Evidence map›Paper›PMID 27014046›Full record

ArticleFrontiers in computational neuroscience2016

Random Forest Segregation of Drug Responses May Define Regions of Biological Significance.

Qasim Bukhari, David Borsook, Markus Rudin, Lino Becerra

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Data-Driven Prediction of Fatigue in Parkinson's Disease Patients.Frontiers in artificial intelligence · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Qasim BukhariInstitute for Biomedical Engineering, ETH Zürich and University of Zürich Zürich, Switzerland.
David BorsookPain and Analgesia Imaging Neuroscience Group, Departments of Anesthesia, Perioperative and Pain Medicine, Boston Children's HospitalWaltham, MA, USA; Department of Radiology, Boston Children's HospitalWaltham, MA, USA.
Markus RudinInstitute for Biomedical Engineering, ETH Zürich and University of ZürichZürich, Switzerland; Institute of Pharmacology and Toxicology, University of ZürichZürich, Switzerland.
Lino BecerraPain and Analgesia Imaging Neuroscience Group, Departments of Anesthesia, Perioperative and Pain Medicine, Boston Children's HospitalWaltham, MA, USA; Department of Radiology, Boston Children's HospitalWaltham, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

buprenorphinefMRImachine learningpharmacologyphMRIrandom forest

Identifiers

PMID27014046
PMCPMC4783407

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.