Neurocircuitry for modeling drug effects

The identification and functional understanding of the neurocircuitry that mediates alcohol and drug effects that are relevant for the development of addictive behavior is a fundamental challenge in addiction research. Here we introduce an assumption-free construction of a neurocircuitry that mediates acute and chronic drug effects on neurotransmitter dynamics that is solely based on rodent neuroanatomy. Two types of data were considered for constructing the neurocircuitry: (1) information on the cytoarchitecture and neurochemical connectivity of each brain region of interest obtained from different neuroanatomical techniques; (2) information on the functional relevance of each region of interest with respect to alcohol and drug effects. We used mathematical data mining and hierarchical clustering methods to achieve the highest standards in the preprocessing of these data. Using this approach, a dynamical network of high molecular and spatial resolution containing 19 brain regions and seven neurotransmitter systems was obtained. Further graph theoretical analysis suggests that the neurocircuitry is connected and cannot be separated into further components. Our analysis also reveals the existence of a principal core subcircuit comprised of nine brain regions: the prefrontal cortex, insular cortex, nucleus accumbens, hypothalamus, amygdala, thalamus, substantia nigra, ventral tegmental area and raphe nuclei. Finally, by means of algebraic criteria for synchronizability of the neurocircuitry, the suitability for in silico modeling of acute and chronic drug effects is indicated. Indeed, we introduced as an example a dynamical system for modeling the effects of acute ethanol administration in rats and obtained an increase in dopamine release in the nucleus accumbensa hallmark of drug reinforcementto an extent similar to that seen in numerous microdialysis studies. We conclude that the present neurocircuitry provides a structural and dynamical framework for large-scale mathematical models and will help to predict chronic drug effects on brain function.