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。人脑成像极大地促进了我们的当前 了解aud，但还有更多的理解。最近，人类神经科学一直是 通过网络科学和神经影像学的整合（现已创造的网络神经科学）的整合。 功能性脑成像用于生成网络以检查同步的互连组 大脑区域。该项目的总体假设是大脑同步只是一半的一半 大脑网络故事。这项工作断言，功能性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同步网络 称为合作功能网络（CFN）。第二层是一个抗拒的网络 同步，称为不透水的功能网络（IFN）。 IFN对于转移至关重要 在子网内部和子网之间的同步，以支持认知需求的转变。两层 在张力方面共存，每个人都扮演自己的角色，以支持稳定但灵活的大脑功能。这个项目 将数学建模和仿真与预测饮酒的方法相结合 青春期（NCANDA）数据集的国家酒精和神经发育联盟的行为。 该项目的成功将证明CFN和IFN都是更完整的重要组成部分 了解正常和异常大脑功能。迫切需要更深入的理解 饮酒/滥用以及更好的诊断和治疗AUD。不幸的是，潜在的生物标志物 针对这些条件的治疗试验不断失败。这个项目可能具有变革性 潜力随着它带来了以前未被发现的大脑的组织原则，并可能导致新的 基于CFN和IFN的综合知识的诊断和治疗策略设计。在 除了改变我们对AUD的理解之外，这项工作有可能革新临床 研究和患有一系列脑部疾病的患者的护理。