Evidence map›Paper›PMID 28417684›Full record

ReviewJournal of psychopharmacology (Oxford, England)2017

Toward a multiscale modeling framework for understanding serotonergic function.

KongFatt Wong-Lin, Da-Hui Wang, Ahmed A Moustafa, Jeremiah Y Cohen, Kae Nakamura

Abstract readReview
In one paragraph

Review in Journal of psychopharmacology (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
  5. Review
  6. Review
  7. THE EXPOSOME IN HUMAN EVOLUTION: FROM DUST TO DIESEL.The Quarterly review of biology · 2019
    Article
  8. Embracing diversity in the 5-HT neuronal system.Nature reviews. Neuroscience · 2019
    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

5 authors.

KongFatt Wong-Lin1 Intelligent Systems Research Centre, School of Computing and Intelligent Systems, University of Ulster, Magee Campus, Derry~Londonderry, UK.
Da-Hui Wang2 School of Systems Science, and National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Ahmed A Moustafa3 School of Social Sciences and Psychology, and Marcs Institute for Brain and Behaviour, University of Western Sydney, Sydney, Australia.
Jeremiah Y Cohen4 Solomon H. Snyder Department of Neuroscience, Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, USA.
Kae Nakamura5 Department of Physiology, Kansai Medical University, Hirakata, Osaka, Japan.

Funding

Biotechnology and Biological Sciences Research Council BB/P003427/1
6 · The paper itself

Abstract

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin.

Indexed as

AnimalsBrainEmotionsHumansNeuronsSerotoninSerotoninmidbrain raphe nucleusmultiscale computational modelneural circuitSerotonin 5-HT

Identifiers

PMID28417684
PMCPMC5606304

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.