Post by Stephanie Williams
What's the science?
Some individuals are more susceptible to developing a dependence to nicotine than others. Identifying biomarkers that distinguish individuals at risk for developing addictions could inform treatment plans and preventative measures. This week in the Journal of Neuroscience, Hsu & Keeley and colleagues characterized a potential biomarker for risk of nicotine addiction.
How did they do it?
The authors administered nicotine to 10 rats for two weeks and used neuroimaging (functional magnetic resonance imaging; fMRI) to look for biomarkers that distinguished rats that became dependent on nicotine from rats that did not. To characterize dependence on nicotine, the authors looked for a cluster of common behaviors characteristic of nicotine withdrawal, including teeth chattering, gasping, body shakes, yawns, and escape attempts. They checked for the behavioral symptoms before administering nicotine, at one day and again at two weeks after stopping the administration of nicotine. Rats were observed for 50 minutes, and assigned a score depending on the number of behaviors they exhibited.
To assess whether brain activity could predict nicotine dependence and withdrawal, the authors analyzed the relationship between functional connectivity between different brain regions on day 1 (the “drug naïve” brain) and dependence behavioral scores. To measure functional connectivity the authors calculated correlations between the signal fluctuations in different brain regions while the rats were lightly anesthetized in the MRI scanner. Analysis resulted in a matrix defining the strength of the correlation between each brain region with every other brain region (“functional connectivity”). The authors then used a graph theory framework (modularity analysis) to define 5 brain modules (groups of brain regions that are similarly connected) as well as several sub-modules. After identifying modules in the brain, they assessed the strength of connections within modules and between modules, as well as the relationship between connectivity strength and drug dependence and reversal.
What did they find?
The authors found that functional connectivity between modules (inter-module connectivity) measured before rats were exposed to nicotine could predict the severity of nicotine dependence. Connectivity between a specific module, the insular-frontal module and the 4 other modules before nicotine exposure predicted dependence severity and abstinence-induced reversal. Specifically, stronger connections between the insular-frontal module and other modules was correlated with greater dependence severity. Weaker connections between the insular-frontal module and other modules was correlated with enhanced dependence reversal after abstinence from nicotine. Intra-module connectivity, in contrast, did not predict dependence severity.
The authors further subdivided the insular-frontal module into three sub-modules and found that (1) the inter-module connectivity of all three submodules predicted dependence on nicotine and (2) the inter-module connectivity of two of the three submodules --the insula and frontal-motor submodules--predicted dependence reversal after two weeks of nicotine abstinence. These results suggest that intrinsic insular-frontal circuits could be used as biomarkers of nicotine dependence risk.
What's the impact?
This is the first study to identify patterns of connectivity that can predict future risk of nicotine dependence. The authors identified an insular-frontal cortical biomarker of nicotine dependence risk. The biomarker has the potential to identify individuals at risk for developing dependence, as well as individuals likely to recover after a period of abstinence.
Hsu, L-M. Keeley, R.J., et al. Intrinsic Insular-Frontal Networks Predict Future Nicotine Dependence Severity. Journal of Neuroscience (2019). Access the original scientific publication here.