Leveraging computational models of neurocognition to improve predictions about individual youths' risk for substance use disorders

利用神经认知的计算模型来改进对青少年个体物质使用障碍风险的预测

基本信息

批准号：
10609805
负责人：
Alexander Weigard
金额：
$ 19.66万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2026-04-30
项目状态：
未结题

项目摘要

PROJECT SUMMARY/ABSTRACT This K23 proposal seeks to provide an early-career clinical psychologist and neuroscientist (Dr. Alexander Weigard) with the mentorship, training, and resources necessary to launch a career as an independent patient- oriented investigator focused on using advanced computational methods to elucidate etiological mechanisms of substance use disorders (SUDs) and generate meaningful predictions for patients. The candidate will work towards this long-term goal through the completion of a research project focused on assessing whether two advanced computational methods can facilitate the selection of features from neuroscientific data that are relevant for the individualized prediction of SUD risk in youth. Although extant research in developmental neuroscience has identified multiple early risk factors that are associated with development of SUD at the group level, there is currently a dearth of large scale, replicable research in which neurocognitive data are used to make reliable and generalizable predictions of SUD outcomes for individual youth. In the proposed project, the candidate will combine his existing expertise in computational models of cognition with new training in predictive informatics methods to assess whether two advanced computational approaches, a) sequential sampling models (SSMs) of cognition and b) network neuroscience, can be used to extract features from longitudinal neurocognitive data that enhance the prediction of youths’ SUD outcomes. The candidate will conduct extensive analyses with two large data sets (Michigan Longitudinal Study, Adolescent Brain Cognitive Development Study) and collect pilot data with 60 young adults to accomplish the following research aims: 1) Quantify the added benefit of SSM parameters for improving the performance of multivariate SUD prediction models, and 2) Identify the multivariate neural signature of v, an SSM parameter with promising links to substance use, and determine the potential of this signature for predicting a precursor to SUD (substance use initiation in mid-adolescence) in ABCD and differentiating young adults with SUDs in the newly-collected pilot sample. Completion of the following training objectives will ensure that the candidate can both carry out the proposed project and establish himself as an independent investigator who is well-equipped to conduct future projects following from this work: 1) Mastering principles of machine learning model development and testing in longitudinal data sets, 2) building expertise in using multivariate network neuroscience methods for feature selection and prediction, 3) increasing clinical and epidemiological knowledge of SUD risk factors beyond neurocognition, and 4) improving professional skills necessary to become an independent patient-oriented investigator. The proposed K23 aims to take a crucial step towards the development of advanced computational neuroscience methods that may ultimately inform SUD prevention efforts by identifying reliable predictors of individuals’ SUD risk, and to set the candidate up to independently conduct leading edge research in the interest of this larger goal.

项目概要/摘要这项 K23 提案旨在提供一名职业生涯早期的临床心理学家和神经科学家（亚历山大博士） Weigard）拥有开展独立患者职业所需的指导、培训和资源- 定向研究人员专注于使用先进的计算方法来阐明病因机制药物滥用障碍 (SUD) 并为患者生成有意义的预测。通过完成一个研究项目来实现这一长期目标，该项目的重点是评估两个先进的计算方法可以促进从神经科学数据中选择特征尽管存在发育方面的研究，但与青少年 SUD 风险的个体化预测相关。神经科学已确定多种与 SUD 发展相关的早期危险因素在群体层面上，目前缺乏使用神经认知数据的大规模、可复制的研究在拟议的项目中，对 SUD 的结果做出可靠且普遍的预测。候选人将把他在认知计算模型方面的现有专业知识与新的培训相结合预测信息学方法来评估两种先进的计算方法是否，a）顺序认知采样模型（SSM）和 b）网络神经科学，可用于从纵向神经认知数据可增强对青少年 SUD 结果的预测。使用两个大型数据集进行广泛的分析（密歇根纵向研究、青少年大脑认知发展研究）并收集 60 名年轻人的试点数据，以实现以下研究目标：1）量化 SSM 参数对于提高多元 SUD 性能的额外优势预测模型，以及 2) 识别 v 的多元神经特征，这是一个 SSM 参数与物质使用有希望的联系，并确定该特征用于预测药物滥用的潜力 ABCD 中 SUD（青春期中期开始物质使用）的前身和区分年轻人新收集的试点样本中患有 SUD 的成年人将完成以下培训目标。确保候选人能够执行拟议的项目并确立自己的独立地位研究者有能力根据这项工作开展未来的项目：1）掌握原理纵向数据集的机器学习模型开发和测试，2）建立以下方面的专业知识使用多元网络神经科学方法进行特征选择和预测，3）增加神经认知之外的 SUD 危险因素的临床和流行病学知识，以及 4) 提高成为独立的以患者为中心的研究者所必需的专业技能。 K23旨在向先进计算神经科学方法的发展迈出关键一步通过识别个人 SUD 风险的可靠预测因素，最终可以为 SUD 预防工作提供信息，以及为了这个更大的目标，让候选人独立进行前沿研究。