Network Analysis and Computational Modeling Core

网络分析和计算建模核心

• 批准号: 10526832
• 负责人: CHRISTOPHER COURT LAPISH
• 金额: $ 15.88万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 1989
• 资助国家: 美国
• 起止时间: 1989-12-01 至 2027-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10526832
• 关键词: ARHGEF5 gene; Alcohol consumption; Alcohols; Area; Articulation; Artificial Intelligence; Behavioral; Biophysics; Brain; Complement; Computer Models; Data; Data Analyses; Data Set; Electrophysiology (science); Functional Magnetic Resonance Imaging; Genetic Determinism; Goals; Heritability; Human; Impairment; Indiana; Lead; Link; Measures; Modeling; NCAM1 gene; Network-based; Neurobiology; Neurophysiology - biologic function; Pathology; Pathway Analysis; Population; Population Analysis; Predisposition; Prosencephalon; Research; Risk; Rodent; Services; Signal Transduction; Source; Statistical Data Interpretation; System; Techniques; Testing; Translations; Work; addiction; alcohol research; alcohol response; alcohol use disorder; dynamic system; experience; high intensity drinking; insight; multidimensional data; neural; neural circuit; novel; novel strategies; synergism; translational potential; virtual

Project Summary This renewal of the P60 Indiana Alcohol Research Center (IARC) will identify the changes in brain activity that predispose one to Binge and High Intensity Drinking (BHID). Towards this goal, the IARC will be collecting large, dense data sets from humans and rodents with a range of techniques that acquire multiple, simultaneous measures of neural function. This creates a critical need for an IARC service core that provide expertise in network analysis and computational modeling to the center’s research components. The goal of the NACM is to interrogate the brain networks and neural circuits that underlie BHID in a translational manner. The objective of the NCAM is to therefore increase synergy and translation across species and components, and to integrate the center’s data into computational models capable of articulating the pathology in neural circuits that underlie BHID. The NACM will work closely with the research components to implement and develop analyses and computational modeling techniques that capture network and systems-level interactions. Accomplishing this will provide added rigor and more integrated, synergistic conclusions generated by IARC components. Thus, the NCAM will provide advanced, targeted analyses and computational models that increase synergy among the components. This will disseminate current IARC expertise in statistical analyses and computational modeling to the center more broadly. As a core, the NACM does not set out to test specific hypotheses, but rather, to work closely with the research components to add rigor and synergy. This allows each component to assess their hypotheses more effectively, with the long-term goal of providing more integrated model of the mechanisms of BHID. This will be accomplished in three Specific Aims: Specific Aim 1: Identify altered, potentially translational, network functions in BHID in humans and rodents. Specific Aim 2: Apply population analysis and artificial intelligence-based approaches to identify altered computations in BHID to human and rodent data. Specific Aim 3: Build computational models of corticolimbic systems altered in BHID. The positive impact of the NACM will be to provide insight into how computations performed by brain networks are altered in those at risk for BHID and AUD. Understanding this will inspire novel approaches to treatment that more tightly link heritable changes in neural circuit function to BHID.

项目概要 P60 印第安纳酒精研究中心 (IARC) 的更新将确定大脑活动的变化， 为实现这一目标，国际癌症研究机构 (IARC) 正在收集暴饮暴食和高强度饮酒 (BHID) 的信息。 来自人类和啮齿动物的大量、密集的数据集，采用一系列技术来获取多个、 这对提供神经功能的 IARC 服务核心产生了迫切的需求。 该中心研究部分的目标是网络分析和计算建模方面的专业知识。 NACM 旨在以翻译方式询问 BHID 背后的大脑网络和神经回路。 因此，NCAM 的目标是增强跨物种和成分的协同作用和转化， 并将该中心的数据整合到能够阐明神经病理学的计算模型中 BHID 背后的电路将与研究组件密切合作来实施和实施。 开发捕获网络和系统级交互的分析和计算建模技术。 实现这一目标将为 IARC 提供更加严格、更加综合、协同的结论 因此，NCAM 将提供先进的、有针对性的分析和计算模型。 加强各组成部分之间的协同作用，这将传播 IARC 当前在统计分析方面的专业知识。 作为核心，NACM 并不打算进行具体测试。 假设，而是与研究组成部分密切合作，以增加严谨性和协同作用。 每个组成部分更有效地评估他们的假设，长期目标是提供更多 BHID 机制的综合模型将通过三个具体目标来实现： 具体目标 1：识别人类和啮齿动物 BHID 中改变的、潜在翻译的网络功能。 具体目标 2：应用群体分析和基于人工智能的方法来识别改变的 BHID 中对人类和啮齿动物数据的计算。 具体目标 3：建立 BHID 中改变的皮质边缘系统的计算模型。 NACM 的积极影响将是深入了解大脑网络如何执行计算 那些有 BHID 和 AUD 风险的人的基因发生了改变，了解这一点将激发新的治疗方法。 神经回路功能的遗传性变化与 BHID 更紧密地联系在一起。