Evidence map›Paper›PMID 29654854›Full record

ArticleJournal of theoretical biology2018

Control of clustered action potential firing in a mathematical model of entorhinal cortex stellate cells.

Luke Tait, Kyle Wedgwood, Krasimira Tsaneva-Atanasova, Jon T Brown, Marc Goodfellow

Open access · hybridAbstract read
In one paragraph

Article in Journal of theoretical biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.3field-weighted citation impact, top 47% 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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 1 country.

Luke TaitLiving Systems Institute, University of Exeter, Exeter, United Kingdom; College of Engineering, Maths, and Physical Sciences, University of Exeter, Exeter, United Kingdom. Electronic address: lt349@exeter.ac.uk.
Kyle WedgwoodLiving Systems Institute, University of Exeter, Exeter, United Kingdom; College of Engineering, Maths, and Physical Sciences, University of Exeter, Exeter, United Kingdom; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom.
Krasimira Tsaneva-AtanasovaLiving Systems Institute, University of Exeter, Exeter, United Kingdom; College of Engineering, Maths, and Physical Sciences, University of Exeter, Exeter, United Kingdom; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.
Jon T BrownUniversity of Exeter Medical School, Exeter, United Kingdom.
Marc GoodfellowLiving Systems Institute, University of Exeter, Exeter, United Kingdom; College of Engineering, Maths, and Physical Sciences, University of Exeter, Exeter, United Kingdom; Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, United Kingdom; EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, United Kingdom.
University of Exeter · GBEngineering and Physical Sciences Research Council · GB

Funding

Wellcome Trust
6 · The paper itself

Abstract

The entorhinal cortex is a crucial component of our memory and spatial navigation systems and is one of the first areas to be affected in dementias featuring tau pathology, such as Alzheimer's disease and frontotemporal dementia. Electrophysiological recordings from principle cells of medial entorhinal cortex (layer II stellate cells, mEC-SCs) demonstrate a number of key identifying properties including subthreshold oscillations in the theta (4-12 Hz) range and clustered action potential firing. These single cell properties are correlated with network activity such as grid firing and coupling between theta and gamma rhythms, suggesting they are important for spatial memory. As such, experimental models of dementia have revealed disruption of organised dorsoventral gradients in clustered action potential firing. To better understand the mechanisms underpinning these different dynamics, we study a conductance based model of mEC-SCs. We demonstrate that the model, driven by extrinsic noise, can capture quantitative differences in clustered action potential firing patterns recorded from experimental models of tau pathology and healthy animals. The differential equation formulation of our model allows us to perform numerical bifurcation analyses in order to uncover the dynamic mechanisms underlying these patterns. We show that clustered dynamics can be understood as subcritical Hopf/homoclinic bursting in a fast-slow system where the slow sub-system is governed by activation of the persistent sodium current and inactivation of the slow A-type potassium current. In the full system, we demonstrate that clustered firing arises via flip bifurcations as conductance parameters are varied. Our model analyses confirm the experimentally suggested hypothesis that the breakdown of clustered dynamics in disease occurs via increases in AHP conductance.

Indexed as

Action PotentialsGamma RhythmModels, NeurologicalAnimalsDementiaEntorhinal CortexHumansBifurcation analysisBurstingDementiaNeuron modelSubthreshold oscillations

Identifiers

PMID29654854
PMCPMC5947116
OpenAlexW2797627802

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.