The role of different midbrain dopamine neuron populations in signalling reward and cost

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The brain must learn to use environmental cues to predict whether an action will have positive or negative consequences and the associated cost (e.g. effort) of performing the action. This type of learning involves the chemical messenger dopamine and there are good theoretical models that explain how the nerve impulses generated by dopamine-releasing cells might signal reward. However, despite the elegant simplicity of these models, at the cellular level there is greater complexity. For example, dopamine cells seem to consist of several populations which convey different aspects of the reward signal. Therefore, to better understand reward processes and when these go awry (e.g. in addiction), we must first understand how different dopamine-cell populations signal different parts of the reward signal and to which parts of the brain these signals are sent. To do this, we will divide populations of dopamine cells according to the brain regions that they innervate and use the combination of different molecules present in each population as a kind of barcode to identify them. We will then use newly-developed, powerful techniques to record nerve impulses from individual dopamine cells in mice during a reward-task and label each recorded neuron. We will investigate how different populations of dopamine cells signal 1) positive and negative consequences and 2) the amount of effort required to obtain reward. We will use each labelled dopamine cell's 'barcode' to tell us where in the brain the signals were sent. To further test the role of different dopamine-cell populations, we will use cutting-edge technologies to measure dopamine released in a particular brain-region and then switch-off one of the dopamine-cell populations. We will also use computer simulations to help us interpret how the dopamine nerve-impulses translate into dopamine release in different regions of the brain. This research will help us to better understand some of the complexity of reward-related signalling and enhance our theoretical models of learning. We will define populations of dopamine cells and reveal which brain regions receive different components of the reward signal. The 'barcodes', data, and computer models we generate will enable us and other researchers to build a better picture of reward learning and understand how it goes wrong in brain disorders.