High-throughput brain activity mapping and machine learning as a foundation for systems neuropharmacology

Xudong Lin, Xin Duan, Claire Jacobs, Jeremy Ullmann, Chung-Yuen Chan, Siya Chen, Shuk-Han Cheng, Wen-Ning Zhao, Annapurna Poduri, Xin Wang (), Stephen J. Haggarty () and Peng Shi ()
Additional contact information
Xudong Lin: City University of Hong Kong
Xin Duan: City University of Hong Kong
Claire Jacobs: Department of Neurology, Harvard Medical School
Jeremy Ullmann: Department of Neurology, Harvard Medical School
Chung-Yuen Chan: City University of Hong Kong
Siya Chen: City University of Hong Kong
Shuk-Han Cheng: City University of Hong Kong
Wen-Ning Zhao: Department of Neurology, Harvard Medical School
Annapurna Poduri: Department of Neurology, Harvard Medical School
Xin Wang: City University of Hong Kong
Stephen J. Haggarty: Department of Neurology, Harvard Medical School
Peng Shi: City University of Hong Kong

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: Abstract 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.

Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-018-07289-5 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07289-5

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-018-07289-5

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().