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dc.contributor.author
Bukhari, Qasim
dc.contributor.author
Borsook, David
dc.contributor.author
Rudin, Markus
dc.contributor.author
Becerra, Lino
dc.date.accessioned
2019-06-27T15:17:47Z
dc.date.available
2017-06-12T03:01:40Z
dc.date.available
2019-06-27T15:17:47Z
dc.date.issued
2016-03
dc.identifier.issn
1662-5188
dc.identifier.other
10.3389/fncom.2016.00021
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/114424
dc.identifier.doi
10.3929/ethz-b-000114424
dc.description.abstract
The ability to assess brain responses in unsupervised manner based on fMRI measure has remained a challenge. Here we have applied the Random Forest (RF) method to detect differences in the pharmacological MRI (phMRI) response in rats to treatment with an analgesic drug (buprenorphine) as compared to control (saline). Three groups of animals were studied: two groups treated with different doses of the opioid buprenorphine, low (LD), and high dose (HD), and one receiving saline. PhMRI responses were evaluated in 45 brain regions and RF analysis was applied to allocate rats to the individual treatment groups. RF analysis was able to identify drug effects based on differential phMRI responses in the hippocampus, amygdala, nucleus accumbens, superior colliculus, and the lateral and posterior thalamus for drug vs. saline. These structures have high levels of mu opioid receptors. In addition these regions are involved in aversive signaling, which is inhibited by mu opioids. The results demonstrate that buprenorphine mediated phMRI responses comprise characteristic features that allow a supervised differentiation from placebo treated rats as well as the proper allocation to the respective drug dose group using the RF method, a method that has been successfully applied in clinical studies.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Frontiers Research Foundation
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
fMRI
en_US
dc.subject
random forest
en_US
dc.subject
machine learning
en_US
dc.subject
phMRI
en_US
dc.subject
pharmacology
en_US
dc.subject
buprenorphine
en_US
dc.title
Random Forest Segregation of Drug Responses May Define Regions of Biological Significance
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2016-03-09
ethz.journal.title
Frontiers in Computational Neuroscience
ethz.journal.volume
10
en_US
ethz.journal.abbreviated
Front. comput. neurosci.
ethz.pages.start
21
en_US
ethz.size
10 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.wos
ethz.identifier.nebis
010194231
ethz.publication.place
Lausanne
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.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03750 - Rudin, Markus (emeritus)
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.::02631 - Institut für Biomedizinische Technik / Institute for Biomedical Engineering::03750 - Rudin, Markus (emeritus)
ethz.date.deposited
2017-06-12T03:02:24Z
ethz.source
ECIT
ethz.identifier.importid
imp5936543e3ce0f24994
ethz.ecitpid
pub:176208
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-24T08:57:25Z
ethz.rosetta.lastUpdated
2019-06-27T15:17:57Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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