Intrinsic Insular-Frontal Networks Predict Future Nicotine Dependence Severity.

Featured Paper of the Month – October 2019.

Smoking remains a major public health burden, with approximately 20% of the world’s population engaging in regular smoking. Given the high relapse rate among smokers who enter treatments programs, early identification of vulnerable individuals, before the conversion from casual experimentation to regular smoking and addiction, is an important milestone to understand, prevent and potentially minimize nicotine dependence. Using a rodent model of nicotine dependence, we developed a quantitative predictor of subsequent nicotine dependence severity using graph theory-based analyses of fMRI BOLD resting state data collected at baseline, prior to any drug experience. Using an entirely data driven, hypothesis-free analytic strategy, we observed that connectivity between insular regions before drug exposure can predict dependence severity following subsequent nicotine exposure. This finding is in stark contrast to current biomarkers that have observed correlative relationships between brain connectivity and addiction-related behaviors observed after the onset and progression of a substance use disorder. The results of our preclinical model offer a novel finding of a predictive biomarker of addiction severity and provide a framework upon which intervention-based and mechanistic experiments can investigate the role of these brain modules in nicotine dependence, relapse and successful abstinence.

Publication Information

Hsu, Li-Ming; Keeley, Robin J; Liang, Xia; Brynildsen, Julia K; Lu, Hanbing; Yang, Yihong; Stein, Elliot A

Intrinsic Insular-Frontal Networks Predict Future Nicotine Dependence Severity. Journal Article

In: J Neurosci, vol. 39, no. 25, pp. 5028–5037, 2019, ISSN: 1529-2401 (Electronic); 0270-6474 (Linking).

Abstract | Links

@article{Hsu:2019aa,

title = {Intrinsic Insular-Frontal Networks Predict Future Nicotine Dependence Severity.},

author = {Li-Ming Hsu and Robin J Keeley and Xia Liang and Julia K Brynildsen and Hanbing Lu and Yihong Yang and Elliot A Stein},

url = {https://www.ncbi.nlm.nih.gov/pubmed/30992371},

doi = {10.1523/JNEUROSCI.0140-19.2019},

issn = {1529-2401 (Electronic); 0270-6474 (Linking)},

year  = {2019},

date = {2019-06-19},

journal = {J Neurosci},

volume = {39},

number = {25},

pages = {5028--5037},

address = {Neuroimaging Research Branch, National Institute on Drug Abuse, Intramural Research Program, Baltimore, Maryland 21224, and.},

abstract = {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 before chronic nicotine administration can help to 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 d; dependence severity was quantified using precipitated withdrawal behaviors collected before, during, and following forced nicotine abstinence. Resting-state fMRI functional connectivity (FC) before 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 five modules in the rat brain. A metric of intermodule connectivity, participation coefficient, 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, insular, and sensory). Higher FC among these three submodules and three of the five originally identified modules (striatal, frontal-executive, and sensory association) also predicted dependence severity. These data suggest that predispositional, intrinsic differences in circuit strength between insular-frontal-based brain networks before drug exposure may identify those at highest risk for 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 before 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.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

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 before chronic nicotine administration can help to 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 d; dependence severity was quantified using precipitated withdrawal behaviors collected before, during, and following forced nicotine abstinence. Resting-state fMRI functional connectivity (FC) before 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 five modules in the rat brain. A metric of intermodule connectivity, participation coefficient, 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, insular, and sensory). Higher FC among these three submodules and three of the five originally identified modules (striatal, frontal-executive, and sensory association) also predicted dependence severity. These data suggest that predispositional, intrinsic differences in circuit strength between insular-frontal-based brain networks before drug exposure may identify those at highest risk for 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 before 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.