Addressing striatal plasticity at the single synapse level, upon motor learning

Learning to execute and automatize motor skills is essential in our everyday life, involving the orchestrated activity of several interconnected brain regions to optimize desired movements by trial and error. This learning process is present throughout our lifetime, enabling basic motor skills such as walking, or riding a bicycle and is also important in rehabilitation from injuries that affect the motor system (e.g. amputations, stroke). However, the mechanisms responsible for motor learning are still not well understood.

In this project, we are exploring the role of a particular neuronal connection in the process of motor learning: the contacts between cortical neurons and the medium spiny neurons (MSNs) of the striatum. This brain region is part of the basal ganglia, which are known to play a crucial role in motor learning and execution. Corticostriatal connections, also termed synapses, are good candidates to affect motor learning as they receive “executive” information in the form of the neurotransmitter lutamate, released from cortical presynaptic terminals, and dopamine modulatory signals transmitted by midbrain regions involved in reward and learning. In fact, corticostriatal synapses were demonstrated to undergo functional changes upon motor learning,
while interfering with activity at these synapses impairs the learning of action sequences.

Our goal is to determine the nature of the changes induced by motor learning on corticostriatal synapses. Therefore, we will address if increases in synaptic strength upon motor learning reflect an increase in the number of synapses, the functional potentiation of existing ones, or even rearrangements in the spatial pattern of high-efficiency synapses. Functional and spatial pattern changes at pre-existing synapses would alter their relative weight, synaptic efficacy, and ability to affect whole neuron activity, while the formation of new contacts with new presynaptic partners, could increase the number of potential combinations that circuits can form to encode a learned skill. We will use a novel lateralized motor learning task recently developed in our laboratory, together with state-of-the-art imaging and electrophysiological techniques, to study synaptic plasticity at the single synapse level upon motor learning. This project will enable us to understand how MSN input computation is modified to establish long-lasting motor skills and potentially impact the understanding and treatment of movement disorders.

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