Random Forest Segregation of Drug Responses May Define Regions of Biological Significance
Open access
Date
2016-03Type
- Journal Article
ETH Bibliography
yes
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000114424Publication status
publishedExternal links
Journal / series
Frontiers in Computational NeuroscienceVolume
Pages / Article No.
Publisher
Frontiers MediaSubject
fMRI; random forest; machine learning; phMRI; pharmacology; buprenorphineOrganisational unit
03750 - Rudin, Markus (emeritus)
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ETH Bibliography
yes
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