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Striatal network modeling in Huntington’s Disease

Medium spiny neurons (MSNs) comprise over 90% of cells in the striatum. In vivo MSNs display coherent burst firing cell assembly activity patterns, even though isolated MSNs do not burst fire intrinsically. This activity is important for the learning and execution of action sequences and is characte...

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Autores principales: Ponzi, Adam, Barton, Scott J., Bunner, Kendra D., Rangel-Barajas, Claudia, Zhang, Emily S., Miller, Benjamin R., Rebec, George V., Kozloski, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197869/
https://www.ncbi.nlm.nih.gov/pubmed/32302302
http://dx.doi.org/10.1371/journal.pcbi.1007648
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author Ponzi, Adam
Barton, Scott J.
Bunner, Kendra D.
Rangel-Barajas, Claudia
Zhang, Emily S.
Miller, Benjamin R.
Rebec, George V.
Kozloski, James
author_facet Ponzi, Adam
Barton, Scott J.
Bunner, Kendra D.
Rangel-Barajas, Claudia
Zhang, Emily S.
Miller, Benjamin R.
Rebec, George V.
Kozloski, James
author_sort Ponzi, Adam
collection PubMed
description Medium spiny neurons (MSNs) comprise over 90% of cells in the striatum. In vivo MSNs display coherent burst firing cell assembly activity patterns, even though isolated MSNs do not burst fire intrinsically. This activity is important for the learning and execution of action sequences and is characteristically dysregulated in Huntington’s Disease (HD). However, how dysregulation is caused by the various neural pathologies affecting MSNs in HD is unknown. Previous modeling work using simple cell models has shown that cell assembly activity patterns can emerge as a result of MSN inhibitory network interactions. Here, by directly estimating MSN network model parameters from single unit spiking data, we show that a network composed of much more physiologically detailed MSNs provides an excellent quantitative fit to wild type (WT) mouse spiking data, but only when network parameters are appropriate for the striatum. We find the WT MSN network is situated in a regime close to a transition from stable to strongly fluctuating network dynamics. This regime facilitates the generation of low-dimensional slowly varying coherent activity patterns and confers high sensitivity to variations in cortical driving. By re-estimating the model on HD spiking data we discover network parameter modifications are consistent across three very different types of HD mutant mouse models (YAC128, Q175, R6/2). In striking agreement with the known pathophysiology we find feedforward excitatory drive is reduced in HD compared to WT mice, while recurrent inhibition also shows phenotype dependency. We show that these modifications shift the HD MSN network to a sub-optimal regime where higher dimensional incoherent rapidly fluctuating activity predominates. Our results provide insight into a diverse range of experimental findings in HD, including cognitive and motor symptoms, and may suggest new avenues for treatment.
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spelling pubmed-71978692020-05-12 Striatal network modeling in Huntington’s Disease Ponzi, Adam Barton, Scott J. Bunner, Kendra D. Rangel-Barajas, Claudia Zhang, Emily S. Miller, Benjamin R. Rebec, George V. Kozloski, James PLoS Comput Biol Research Article Medium spiny neurons (MSNs) comprise over 90% of cells in the striatum. In vivo MSNs display coherent burst firing cell assembly activity patterns, even though isolated MSNs do not burst fire intrinsically. This activity is important for the learning and execution of action sequences and is characteristically dysregulated in Huntington’s Disease (HD). However, how dysregulation is caused by the various neural pathologies affecting MSNs in HD is unknown. Previous modeling work using simple cell models has shown that cell assembly activity patterns can emerge as a result of MSN inhibitory network interactions. Here, by directly estimating MSN network model parameters from single unit spiking data, we show that a network composed of much more physiologically detailed MSNs provides an excellent quantitative fit to wild type (WT) mouse spiking data, but only when network parameters are appropriate for the striatum. We find the WT MSN network is situated in a regime close to a transition from stable to strongly fluctuating network dynamics. This regime facilitates the generation of low-dimensional slowly varying coherent activity patterns and confers high sensitivity to variations in cortical driving. By re-estimating the model on HD spiking data we discover network parameter modifications are consistent across three very different types of HD mutant mouse models (YAC128, Q175, R6/2). In striking agreement with the known pathophysiology we find feedforward excitatory drive is reduced in HD compared to WT mice, while recurrent inhibition also shows phenotype dependency. We show that these modifications shift the HD MSN network to a sub-optimal regime where higher dimensional incoherent rapidly fluctuating activity predominates. Our results provide insight into a diverse range of experimental findings in HD, including cognitive and motor symptoms, and may suggest new avenues for treatment. Public Library of Science 2020-04-17 /pmc/articles/PMC7197869/ /pubmed/32302302 http://dx.doi.org/10.1371/journal.pcbi.1007648 Text en © 2020 Ponzi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Ponzi, Adam
Barton, Scott J.
Bunner, Kendra D.
Rangel-Barajas, Claudia
Zhang, Emily S.
Miller, Benjamin R.
Rebec, George V.
Kozloski, James
Striatal network modeling in Huntington’s Disease
title Striatal network modeling in Huntington’s Disease
title_full Striatal network modeling in Huntington’s Disease
title_fullStr Striatal network modeling in Huntington’s Disease
title_full_unstemmed Striatal network modeling in Huntington’s Disease
title_short Striatal network modeling in Huntington’s Disease
title_sort striatal network modeling in huntington’s disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197869/
https://www.ncbi.nlm.nih.gov/pubmed/32302302
http://dx.doi.org/10.1371/journal.pcbi.1007648
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