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.