CRCNS: Functional Brain Networks with Tensioned Stability for Optimal Processing

CRCNS：具有张力稳定性的功能性大脑网络，可实现最佳处理

• 批准号: 10683341
• 负责人: Erik Bollt
• 金额: $ 33.38万
• 依托单位: WAKE FOREST UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10683341
• 关键词: Adolescence; Alcohol consumption; Alcohols; Area; Biological; Brain; Brain Diseases; Brain imaging; Brain region; Clinical Research; Cognitive; Coin; Data; Data Set; Development; Diagnosis; Etiology; Formulation; Functional Imaging; Future; Human; Knowledge; Maps; Mathematical Model Simulation; Mathematics; Methodology; Methods; Neurosciences; Participant; Patient Care; Play; Principal Investigator; Process; Reproducibility; Rest; Role; Science; Series; Time; Work; alcohol misuse; alcohol use disorder; brain dysfunction; cooperative study; design; drinking behavior; dynamic system; effective therapy; flexibility; neural; neurodevelopment; neuroimaging; novel diagnostics; novel therapeutic intervention; potential biomarker; programs; success; targeted therapy trials; underage drinking

Understanding the brain processes underlying alcohol use and misuse are essential for the development of effective treatments for alcohol use disorder or AUD. Human brain imaging has greatly contributed to our current understanding of AUD, but much more remains to be understood. Most recently, human neuroscience has been transformed by the integration of network science and neuroimaging (now coined network neuroscience). Functional brain imaging is used to generate networks to examine interconnected groups of synchronized brain regions. The overarching hypothesis of this project is that brain synchronization is only half of the brain network story. This work asserts that functional brain networks actually have two critical sublayers. The first layer is the well-established network of synchronization that is identified using correlation methods, called the cooperative functional network (cFN). The second layer is a proposed network that resists synchronization and is called the impervious functional network (iFN). The iFN is essential for shifting synchronization within and between subnetworks to support shifts in cognitive demands. The two layers coexist in tension, each playing their own role to support stable, yet flexible, brain function. This project combines mathematical modeling and simulation with application of the methods to predict alcohol drinking behavior in National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) data set. Success of this project will demonstrate that both cFN and iFN are essential components of a more complete understanding of normal and abnormal brain function. There is a desperate need for a deeper understanding of alcohol use/misuse and for better diagnosis and treatment of AUD. Unfortunately, potential biomarkers and treatment trials targeting these conditions have continually failed. This project could have transformative potential as it brings forth a previously undiscovered organizational principle of the brain, and could lead to new diagnostic and therapeutic strategies designs based on the combined knowledge of the cFN and iFN. In addition to transforming our understanding of AUD, this work has the potential to revolutionize clinical studies and the care of patients with a range of brain disorders.

了解酒精使用和滥用背后的大脑过程对于发展 有效治疗酒精使用障碍或AUD。人脑成像在很大程度上造就了我们目前的 对澳元的了解，但还有更多的事情需要了解。最近，人类神经科学一直在 由网络科学和神经成像(现在被称为网络神经科学)的整合而转变。 脑功能成像被用来产生网络来检查相互关联的同步组 大脑区域。这个项目的主要假设是大脑同步只有大脑 大脑网络故事。这项研究断言，起作用的大脑网络实际上有两个关键的子层。 第一层是使用相关方法识别的良好建立的同步网络， 称为协作功能网络(CFN)。第二层是一个建议的网络，它可以抵抗 同步，被称为不渗透功能网络(干扰素)。干扰素对于转移来说是必不可少的 子网络内部和子网络之间的同步，以支持认知需求的变化。这两个层面 在紧张中共存，每个人都扮演着各自的角色，以支持稳定而灵活的大脑功能。这个项目 数学建模与仿真相结合的方法在饮酒预测中的应用 国家酒精与青春期神经发育联盟(NCANDA)数据集中的行为。 该项目的成功将证明，CFN和干扰素都是更完整的 了解正常和异常的大脑功能。我们迫切需要更深入的了解 关于酒精使用/滥用以及更好地诊断和治疗AUD的建议。不幸的是，潜在的生物标志物 针对这些疾病的治疗试验不断失败。这个项目可能会有变革性的 潜力，因为它带来了一种以前未被发现的大脑组织原理，并可能导致新的 基于对CFN和干扰素的综合认识的诊断和治疗策略设计。在……里面 除了改变我们对AUD的理解，这项工作还有可能给临床带来革命性的变化 研究和护理一系列脑部疾病的患者。