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Systems Biology of Alcohol Addiction: Modeling and validating disease state networks in human and animal brains for understanding pathophysiolgy, predicting outcomes and improving therapy

Periodic Reporting for period 1 - SyBil-AA (Systems Biology of Alcohol Addiction: Modeling and validating disease state networks in human and animal brains for understanding pathophysiolgy, predicting outcomes and improving therapy)

Reporting period: 2016-01-01 to 2017-06-30

In the EU, an adult consumes on average 11 liters of pure alcohol each year – more than in any other region in the world. The impact on society is enormous. Not only is alcohol linked to many non-communicable diseases, including cancers, cardiovascular diseases, liver cirrhosis, neuropsychiatric disorders and fetal alcohol syndrome. It also leads to alcohol addiction – also called alcoholism – the most severe form of alcohol use disorders. Nearly 20 million European citizens may suffer from this disorder which leads to a much larger loss in average life expectancy than life-time smoking.

Alcohol addiction is characterized by cycles of excessive alcohol consumption, interspersed with intervals of abstinence, and frequent relapses. The vulnerability to relapse is a key element of the disease process and blocking relapse is therefore the most important treatment objective. Despite many efforts, the outcomes for even the best studied treatment programs are not impressive, with about half of the patients relapsing within a year. The few pharmacotherapies available for alcohol addiction have not changed this situation because these medications have small effect sizes and only a minority of the patients do benefit from such treatments. Not surprisingly, the impact of these pharmacotherapies on clinical practice is low.

The urgency to understand more about the changes in the brain of alcohol addicts and to find effective treatments is obvious. Considerable research efforts have been made all over the world and led to increased understanding of the psychological and neurobiological processes that underlie addictive disorders. In turn, there was substantial enthusiasm about the prospect of developing novel, mechanism based therapies. So far, these prospects have not played out, indicating that the translation of scientific knowledge into the clinical praxis of addiction therapy is not easy. While this translational gap has complex causes, we think that one important limitation of the current neurobiological view on alcohol addiction is the focus on only a few processes and brain regions, albeit the broad systemic actions of alcohol are commonly acknowledged.

Within the SyBil-AA project we address this problem by adopting a global perspective on brain network organization – the connectome – by making use of mathematical and network theoretical methods. These advanced computational tools will help us to properly describe the dynamics of brain networks in the addicted state and to compare these with healthy brains. We can then build predictive models about the effects of therapeutic interventions, and generate new hypotheses for testing in animal models and humans.

To build predictive models of ‘relapse-prone’ states of the brain connectome we will use magnetic resonance imaging (MRI). This method is widely employed in basic and clinical research to map both the functional and structural organization of the brain. Hence, MRI is principally well suited to obtain a global view on brain activity. MRI information from humans and laboratory animals is complemented by electrophysiology and neurochemical data. Among our highly interdisciplinary collaboration, leading experts on animal models of alcoholism will build an important foundation through experiments that can ascertain molecular and cellular processes in great detail. The concepts which they develop will then be validated and extended in human brain imaging and intervention studies by the most established clinical alcohol researchers in Europe. Several young teams with outstanding expertise in systems biology and network science will analyze the findings from the animal and human experiments. They will propose connectome models for the alcoholic brain as well as potential access points for guiding the network to more normal functioning. Together, we will achieve our translational goal by a theoretical and experimental framework for making predictions based on MRI an
Already during the initial period of the SyBil-AA project several high-profile publications have emanated. In a proof-of-concept study published in PNAS (Hirth et al. 2016) we demonstrated the strong benefit that can be gained from systems theoretical approaches for experimental work. Contrary to previous views suggesting low dopamine levels in the reward system as driving force for relapse behavior, we found increased levels of this neurotransmitter after a few weeks of abstinence. Thus, abstinence should not be conceptualized as a distinct state of the brain with fixed neurochemical and functional alterations. Rather complex dynamics occur, e.g. moving from a hypo- to a hyperdopaminergic state and may be back. Elucidating the causes and consequences associated with the changing dopamine levels could identify ways to understand, and thus curtail, relapse.

In two seminal papers published in PLoS Biology (Horvát et al. 2016; Noori et al. 2017), systems researchers from the SyBil-AA consortium describe a universal organizational principle of networks across brains of different sizes and mammalian orders (e.g. monkey, rat, mouse). This finding is highly important for the translation of network properties from rodents to humans and reverse. The other crucial aspect of this work was the generation of a new connectome database of the rat (ChemNetDB.org) that organizes over 50 years of neuroanatomical and neurochemical measurements from over 35.000 rats. It has significant advance over existing databases, that is ChemNetDB is currently the most comprehensive multi-scale database, it integrates neurochemical information in a consistent and validated manner. ChemNetDB is openly accessible to the research community.

Important progress has also been made in the development of new methods for our planned studies. So far, SyBil-AA researchers have published 13 articles. Many of these papers report shared work from several partners illustrating the functioning and productivity of the consortium.
The SyBil-AA team aims to closely integrate fragmented research efforts on alcoholism across leading laboratories and clinics in Europe, and to combine this with leading expertise in mathematical modelling. Rapid knowledge transfer between these specialized research groups will enable us to accomplish a goal not feasible for any one of the participants alone: to identify brain connectome alterations that can point to novel alcoholism treatments, and biomarkers that are predictive of clinical efficacy. These will be important steps towards a personalized medicine approach to better help the individual patient.
In a wider perspective, an interdisciplinary consortium such as the SyBil-AA team will also have tremendous impact on the way research is carried out in the future. A new generation of scientists will be taught to better understand the overall picture, to communicate more easily with scientists from different fields, and to apply their own expertise within such a framework.
Brain network states as neuroimaging markers in alcohol addiction – a translational approach