Functional Connectomics Component - George

摘要 TSRI-ARC和其他人已经确定了许多分子变化和失调， 特定的神经回路，包括延伸的杏仁核，导致过量的酒精 依赖动物的饮水然而，有大量证据表明，这种复杂的 行为状态和相关的行为在整个大脑中编码成几十个，如果不是一个。 100个脑区不幸的是，单细胞全脑的功能连接 由于技术限制，戒酒期间的网络在很大程度上是未知的。的 Functional Connectomics组件将使用单细胞全脑成像弥合这一差距 立即早期基因，以确定与药理学相关的网络机制， 干预措施（具体目标1）和回路特定干预措施（具体目标2）， 戒断期间的成瘾行为总体假设是FDA批准的 用于治疗酒精的药物和ARC相关的实验化合物候选物 使用紊乱使网络模块化正常化，停用扩展杏仁核网络，以及 强化大脑皮层网络我们还将检验以下假设： 其他ARC成分显示的外侧下丘脑-边缘下核-杏仁核通路， 减少成瘾行为也会增加大脑模块化，并确定特定的 与这些操作相关的子网机制。采用先进 计算网络分析，包括图论，机器学习框架，最小 网络分析和先进的网络比较将使我们能够识别全脑 预测行为状态并识别连接组学变化的重复模式的网络 来预测减少成瘾行为的干预措施的治疗效果。

Abstract The TSRI-ARC and others have identified numerous molecular changes and dysregulations of specific neuronal circuits, including the extended amygdala, contributing to excessive alcohol drinking in dependent animals. However, there is considerable evidence that such complex behavioral states and associated behaviors are encoded throughout the brain in dozens if not a hundred brain regions. Unfortunately, the functional connectivity of single-cell whole-brain networks during alcohol abstinence is largely unknown because of technical limitations. The Functional Connectomics component will bridge this gap using single-cell whole-brain imaging of immediate-early genes to identify the network mechanisms associated with pharmacological interventions (Specific Aim 1) and circuit-specific interventions (Specific Aim 2) that decrease addiction-like behaviors during abstinence. The overarching hypothesis is that FDA-approved medications and ARC-related experimental compounds candidates for the treatment of alcohol use disorder normalize network modularity, deactivate the extended amygdala network, and strengthen the cortical networks. We will also test the hypothesis that manipulations of the lateral hypothalamus-infralimbic-amygdala pathway shown by other ARC components to decrease addiction-like behaviors will also increase brain modularity and identify the specific subnetwork mechanisms associated with these manipulations. The use of advanced computational network analysis, including graph theory, machine learning frameworks, minimal network analysis, and advanced network comparisons, will allow us to identify whole-brain networks that predict behavioral states and identify repetitive patterns of connectomics changes that predict the therapeutic effects of interventions that decrease addiction-like behavior.