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High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology
Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherapeutics for neurological and psychiatric disorders....
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277389/ https://www.ncbi.nlm.nih.gov/pubmed/30510233 http://dx.doi.org/10.1038/s41467-018-07289-5 |
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author | Lin, Xudong Duan, Xin Jacobs, Claire Ullmann, Jeremy Chan, Chung-Yuen Chen, Siya Cheng, Shuk-Han Zhao, Wen-Ning Poduri, Annapurna Wang, Xin Haggarty, Stephen J. Shi, Peng |
author_facet | Lin, Xudong Duan, Xin Jacobs, Claire Ullmann, Jeremy Chan, Chung-Yuen Chen, Siya Cheng, Shuk-Han Zhao, Wen-Ning Poduri, Annapurna Wang, Xin Haggarty, Stephen J. Shi, Peng |
author_sort | Lin, Xudong |
collection | PubMed |
description | Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherapeutics for neurological and psychiatric disorders. Here, we describe an in vivo drug screening strategy that combines high-throughput technology to generate large-scale brain activity maps (BAMs) with machine learning for predictive analysis. This platform enables evaluation of compounds’ mechanisms of action and potential therapeutic uses based on information-rich BAMs derived from drug-treated zebrafish larvae. From a screen of clinically used drugs, we found intrinsically coherent drug clusters that are associated with known therapeutic categories. Using BAM-based clusters as a functional classifier, we identify anti-seizure-like drug leads from non-clinical compounds and validate their therapeutic effects in the pentylenetetrazole zebrafish seizure model. Collectively, this study provides a framework to advance the field of systems neuropharmacology. |
format | Online Article Text |
id | pubmed-6277389 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-62773892018-12-05 High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology Lin, Xudong Duan, Xin Jacobs, Claire Ullmann, Jeremy Chan, Chung-Yuen Chen, Siya Cheng, Shuk-Han Zhao, Wen-Ning Poduri, Annapurna Wang, Xin Haggarty, Stephen J. Shi, Peng Nat Commun Article Technologies for mapping the spatial and temporal patterns of neural activity have advanced our understanding of brain function in both health and disease. An important application of these technologies is the discovery of next-generation neurotherapeutics for neurological and psychiatric disorders. Here, we describe an in vivo drug screening strategy that combines high-throughput technology to generate large-scale brain activity maps (BAMs) with machine learning for predictive analysis. This platform enables evaluation of compounds’ mechanisms of action and potential therapeutic uses based on information-rich BAMs derived from drug-treated zebrafish larvae. From a screen of clinically used drugs, we found intrinsically coherent drug clusters that are associated with known therapeutic categories. Using BAM-based clusters as a functional classifier, we identify anti-seizure-like drug leads from non-clinical compounds and validate their therapeutic effects in the pentylenetetrazole zebrafish seizure model. Collectively, this study provides a framework to advance the field of systems neuropharmacology. Nature Publishing Group UK 2018-12-03 /pmc/articles/PMC6277389/ /pubmed/30510233 http://dx.doi.org/10.1038/s41467-018-07289-5 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lin, Xudong Duan, Xin Jacobs, Claire Ullmann, Jeremy Chan, Chung-Yuen Chen, Siya Cheng, Shuk-Han Zhao, Wen-Ning Poduri, Annapurna Wang, Xin Haggarty, Stephen J. Shi, Peng High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology |
title | High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology |
title_full | High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology |
title_fullStr | High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology |
title_full_unstemmed | High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology |
title_short | High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology |
title_sort | high-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6277389/ https://www.ncbi.nlm.nih.gov/pubmed/30510233 http://dx.doi.org/10.1038/s41467-018-07289-5 |
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