Neuroscience
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Detecting morphological changes of dendritic spines in time-lapse microscopy images and correlating them with functional properties such as memory and learning, are fundamental and challenging problems in neurobiology research. In this paper, we propose an algorithm for dendritic spine detection in time series. The proposed approach initially performs spine detection at each time point and improves the accuracy by exploiting the information obtained from tracking of individual spines over time. ⋯ Finally, we determine the spine location more precisely by performing a watershed-geodesic active contour model. We quantitatively assess the performance of the proposed spine detection algorithm based on annotations performed by biologists and compare its performance with the results obtained by the noncommercial software NeuronIQ. Experiments show that our approach can accurately detect and quantify spines in 2-photon microscopy time-lapse data and is able to accurately identify spine elimination and formation.
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Locomotor patterns are mainly modulated by afferent feedback, but its actual contribution to spinal network activity during continuous passive limb training is still unexplored. To unveil this issue, we devised a robotic in vitro setup (Bipedal Induced Kinetic Exercise, BIKE) to induce passive pedaling, while simultaneously recording low-noise ventral and dorsal root (VR and DR) potentials in isolated neonatal rat spinal cords with hindlimbs attached. As a result, BIKE evoked rhythmic afferent volleys from DRs, reminiscent of pedaling speed. ⋯ Patch clamp recordings from single motoneurons after 90-min sessions indicated an increased frequency of both fast- and slow-decaying synaptic input to motoneurons. In conclusion, hindlimb rhythmic and alternated pedaling for different durations affects distinct dorsal and ventral spinal networks by modulating excitatory and inhibitory input to motoneurons. These results suggest defining new parameters for effective neurorehabilitation that better exploits spinal circuit activity.