Random Forest Segregation of Drug Responses May define Regions of Biological Significance
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 t...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2016-03-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2016.00021/full |
| _version_ | 1828804777389064192 |
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| author | Qasim eBukhari David eBorsook Markus eRudin Markus eRudin Lino eBecerra |
| author_facet | Qasim eBukhari David eBorsook Markus eRudin Markus eRudin Lino eBecerra |
| author_sort | Qasim eBukhari |
| collection | DOAJ |
| description | 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 an unsupervised 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. |
| first_indexed | 2024-12-12T07:45:17Z |
| format | Article |
| id | doaj.art-a85fc806d3b847bab3e78d237f4711e1 |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-12T07:45:17Z |
| publishDate | 2016-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-a85fc806d3b847bab3e78d237f4711e12022-12-22T00:32:37ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882016-03-011010.3389/fncom.2016.00021171300Random Forest Segregation of Drug Responses May define Regions of Biological SignificanceQasim eBukhari0David eBorsook1Markus eRudin2Markus eRudin3Lino eBecerra4Institute for Biomedical Engineering, ETH Zürich and University of ZürichBoston Children's Hospital, Harvard Medical SchoolsInstitute for Biomedical Engineering, ETH Zürich and University of ZürichInstitute of Pharmacology and Toxicology, University of ZürichBoston Children's Hospital, Harvard Medical SchoolsThe 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 an unsupervised 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.http://journal.frontiersin.org/Journal/10.3389/fncom.2016.00021/fullBuprenorphinePharmacologyfMRImachine learningrandom forestphMRI |
| spellingShingle | Qasim eBukhari David eBorsook Markus eRudin Markus eRudin Lino eBecerra Random Forest Segregation of Drug Responses May define Regions of Biological Significance Frontiers in Computational Neuroscience Buprenorphine Pharmacology fMRI machine learning random forest phMRI |
| title | Random Forest Segregation of Drug Responses May define Regions of Biological Significance |
| title_full | Random Forest Segregation of Drug Responses May define Regions of Biological Significance |
| title_fullStr | Random Forest Segregation of Drug Responses May define Regions of Biological Significance |
| title_full_unstemmed | Random Forest Segregation of Drug Responses May define Regions of Biological Significance |
| title_short | Random Forest Segregation of Drug Responses May define Regions of Biological Significance |
| title_sort | random forest segregation of drug responses may define regions of biological significance |
| topic | Buprenorphine Pharmacology fMRI machine learning random forest phMRI |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2016.00021/full |
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