458 research outputs found

    "Whole-body imaging of neural and muscle activity during behavior in hydra vulgaris: effect of osmolarity on contraction bursts"

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    © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Yamamoto, W., & Yuste, R. "Whole-body imaging of neural and muscle activity during behavior in hydra vulgaris: effect of osmolarity on contraction bursts". Eneuro, (2020), doi: 10.1523/ENEURO.0539-19.2020.The neural code relates the activity of the nervous system to the activity of the muscles to the generation of behavior. To decipher it, it would be ideal to comprehensively measure the activity of the entire nervous system and musculature in a behaving animal. As a step in this direction, we used the cnidarian Hydra vulgaris to explore how physiological and environmental conditions alters simple contractile behavior and its accompany neural and muscle activity. We used whole-body calcium imaging of neurons and muscle cells and studied the effect of temperature, media osmolarity, nutritional state and body size on contractile behavior. In mounted Hydra preparations, changes in temperature, nutrition state or body size did not have a major effect on neural or muscle activity, or on contractile behavior. But changes in media osmolarity systematically altered contractile behavior and foot detachments, increasing their frequency in hypo-osmolar media solutions and decreasing it in hyperosmolar media. Similar effects were seen in ectodermal, but not in endodermal muscle. Osmolarity also bidirectionally changed the activity of contraction burst neurons, but did not affect the network of rhythmic potential neurons in the ectoderm. These findings show osmolarity-dependent changes in the activity of contraction burst neurons and ectodermal muscle, consistent with the hypothesis that contraction burst neurons respond to media hypo-osmolarity, activating ectodermal muscle to generate contraction bursts. This dedicated circuit could serve as an excretory system to prevent osmotic injury. This work demonstrates the feasibility of studying an entire neuronal and muscle activity in a behaving animal.This work was supported by the NSF (CRCNS 1822550). MBL research was supported in part by competitive fellowship funds from the H. Keffer Hartline, Edward F. MacNichol, Jr. Fellowship Fund, The E. E. Just Endowed Research Fellowship Fund, Lucy B. Lemann Fellowship Fund, and Frank R. Lillie Fellowship Fund Fellowship Fund of the Marine Biological Laboratory in Woods Hole, MA

    Imaging voltage in neurons

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    In the last decades, imaging membrane potential has become a fruitful approach to study neural circuits, especially in invertebrate preparations with large, resilient neurons. At the same time, particularly in mammalian preparations, voltage imaging methods suffer from poor signal to noise and secondary side effects, and they fall short of providing single-cell resolution when imaging of the activity of neuronal populations. As an introduction to these techniques, we briefly review different voltage imaging methods (including organic fluorophores, SHG chromophores, genetic indicators, hybrid, nanoparticles, and intrinsic approaches) and illustrate some of their applications to neuronal biophysics and mammalian circuit analysis. We discuss their mechanisms of voltage sensitivity, from reorientation, electrochromic, or electro-optical phenomena to interaction among chromophores or membrane scattering, and highlight their advantages and shortcomings, commenting on the outlook for development of novel voltage imaging methods

    Las nuevas neurotecnologías y su impacto en la ciencia, medicina y sociedad

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    El presente discurso fue leído el 19 de diciembre de 2019 en el Paraninfo de la Universidad de Zaragoza, 150 años después de que Santiago Ramón y Cajal se incorporara a las aulas de esta universidad. LOS RECIENTES AVANCES en neurotecnología e inteli- gencia artificial están permitiendo un acceso mayor y más rápido a la información acumulada en el cerebro de animales y personas. El esfuerzo científico mundial, que ha provocado la creación de la Iniciativa Internacional del Cerebro, y el desarrollo de redes neu- ronales cada vez más potentes realizado por la industria tecnoló- gica están impulsando unas nuevas neurotecnologías que podrían marcar el comienzo de una revolución en la neurociencia que nos permitirá descifrar las bases científicas de nuestras mentes y facili- tará la comprensión y la obtención de novedosos tratamientos para las enfermedades mentales y neurológicas. Pero, al mismo tiempo, estas tecnologías, combinadas con la inteligencia artificial, podrían usarse para descifrar y manipular procesos mentales y para aumen- tar cognitivamente a las personas conectándolas a las interfaces cerebro-computadora, alterando lo que significa ser humano..

    Automatic Reconstruction of Neural Morphologies with Multi-Scale Tracking

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    Neurons have complex axonal and dendritic morphologies that are the structural building blocks of neural circuits. The traditional method to capture these morphological structures using manual reconstructions is time-consuming and partly subjective, so it appears important to develop automatic or semi-automatic methods to reconstruct neurons. Here we introduce a fast algorithm for tracking neural morphologies in 3D with simultaneous detection of branching processes. The method is based on existing tracking procedures, adding the machine vision technique of multi-scaling. Starting from a seed point, our algorithm tracks axonal or dendritic arbors within a sphere of a variable radius, then moves the sphere center to the point on its surface with the shortest Dijkstra path, detects branching points on the surface of the sphere, scales it until branches are well separated and then continues tracking each branch. We evaluate the performance of our algorithm on preprocessed data stacks obtained by manual reconstructions of neural cells, corrupted with different levels of artificial noise, and unprocessed data sets, achieving 90% precision and 81% recall in branch detection. We also discuss limitations of our method, such as reconstructing highly overlapping neural processes, and suggest possible improvements. Multi-scaling techniques, well suited to detect branching structures, appear a promising strategy for automatic neuronal reconstructions
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