Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects

Peter M. Eimon; Mostafa Ghannad-Rezaie; Gianluca De Rienzo; Amin Allalou; Yuelong Wu; Mu Gao; Ambrish Roy; Jeffrey Skolnick; Mehmet Fatih Yanik

doi:10.1038/s41467-017-02404-4

Nature Communications (Jan 2018)

Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects

Peter M. Eimon,
Mostafa Ghannad-Rezaie,
Gianluca De Rienzo,
Amin Allalou,
Yuelong Wu,
Mu Gao,
Ambrish Roy,
Jeffrey Skolnick,
Mehmet Fatih Yanik

Affiliations

Peter M. Eimon: Massachusetts Institute of Technology
Mostafa Ghannad-Rezaie: Massachusetts Institute of Technology
Gianluca De Rienzo: Massachusetts Institute of Technology
Amin Allalou: Massachusetts Institute of Technology
Yuelong Wu: Massachusetts Institute of Technology
Mu Gao: Georgia Institute of Technology
Ambrish Roy: Georgia Institute of Technology
Jeffrey Skolnick: Georgia Institute of Technology
Mehmet Fatih Yanik: Massachusetts Institute of Technology

DOI: https://doi.org/10.1038/s41467-017-02404-4
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 14

Abstract

Read online

One challenge in drug screening for neurological disorders is how to accurately capture disease pathology and side effects. Here, the authors developed a multi-channel recording platform based on a zebrafish genetic model of epilepsy to screen for antiepileptic drugs.

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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