Addiction Connectome Core (ACC)
ACC Led by
Benjamin Hayden, Medical School; Anna Zilverstand, Medical School; and Jan Zimmermann, Medical School.
The Addiction Connectome Core (ACC) aims to create a big data computational platform that integrates findings across many types of experimental studies to reveal the relationships between exposure to addictive drugs, brain circuits and addictive behavior.
The brain’s function is determined by its anatomical connectivity. Exposure to addictive drugs leads to compromise in brain function and those changes are mediated, in large part, by systematic changes to connectivity patterns. As such we believe that identifying the anatomical changes causes by exposure too addictive drugs is crucial for understanding the neural basis of drug addiction. Moreover, understanding the changes in brain maps causes by drug exposure will be crucial for developing treatments for addiction. The goal of the Addiction Connectome Core (ACC) is to generate and curate a database of brain connectomes in different drug exposure conditions. A critical element of our philosophy is that we must make use of animal models. Only animal models allow us to leverage state of the art advances in imaging and big data analysis approaches to obtain a detailed understanding of the effects of drugs on the brain. One consequence of this is that cross-species homology is essential for understanding connectomes. We will obtain our connectomes both internally, from our own data collection system, and externally, from a global set of participating partner institutions.
Large-scale human connectome projects (including those based at UMN) have allowed wide-ranging integration of and analysis of human neuroimaging data. However, no such resource exists for addiction-related data or for nonhuman animal models. The development and promulgation of these resources will directly aid the understanding of addiction by providing a unique platform for the development of a multimodal, translational addiction connectome that will be readily accessible to a national and international addiction research community.
Inset bar: A Connectome is a full characterization of the connectivity patterns of the whole brain of an individual. The realization of the importance of these global functional connectivity patterns for explaining disease-related behavior has spurred the investigation of “connectomics”, which has until recently focused on humans.
There is no better place to launch an Addiction Connectome Core than at UMN and, especially, at the Center for Magnetic Resonance Research (CMRR) at the University of Minnesota. The CMRR is the pre-eminent neuroimaging facility in the world, and uniquely has the MRI machines needed to collect the Core data of this proposal (specifically a full-bore 10.5T machine and 16.4 machine for mice). The CMRR is also co-lead of the Human Connectome Project, and therefore brings ten years of expertise to connectome collection and curation. The two projects, in other words, will synergize. That includes experience in making connectome data available to the world research community, and in combining cross-species measures to gain translational insights. Finally, the Minnesota Discovery Team on Addiction (MTDA), led by Dr. Mark Thomas, offers one of the strongest addiction-focused research communities in the world, meaning it will be straightforward to incorporate synergistic imaging techniques into the addiction connectome.
Addiction Connectomes in Macaques and Mice
Advances in neuroimaging have given us the ability to assess the connectivity patterns of the whole brain – in essence, its roadmap – in a relatively efficient manner. The idealized complete map of the brain’s connections – the connectome – can be collected in experimental and control conditions and compared. A connectome offers a valuable tool for examining the effects of any intervention that alters brain activity. For this reason, the connectome has proven to be an extremely valuable tool for a large number of psychiatric and neurological diseases. We propose to add addiction to this list. The addiction connectome will be a resource that can be used by anyone interested in the relationship between connectivity and chronic drug exposure.
The core dataset will involve the direct comparison of the effects of stimulants and opiates on the connectomes of mice and monkeys. We are currently leveraging the extensive neuroimaging expertise at the University of Minnesota to collect (1) diffusion-weighted scans to assess structural connectivity and (2) resting state functional MRI to assess functional connectivity from both species before and after chronic self-administration of stimulants and opiates. Our mice are be from the innovative Targeted Recombination of Active Populations (TRAP) line, which will allow us to visualize active populations of neurons and their connections. Thus, in the same animals, we are partnering with the SCC to determine which neurons are active in response to drug administration. Finally, in a subset of the same animals, we have partnered with the ICBC to collect wide field-of-view optical imaging to the effects of drug-exposure on a meso- and macro-scale. All of these structural and functional data will be combined and analyzed not just within method, but to find novel relationships across methods. Data will be linked to behavioral phenotypes describing drug-seeking behavior.
To enable efficient data processing with high-throughput data pipelines, we will establish standardized data formats as a prerequisite for data integration. We will use as our basis the data formats that have been established in the human neuroimaging field (most importantly, Brain Imaging Data Structure, BIDS). The principle for integration of the data will be the projection of a common space (Paxinos and D99 for monkeys, Allen Brain Atlas for mice) into the individual animals’ data space. This projection into a common space ensures independence of framework and modality and avoidance of unnecessary interpolation steps. It will help platform users by providing a way to morph data into any of several possible spatial reference frames. It will also allow for later upgrades, for example, with regards to expected improvements in the spatial and/or temporal resolution of data in the future. Aside from the initial data set, we will curate the integration of data from users of the Center and beyond; these data will be stored in the same format.
SERVICES COMING SOON
Computational infrastructure and multi-user web-based connectome explorer. A major barrier to the development of an addiction connectome is the current lack of a single general repository for connectivity data related to addiction. Data placed in such a repository will be in a single, standardized format through an accessible web-based platform. We will create this platform and develop such standard formats for the rodent, non-human primate, and human brain. This platform will be usable by addiction researchers around the world and will serve as a central repository for data collected elsewhere.
Benjamin Hayden, PhD. Dr. Hayden is an NIDA-fiunded Associate Professor of Neuroscience at the University of Minnesota Medical School. His research involves understanding the reward circuitry, especially from the level of single neuron responses, in both rhesus macaques and humans. He is especially interested in decision-making, including impairments in decision-making that least to failures of self-control.
Anna Zilverstand, PhD. Dr. Zilverstand is an Assistant Professor in the department of Psychiatry at the University of Minnesota Medical School. Her research focuses on neuroimaging of humans in different addicted states.
Jan Zimmermann, PhD. Dr. Zimmermann is an Assistant Professor in the department of Neuroscience at the University of Minnesota Medical School. His research involved studying reward and decision-making processes, including using methods of electrophysiology and neuroimaging. He has a special expertise in making use of ultra-high field neuroimaging techniques to obtain detailed brain maps in human and animal models.
Thomas Pengo, PhD. Dr. Pengo is the Associate Director of the UMN Informatics Institute (UMII). His role is both to provide direction for UMII and to provide image analysis services for any researcher at UMN. His background in computer engineering and he has over a decade of experience in image analysis makes him ideally suited for guiding the development and coordination of the computational platform.