Format

Send to

Choose Destination
Nat Commun. 2018 Dec 3;9(1):5142. doi: 10.1038/s41467-018-07289-5.

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

Author information

1
Department of Biomedical Engineering, City University of Hong Kong, 999077, Kowloon, Hong Kong SAR, China.
2
Department of Biomedical Science, City University of Hong Kong, 999077, Kowloon, Hong Kong SAR, China.
3
Chemical Neurobiology Laboratory, Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA, 02114, USA.
4
Epilepsy Genetics Program and F.M. Kirby Neurobiology Center, Boston Children's Hospital, Department of Neurology, Harvard Medical School, Boston, MA, 02115, USA.
5
Department of Biomedical Science, City University of Hong Kong, 999077, Kowloon, Hong Kong SAR, China. xin.wang@cityu.edu.hk.
6
Shenzhen Research Institute, City University of Hong Kong, 518057, Shenzhen, China. xin.wang@cityu.edu.hk.
7
Chemical Neurobiology Laboratory, Center for Genomic Medicine, Massachusetts General Hospital, Department of Neurology, Harvard Medical School, Boston, MA, 02114, USA. shaggarty@mgh.harvard.edu.
8
Department of Biomedical Engineering, City University of Hong Kong, 999077, Kowloon, Hong Kong SAR, China. pengshi@cityu.edu.hk.
9
Shenzhen Research Institute, City University of Hong Kong, 518057, Shenzhen, China. pengshi@cityu.edu.hk.

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.

PMID:
30510233
PMCID:
PMC6277389
DOI:
10.1038/s41467-018-07289-5
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Nature Publishing Group Icon for PubMed Central
Loading ...
Support Center