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dc.contributor.author
Eimon, Peter M.
dc.contributor.author
Ghannad-Rezaie, Mostafa
dc.contributor.author
De Rienzo, Gianluca
dc.contributor.author
Allalou, Amin
dc.contributor.author
Wu, Yuelong
dc.contributor.author
Gao, Mu
dc.contributor.author
Roy, Ambrish
dc.contributor.author
Skolnick, Jeffrey
dc.contributor.author
Yanik, Mehmet F.
dc.date.accessioned
2018-01-17T09:15:53Z
dc.date.available
2018-01-17T08:51:53Z
dc.date.available
2018-01-17T09:15:53Z
dc.date.issued
2018
dc.identifier.other
10.1038/s41467-017-02404-4
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/229893
dc.identifier.doi
10.3929/ethz-b-000229893
dc.description.abstract
Neurological drugs are often associated with serious side effects, yet drug screens typically focus only on efficacy. We demonstrate a novel paradigm utilizing high-throughput in vivo electrophysiology and brain activity patterns (BAPs). A platform with high sensitivity records local field potentials (LFPs) simultaneously from many zebrafish larvae over extended periods. We show that BAPs from larvae experiencing epileptic seizures or drug-induced side effects have substantially reduced complexity (entropy), similar to reduced LFP complexity observed in Parkinson’s disease. To determine whether drugs that enhance BAP complexity produces positive outcomes, we used light pulses to trigger seizures in a model of Dravet syndrome, an intractable genetic epilepsy. The highest-ranked compounds identified by BAP analysis exhibit far greater anti-seizure efficacy and fewer side effects during subsequent in-depth behavioral assessment. This high correlation with behavioral outcomes illustrates the power of brain activity pattern-based screens and identifies novel therapeutic candidates with minimal side effects.
en_US
dc.language.iso
en
en_US
dc.publisher
Nature
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Epilepsy
en_US
dc.subject
High-throughput screening
en_US
dc.subject
Molecular neuroscience
en_US
dc.title
Brain activity patterns in high-throughput electrophysiology screen predict both drug efficacies and side effects
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2018-01-15
ethz.journal.title
Nature Communications
ethz.journal.volume
9
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
Nat Commun
ethz.pages.start
219
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02533 - Institut für Neuroinformatik / Institute of Neuroinformatics::09474 - Yanik, Mehmet Fatih / Yanik, Mehmet Fatih
ethz.date.deposited
2018-01-17T08:51:53Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2018-01-17T09:15:58Z
ethz.rosetta.lastUpdated
2019-02-02T14:07:53Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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