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

项目总结/摘要 这个K23计划旨在提供一个早期职业临床心理学家和神经科学家（亚历山大博士 Weigard）与导师，培训和必要的资源，以启动作为一个独立的病人的职业生涯- 导向的研究者集中使用先进的计算方法来阐明病因机制 物质使用障碍（SUD），并为患者产生有意义的预测。候选人将工作 通过完成一个研究项目，重点评估两个 先进的计算方法可以促进从神经科学数据中选择特征， 与青年SUD风险的个体化预测相关。虽然现有的研究在发展 神经科学已经确定了多种与SUD发展相关的早期风险因素， 在群体水平上，目前缺乏使用神经认知数据的大规模可复制研究 对青年个体的SUD结果进行可靠和可推广的预测。在拟议的项目中， 候选人将联合收割机结合他在认知计算模型方面的现有专业知识， 预测信息学方法，以评估是否有两个先进的计算方法，a）顺序 采样模型（SSM）的认知和B）网络神经科学，可以用来提取特征，从 纵向神经认知数据增强了对青少年SUD结果的预测。候选人将 对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参数对提高多变量SUD性能的附加益处 预测模型，以及2）识别v的多变量神经特征，v是具有 有希望的联系，物质的使用，并确定这一签名的潜力， ABCD中SUD的前体（青春期中期开始使用物质）和分化的年轻人 新收集的试点样本中的SUD成人。完成以下培训目标将 确保候选人既能执行拟议的项目，又能确立自己作为一个独立的 研究者谁是装备精良，以进行未来的项目，从这项工作如下：1）掌握原则 机器学习模型开发和纵向数据集测试，2）建立专业知识， 使用多变量网络神经科学方法进行特征选择和预测，3）增加 临床和流行病学知识的SUD危险因素超出神经认知，和4）提高 成为一名独立的以病人为导向的调查员所需的专业技能。拟议 K23旨在朝着先进的计算神经科学方法的发展迈出关键一步 通过识别个体SUD风险的可靠预测因子，最终为SUD预防工作提供信息， 为了这个更大的目标，让候选人独立地进行前沿研究。

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.