bioRxivNeurosciencePreprint
A dopamine-dependent decrease in dorsomedial striatum direct pathway neuronal activity is required for learned motor coordination
Stefano CataldiiD, Clay LacefieldiD, N ShashaankiD, Gautam KumariD, David L. SulzeriD
Abstract
It has been suggested that the dorsomedial striatum (DMS) facilitates the early stages of motor learning for goal-directed actions, whereas at later stages, control is transferred to the dorsolateral striatum (DLS), which enables the motor actions to become a skill or habit. It is unknown whether these striatal regions are simultaneously active while expertise is acquired during skill learning. To address this question, we developed the "treadmill training task" to track changes in mouse locomotor coordination during practice at running that simultaneously provides a means to measure local neuronal activity using photometry. We analyzed body position and paw movement to evaluate changes in motor coordination over practice sessions on the treadmill using DeepLabCut and custom-built code. By correlating improvements in motor coordination during training with simultaneous neuronal calcium activity in the striatum, we found that DMS direct pathway neurons exhibited decreased activity as the mouse gains proficiency at running. In contrast, direct pathway activity in the DLS was similar throughout training and did not correlate with learned skill proficiency. Pharmacological blockade of D1 dopamine receptors in these subregions during task performance confirmed that dopamine neurotransmission in the DMS direct pathway activity is necessary for efficient motor coordination learning, while dopamine signalling in the DLS is important for both coordination learning and maintenance of the acquired skill. These results provide new tools to measure changes in fine motor skills during simultaneous recordings of brain activity, revealing fundamental features of the neuronal substrates of motor learning.
- organism: mouse
- assay: imaging
- research focus: cell type annotation
- approach: machine learning (classical)
- Citations
- 3
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