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; Peng Shi

doi:10.1038/s41467-018-07289-5

Nature Communications (Dec 2018)

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,
Peng Shi

Affiliations

Xudong Lin: Department of Biomedical Engineering, City University of Hong Kong
Xin Duan: Department of Biomedical Science, City University of Hong Kong
Claire Jacobs: Chemical Neurobiology Laboratory, Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School
Jeremy Ullmann: Epilepsy Genetics Program and F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Department of Neurology, Harvard Medical School
Chung-Yuen Chan: Department of Biomedical Engineering, City University of Hong Kong
Siya Chen: Department of Biomedical Engineering, City University of Hong Kong
Shuk-Han Cheng: Department of Biomedical Science, City University of Hong Kong
Wen-Ning Zhao: Chemical Neurobiology Laboratory, Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School
Annapurna Poduri: Epilepsy Genetics Program and F.M. Kirby Neurobiology Center, Boston Children’s Hospital, Department of Neurology, Harvard Medical School
Xin Wang: Department of Biomedical Science, City University of Hong Kong
Stephen J. Haggarty: Chemical Neurobiology Laboratory, Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School
Peng Shi: Department of Biomedical Engineering, City University of Hong Kong

DOI: https://doi.org/10.1038/s41467-018-07289-5
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 12

Abstract

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A major goal in neuropharmacology is to develop new tools to effectively test the therapeutic potential of pharmacological agents to treat neurological and psychiatric conditions. Here, authors present an in vivo drug screening system that generates large-scale brain activity maps to be used with machine learning to predict the therapeutic potential of clinically relevant drug leads.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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