Project 1: Dynamic functional brain network phenotypes associated with vulnerability to hazardous alcohol consumption Paul Laurienti, Colleen Hanlon, Heather Shappell, Mohsen Bahrami This project is a continuation of the human neuroimaging studies examining brain networks associated with drinking behaviors in the Wake Forest Translational Alcohol Research Center (WF-TARC). The overarching hypothesis of this proposal that the vulnerability to develop hazardous drinking is manifest in dynamic network connectivity within and between the default mode network (DMN) salience network (SN) and the sensorimotor network (SMN). This hypothesis is based on a growing body of resting-state brain network studies suggesting that hazardous drinking and AUD are associated with decreased DMN connectivity and increased SN and SMN connectivity. However, much more research needed on these three intrinsic brain networks, particularly evaluation of network dynamics. The primary goals of this project are to: (Aims 1 & 2) evaluate the network dynamics associated with the development of hazardous drinking and (Aim 3) determine if non-invasive brain stimulation directed at the medial prefrontal cortex (MPFC), a node in the DMN, modulates the network dynamics. To achieve these goals the current study will use advanced quantitative analytics based on Hidden Semi-Markov Modeling (HSMM) and machine learning predictive analytics to identify and characterize functional brain network dynamics associated with vulnerability to developing hazardous drinking. The National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) is a longitudinal study that collected brain imaging and alcohol consumption history from adolescents over a 4-year period. This de- identified and publically available dataset will be used to examine brain network dynamics associated with hazardous drinking (Aims 1 and 2). We will also use de-identified longitudinal brain imaging data collected before and after individuals with AUD received a course of continuous theta burst stimulation (TBS) or sham treatment to the MPFC. This an ideal dataset to determine if dynamic network connectivity within and between the DMN, SN, and SMN is responsive to non-invasive neuromodulation. Understanding the dynamic interconnectivity within and between these circuits can guide future research designed to identify network- based treatment strategies. For example, research on new targets for TBS delivery or behavioral interventions that that rely on particular circuits can utilize information about individual dynamic connectivity profiles based on the outcomes of the proposed studies.