Evidence map›Paper›PMID 27113252›Full record

ReviewCurrent neurology and neuroscience reports2016

Advances of Molecular Imaging in Epilepsy.

Marian Galovic, Matthias Koepp

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current neurology and neuroscience reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.8field-weighted citation impact, top 17% of its field
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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. European journal of hybrid imaging · 2020
    Article
  8. Companion animal models of neurological disease.Journal of neuroscience methods · 2020
    Review
  9. Article
  10. Review
  11. Review
  12. Review
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

2 authors at 1 institution in 1 country.

Marian GalovicDepartment of Clinical and Experimental Epilepsy, UCL Institute of Neurology, 33 Queen Square, London, WC1N 3BG, United Kingdom.
Matthias KoeppDepartment of Clinical and Experimental Epilepsy, UCL Institute of Neurology, 33 Queen Square, London, WC1N 3BG, United Kingdom. m.koepp@ucl.ac.uk.
National Hospital for Neurology and Neurosurgery · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Positron emission tomography (PET) is a neuroimaging method that offers insights into the molecular functioning of a human brain. It has been widely used to study metabolic and neurotransmitter abnormalities in people with epilepsy. This article reviews the development of several PET radioligands and their application in studying the molecular mechanisms of epilepsy. Over the last decade, tracers binding to serotonin and γ-aminobutyric acid (GABA) receptors have been used to delineate the location of the epileptic focus. PET studies have examined the role of opioids, cannabinoids, acetylcholine, and dopamine in modulating neuronal hyperexcitability and seizure termination. In vivo analyses of drug transporters, e.g., P-glycoprotein, have increased our understanding of pharmacoresistance that could inform new therapeutic strategies. Finally, PET experiments targeting neuroinflammation and glutamate receptors might guide the development of novel biomarkers of epileptogenesis.

Indexed as

Molecular ImagingAnimalsBrainEpilepsyHumansNeuroimagingSeizuresEpilepsyNeuroimagingPositron-emission tomographySeizures

Identifiers

PMID27113252
PMCPMC4844640
OpenAlexW2343925121

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.