À PROPOS DE CET ÉPISODE
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.04.22.019661v1?rss=1
Authors: Allegra Mascaro, A. L., Falotico, E., Petkoski, S., Pasquini, M., Vannucci, L., Tort-Colet, N., Conti, E., Resta, F., Spalletti, C., Ramalingasetty, S. T., von Arnim, A., Formento, E., Angelidis, E., Blixhavn, C. H., Leergaard, T. B., Caleo, M., Destexhe, A., Ijspeert, A., Micera, S., Laschi, C., Jirsa, V., Gewaltig, M. O., Pavone, F.
Abstract:
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
Copy rights belong to original authors. Visit the link for more info
http://biorxiv.org/cgi/content/short/2020.04.22.019661v1?rss=1
Authors: Allegra Mascaro, A. L., Falotico, E., Petkoski, S., Pasquini, M., Vannucci, L., Tort-Colet, N., Conti, E., Resta, F., Spalletti, C., Ramalingasetty, S. T., von Arnim, A., Formento, E., Angelidis, E., Blixhavn, C. H., Leergaard, T. B., Caleo, M., Destexhe, A., Ijspeert, A., Micera, S., Laschi, C., Jirsa, V., Gewaltig, M. O., Pavone, F.
Abstract:
Being able to replicate real experiments with computational simulations is a unique opportunity to refine and validate models with experimental data and redesign the experiments based on simulations. However, since it is technically demanding to model all components of an experiment, traditional approaches to modeling reduce the experimental setups as much as possible. In this study, our goal is to replicate all the relevant features of an experiment on motor control and motor rehabilitation after stroke. To this aim, we propose an approach that allows continuous integration of new experimental data into a computational modeling framework. First, results show that we could reproduce experimental object displacement with high accuracy via the simulated embodiment in the virtual world by feeding a spinal cord model with experimental registration of the cortical activity. Second, by using computational models of multiple granularities, our preliminary results show the possibility of simulating several features of the brain after stroke, from the local alteration in neuronal activity to long-range connectivity remodeling. Finally, strategies are proposed to merge the two pipelines. We further suggest that additional models could be integrated into the framework thanks to the versatility of the proposed approach, thus allowing many researchers to achieve continuously improved experimental design.
Copy rights belong to original authors. Visit the link for more info
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.04.22.056432v1?rss=1
Authors: Yamashita, A., Sakai, Y., Yamada, T., Yahata, N., Kunimatsu, A., Okada, N., Itahashi, T., Hashimoto, R.-i., Mizuta, H., Ichikawa, N., Takamura, M., Okada, G., Yamagata, H., Harada, K., Matsuo, K., Tanak…
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.04.22.056481v1?rss=1
Authors: Chen, P.-C., Whitehurst, L. N., Naji, M., Mednick, S. C.
Abstract:
Working memory (WM) is an executive function that can improve with training. However, the precise mechanism for this improvement is n…
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.04.22.054064v1?rss=1
Authors: Matho, K. S. H., Huilgol, D., Galbavy, W., Kim, G., He, M., An, X., Lu, J., Wu, P., Di Bella, D. J., Shetty, A. S., Palaniswamy, R., Hatfield, J., Raudales, R., Narasimhan, A., Gamache, E., Levine, J. M…
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.04.22.055731v1?rss=1
Authors: Roth, J. G., Muench, K. L., Asokan, A., Mallett, V. M., Gai, H., Verma, Y., Weber, S., Charlton, C., Fowler, J. L., Loh, K. M., Dolmetsch, R. E., Palmer, T. D.
Abstract:
Microdeletions and microduplic…
Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.04.22.055228v1?rss=1
Authors: Hollinger, K. R., Sharma, A., Tallon, C., Lovell, L., Thomas, A. G., Zhu, X., Kambhampati, S. P., Liaw, K., Sharma, R., Rojas, C., Rais, R., Kannan, S., Kannan, R. M., Slusher, B.
Abstract:
Roughly ha…
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