Altered structural connectivity and functional brain dynamics in individuals with heavy alcohol use

Abstract

Heavy alcohol use and its associated conditions, such as alcohol use disorder (AUD), impact millions of individuals worldwide. While our understanding of the neurobiological correlates of AUD has evolved substantially, we still lack models incorporating whole-brain neuroanatomical, functional, and pharmacological information under one framework. Here, we utilize diffusion and functional magnetic resonance imaging to investigate alterations to brain dynamics inN= 130 individuals with a high amount of current alcohol use. We compared these alcohol using individuals toN= 308 individuals with minimal use of any substances. We find that individuals with heavy alcohol use had less dynamic and complex brain activity, and through leveraging network control theory, had increased control energy to complete transitions between activation states. Further, using separately acquired positron emission tomography (PET) data, we deploy anin silicoevaluation demonstrating that decreased D2 receptor levels, as found previously in individuals with AUD, may relate to our observed findings. This work demonstrates that whole-brain, multimodal imaging information can be combined under a network control framework to identify and evaluate neurobiological correlates and mechanisms of AUD.

Reducing maladaptive behavior in neuropsychiatric disorders using network modification

“…Overall, we are concerned that these types of systems neuroscience analyses lean heavily on the assumption that individual parts of the brain or any network should have clear roles that we can understand [41,42]. Rather, there is substantial evidence that understanding synergistic neural behavior across multiple neural populations is necessary to understand complex behaviors [43][44][45]. Furthermore, it has been shown that traditional systems neuroscience tools like those used here are not capable of providing thorough explanations for complex information processing systems and that non-linear dynamical models, such as neural network models we propose to use in the future to capture in vivo network behavior, are better suited to this goal [46].…”