Functional connectivity reconfigurations in risk for alcohol use disorders

Project Summary An important aspect of inherited risk for alcohol use disorder (AUD) is low executive function and adaptive behavioral control. Emerging work shows that such executive abilities are related to activity-induced changes in brain network functional connectivity (FC). We know very little about how inherited AUD risk affects the large-scale network FC supporting executive functions, and the small body of extant research does not study how networks dynamically adapt to changing demands. Our lab’s long-term goal is to understand inherited brain vulnerabilities for AUD. This application’s objective is to determine how AUD risks affect brain network FC reconfiguration during mental state transitions. As informed by our published preliminary findings, our central hypotheses are that (i) inherited AUD risk involves inefficient FC reconfiguration in transitioning between extremes of cognitive and reward engagement, and that (ii) these transitional reconfigurations relate to adaptive control in the key AUD risk domains. Our specific aims are therefore to: Aim 1: Determine how FC reconfiguration during transitions between cognitive engagement and low cognitive load (“rest”) relates to AUD risk factors. Aim 2: Determine how FC reconfiguration during transitions between alcohol-cue stimulation (“appetitive engagement”) and rest relates to AUD risk factors. Aim 3: Determine how FC reconfiguration relates to drinking and alcohol-related problems. Exploratory Aims: Test for (A) joint appetitive and cognitive task network reconfiguration effects; B) multiple mediation effects on drinking, and (C) effects related to loss of control drinking. Our proposed work uses a novel paradigm and analyses to characterize transitions between rest and states of cognitive control and alcohol cue exposure. The work is thus poised to discover new fundamental knowledge about brain network interactions necessary for flexible and adaptive behavioral regulation. Such data are critical to understanding mechanisms of AUD risk. The findings will also be used to develop biomarkers of “disease networks” that can be monitored in treatment research for normalization, or that can predict therapeutic response.