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dc.contributor.author
Honegger, Thibault
dc.contributor.author
Thielen, Moritz I.
dc.contributor.author
Feizi, Soheil
dc.contributor.author
Sanjana, Neville E.
dc.contributor.author
Voldman, Joel
dc.date.accessioned
2018-10-19T09:18:03Z
dc.date.available
2017-06-12T19:22:09Z
dc.date.available
2018-10-19T09:18:03Z
dc.date.issued
2016-06-22
dc.identifier.other
10.1038/srep28384
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/127658
dc.identifier.doi
10.3929/ethz-b-000127658
dc.description.abstract
The central nervous system is a dense, layered, 3D interconnected network of populations of neurons, and thus recapitulating that complexity for in vitro CNS models requires methods that can create defined topologically-complex neuronal networks. Several three-dimensional patterning approaches have been developed but none have demonstrated the ability to control the connections between populations of neurons. Here we report a method using AC electrokinetic forces that can guide, accelerate, slow down and push up neurites in un-modified collagen scaffolds. We present a means to create in vitro neural networks of arbitrary complexity by using such forces to create 3D intersections of primary neuronal populations that are plated in a 2D plane. We report for the first time in vitro basic brain motifs that have been previously observed in vivo and show that their functional network is highly decorrelated to their structure. This platform can provide building blocks to reproduce in vitro the complexity of neural circuits and provide a minimalistic environment to study the structure-function relationship of the brain circuitry.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Nature Publishing Group
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
Electrical and electronic engineering
en_US
dc.subject
Nanoscience and technology
en_US
dc.subject
Neural circuits
en_US
dc.title
Microfluidic neurite guidance to study structure-function relationships in topologically complex population-based neural networks
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
Scientific Reports
ethz.journal.volume
6
en_US
ethz.pages.start
28384
en_US
ethz.size
10 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.identifier.nebis
006751867
ethz.publication.place
London
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::03609 - Hierold, Christofer / Hierold, Christofer
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02130 - Dep. Maschinenbau und Verfahrenstechnik / Dep. of Mechanical and Process Eng.::03609 - Hierold, Christofer / Hierold, Christofer
ethz.date.deposited
2017-06-12T19:22:14Z
ethz.source
ECIT
ethz.identifier.importid
imp593655331475d71042
ethz.ecitpid
pub:190519
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T22:01:14Z
ethz.rosetta.lastUpdated
2019-01-02T14:51:12Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.atitle=Microfluidic%20neurite%20guidance%20to%20study%20structure-function%20relationships%20in%20topologically%20complex%20population-based%20neural%20networks&rft.jtitle=Scientific%20Reports&rft.date=2016-06-22&rft.volume=6&rft.spage=28384&rft.au=Honegger,%20Thibault&Thielen,%20Moritz%20I.&Feizi,%20Soheil&Sanjana,%20Neville%20E.&Voldman,%20Joel&rft.genre=article&
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