegenerative Disorders Neurosciences Outcome Patients Pattern Performance Persons Population Populations at Risk Predictive Factor Prefrontal Cortex Proxy Public Health Quality of life Research Research Personnel Resources Rest Risk Sample Size Sampling Science Short-Term Memory Structure Superior temporal gyrus Techniques Thinking Training Training Programs Training and Education Universities Work amnestic mild cognitive impairment behavioral clinical trial career career development clinical translation cognitive performance cognitive training cohort comparison control design executive function experience gray matter group intervention high risk improved individual patient innovation machine learning model mild cognitive impairment multimodal neuroimaging neural neuroimaging normal aging payment personalized intervention personalized medicine predicting response predictive modeling processing speed professor random forest recruit responders and non-responders response skills support vector machine tool treatment effect vector white matter

项目摘要

Project Summary/Abstract: This K23 Clinical Trial project will provide Dr. Gullett, an Assistant Professor at the University of Florida, the direct mentored-training needed to address important questions related to intervention response in an amnestic mild cognitive impairment (aMCI) diagnosed population at risk for Alzheimer’s disease. As a neuropsychologist, Dr. Gullett has gained clinical experience in the assessment of neurodegenerative diseases including Alzheimer’s disease and its precursor, MCI, as well as research experience using structural neuroimaging to investigate various clinical disorders. The support provided by the K23 mechanism through the NIA will provide Dr. Gullett with the protected mentored-training needed to build on his current skills and become an expert in clinical neuroscience, machine learning, and behavioral interventions for mild cognitive impairment and Alzheimer’s disease. Career development and training plan: Dr. Gullett’s training plan consists of foundational formal coursework in 1) clinical trials, 2) MCI and Alzheimer’s disease effects, and 3) biostatistics and machine learning investigative techniques. These foundations will be directly applied through mentorship by experts in the fields of behavioral cognitive interventions, neuroimaging, and machine learning, as well as a proposed in-person workshop in functional neuroimaging analysis. This mentored-training plan will provide Dr. Gullett with the expertise to not only carry out the proposed project, but to become a unique and invaluable resource for future collaborative efforts applying neuroscience-based machine learning tools to investigate personalized interventions for Alzheimer’s disease. Research plan: The proposed project will provide the clinical trials training needed for Dr. Gullett to establish the effectiveness of a planned take-home, 12-week cognitive training program in patients with amnestic mild cognitive impairment (N=75; Aim 1). The expert mentorship team proposed has decades of experience in behavioral clinical trials interventions, which will provide the applicant with design and methodology guidance, as well as the recruitment infrastructure and resources needed to successfully carry out the proposed project. Further, this project will provide training in multi-modal neuroimaging-based machine learning to determine the baseline neural, cognitive, and functional factors that distinguish aMCI patients who respond to treatment from those who do not (Aim 2). This innovative approach will ultimately allow the applicant to investigate which of a myriad of features aMCI patients possess at a baseline assessment are the most salient predictors of their ability to improve from a well-validated cognitive training intervention. A project such as this will enable Dr. Gullett to develop a unique skillset to facilitate an R01-level academic career tasked with providing individual aMCI patients personalized interventions based on their own unique neurobiological and cognitive features.

项目摘要/摘要：这个K23临床试验项目将为佛罗里达大学的助理教授Gullett博士提供直接需要鉴定培训，以解决与干预反应有关的重要问题认知障碍（AMCI）诊断出患阿尔茨海默氏病风险的人口。作为神经心理学家Gullett博士在评估神经退行性疾病（包括阿尔茨海默氏病）和它的前体MCI以及使用结构神经影像学研究各种临床的研究经验疾病。 K23机制通过NIA提供的支持将为Gullett博士提供受保护的支持需要鉴定培训才能以他当前的技能为基础，并成为临床神经科学，机器的专家对轻度认知障碍和阿尔茨海默氏病的学习和行为干预措施。职业发展和培训计划：Gullett博士的培训计划包括1中的基础正式课程）临床试验，2）MCI和阿尔茨海默氏病影响，以及3）生物统计学和机器学习调查技术。这些基金会将通过行为领域的专家直接通过Mentalship直接应用认知干预措施，神经影像学和机器学习以及功能性的面对面研讨会神经影像分析。该修改培训计划将为Gullett博士提供专业知识，不仅执行拟议的项目，但要成为应用程序的独特且宝贵的资源基于神经科学的机器学习工具，以调查阿尔茨海默氏病的个性化干预措施。研究计划：拟议的项目将提供Gullett博士建立所需的临床试验培训计划的带回家，为期12周的认知训练计划的有效性障碍（n = 75;目标1）。提出的专家Mentalship团队在行为临床方面拥有数十年的经验试验干预措施将为申请人提供设计和方法论指导以及招聘成功执行拟议项目所需的基础设施和资源。此外，该项目将提供基于多模式神经影像学的机器学习培训，以确定基线神经元，认知和区分AMCI患者的功能因素与不接受治疗的患者（AIM 2）。这创新的方法最终将允许适用的方法调查AMCI患者的多种功能在基线评估中拥有的最显着预测因素是它们从验证良好的验证中提高的能力认知训练干预措施。这样的项目将使Gullett博士能够发展出独特的技能，以促进 R01级的学术职业的任务是根据他们的AMCI患者的个性化干预措施自己独特的神经生物学和认知特征。