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

• 批准号: 10213907
• 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
• 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
• 来源: https://reporter.nih.gov/project-details/10213907
• 关键词: Address; Adolescence; Adolescent; Adult; Advanced Development; Age; Behavioral; Brain; Brain region; Clinical; Clinical Investigator; Cognition; Cognitive; Collection; Computer Models; Computing Methodologies; Data; Development; Diagnosis; Early Intervention; Ensure; Epidemiology; Etiology; Future; Goals; Image Analysis; Individual; Individual Differences; Informatics; Knowledge; Lead; Link; Longitudinal Studies; Machine Learning; Measures; Mentorship; Methods; Michigan; Modeling; Neurocognition; Neurocognitive; Neurologic; Neurosciences; Neurosciences Research; Outcome; Patients; Performance; Personality; Psychologist; Psychopathology; Public Health; Research; Research Personnel; Research Project Grants; Research Training; Resources; Rest; Risk; Risk Factors; Sampling; Sampling Studies; Scientist; Substance Use Disorder; Testing; Training; Validation; Work; Youth; addiction; adolescent substance use; base; career; cognitive development; cognitive function; computational neuroscience; connectome; design; disorder prevention; disorder risk; early onset substance use; emerging adulthood; feature selection; improved; indexing; interest; large datasets; learning network; longitudinal dataset; meetings; model development; neural patterning; neurocognitive test; neuroimaging; novel; patient oriented; personalized predictions; predictive modeling; predictive signature; preventive intervention; psychosocial; relating to nervous system; skills; statistics; substance use; theories; tool; trait; young adult

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.

项目摘要/摘要 这份K23提案旨在提供一位职业生涯早期的临床心理学家和神经学家(亚历山大博士 Weigard)拥有作为独立患者开始职业生涯所需的导师、培训和资源- 定向调查者专注于使用先进的计算方法来阐明病因机制 对物质使用障碍(SOD)的预测，并为患者产生有意义的预测。候选人会成功的 为了实现这一长期目标，完成了一项研究项目，重点是评估两个 先进的计算方法可以帮助从神经科学数据中选择符合以下条件的特征 与青年SUD风险的个体化预测有关。尽管已有的关于发展的研究 神经科学已经确定了多种早期危险因素，这些危险因素与短暂性脑缺血发作有关。 在群体层面，目前缺乏使用神经认知数据的大规模、可复制的研究 对个别青年的SUD结果做出可靠和可推广的预测。在拟议的项目中， 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 评估两种高级计算方法的预测信息学方法，a)顺序 认知和b)网络神经科学的采样模型(SSM)可用于从 纵向神经认知数据，增强了对青少年SUD结果的预测。候选人将会 使用两个大型数据集进行广泛的分析(密歇根州纵向研究，青少年大脑认知 发展研究)，并与60名年轻人收集试点数据，以实现以下研究目标：1) 量化SSM参数对提高多变量SUD性能的附加效益 预测模型，以及2)识别v的多变量神经特征，SSM参数具有 与物质使用的有希望的链接，并确定此签名预测 青春期中期物质使用启动的前体--ABCD和青年分化 在新收集的试点样本中有肥皂泡的成年人。完成以下培训目标将 确保候选人既能执行提议的项目，又能使自己成为一个独立的 有能力开展后续项目的调查员：1)掌握原则 在纵向数据集中进行机器学习模型开发和测试，2)在以下方面建立专业知识 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 神经认知以外的SUD危险因素的临床和流行病学知识，以及4)改进 具备成为独立的、以病人为中心的调查员所需的专业技能。建议数 K23的目标是朝着发展先进的计算神经科学方法迈出关键的一步 这可能最终通过确定个人的SUD风险的可靠预测因素来为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。