No Discrete Start/Stop Signals in the Dorsal Striatum of Mice Performing a Learned Action.

A popular hypothesis is that the dorsal striatum generates discrete "traffic light" signals that initiate, maintain, and terminate the execution of learned actions. Alternatively, the striatum may continuously monitor the dynamics of movements associated with action execution by processing inputs from somatosensory and motor cortices. Here, we recorded the activity of striatal neurons in mice performing a run-and-stop task and characterized the diversity of firing rate modulations relative to run performance (tuning curves) across neurons. We found that the tuning curves could not be statistically clustered in discrete functional groups (start or stop neurons). Rather, their shape varied continuously according to the movement dynamics of the task. Moreover, striatal spiking activity correlated with running speed on a run-by-run basis and was modulated by task-related non-locomotor movements, such as licking. We hypothesize that such moment-to-moment movement monitoring by the dorsal striatum contributes to the learning of adaptive actions and/or updating their kinematics.

Segmentation of 3D images of plant tissues at multiple scales using the level set method.

BACKGROUND: Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue. RESULTS: We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue. CONCLUSION: The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.