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Intrinsic insular-frontal networks predict future nicotine dependence severity.

Although 60% of the US population have tried smoking cigarettes, only 16% smoke regularly. Identifying this susceptible subset of the population before the onset of nicotine dependence may encourage targeted early interventions to prevent regular smoking and/or minimize severity. While prospective neuroimaging in human populations can be challenging, preclinical neuroimaging models prior to chronic nicotine administration can help develop translational biomarkers of disease risk. Chronic, intermittent nicotine (0, 1.2 or 4.8 mg/kg/d (N = 10-11/group)) was administered to male Sprague Dawley rats for 14 days; dependence severity was quantified using precipitated withdrawal behaviors collected prior to, during and following forced nicotine abstinence. Resting state fMRI functional connectivity (FC) prior to drug administration was subjected to a graph theory analytical framework to form a predictive model of subsequent individual differences in nicotine dependence. Whole brain modularity analysis identified 5 modules in the rat brain. A metric of inter-module connectivity, participation coefficient (PC), of an identified insular-frontal cortical module predicted subsequent dependence severity, independent of nicotine dose. To better spatially isolate this effect, this module was subjected to a secondary exploratory modularity analysis, which segregated it into three submodules (frontal-motor, insula and sensory). Higher FC between these 3 sub-modules and 3 of the 5 originally identified modules (striatal, frontal-executive and sensory association) also predicted dependence severity. These data suggest that pre-dispositional, intrinsic differences in circuit strength between insular-frontal based brain networks prior to drug exposure may identify those at highest risk to the development of nicotine dependence. Significance statement: Developing biomarkers of individuals at high risk for addiction before the onset of this brain-based disease is essential for prevention, early intervention and/or subsequent treatment decisions. Using a rodent model of nicotine dependence and a novel data-driven, network-based analysis of resting state fMRI data collected prior to drug exposure, functional connections centered on an intrinsic insular-frontal module predicted the severity of nicotine dependence after drug exposure. The predictive capacity of baseline network measures was specific to inter-regional but not within-region connectivity. While insular and frontal regions have consistently been implicated in nicotine dependence, this is the first study to reveal that innate, individual differences in their circuit strength have the predictive capacity to identify those at greatest risk for and resilience to drug dependence.

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