Evidence map›Paper›PMID 29432411›Full record

ArticlePLoS computational biology2018

Electrical receptive fields of retinal ganglion cells: Influence of presynaptic neurons.

Matias I Maturana, Nicholas V Apollo, David J Garrett, Tatiana Kameneva, Shaun L Cloherty, David B Grayden, Anthony N Burkitt, Michael R Ibbotson, Hamish Meffin

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.1field-weighted citation impact, top 28% 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

6 citing papers in PubMed, 19 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Matias I MaturanaNational Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia.ORCID 0000-0003-1432-8835
Nicholas V ApolloSchool of Physics, The University of Melbourne, Parkville, VIC, Australia.
David J GarrettSchool of Physics, The University of Melbourne, Parkville, VIC, Australia.
Tatiana KamenevaNeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Australia.ORCID 0000-0003-0081-8569
Shaun L ClohertyNational Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia.ORCID 0000-0002-7679-1764
David B GraydenDepartment of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, VIC, Australia.ORCID 0000-0002-5497-7234
Anthony N BurkittNeuroEngineering Laboratory, Department of Biomedical Engineering, The University of Melbourne, Australia.ORCID 0000-0001-5672-2772
Michael R IbbotsonNational Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia.ORCID 0000-0002-3803-6653
Hamish MeffinNational Vision Research Institute, Australian College of Optometry, Carlton, Victoria, Australia.ORCID 0000-0003-4307-6841
The University of Melbourne · AUAustralian College of Optometry · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Implantable retinal stimulators activate surviving neurons to restore a sense of vision in people who have lost their photoreceptors through degenerative diseases. Complex spatial and temporal interactions occur in the retina during multi-electrode stimulation. Due to these complexities, most existing implants activate only a few electrodes at a time, limiting the repertoire of available stimulation patterns. Measuring the spatiotemporal interactions between electrodes and retinal cells, and incorporating them into a model may lead to improved stimulation algorithms that exploit the interactions. Here, we present a computational model that accurately predicts both the spatial and temporal nonlinear interactions of multi-electrode stimulation of rat retinal ganglion cells (RGCs). The model was verified using in vitro recordings of ON, OFF, and ON-OFF RGCs in response to subretinal multi-electrode stimulation with biphasic pulses at three stimulation frequencies (10, 20, 30 Hz). The model gives an estimate of each cell's spatiotemporal electrical receptive fields (ERFs); i.e., the pattern of stimulation leading to excitation or suppression in the neuron. All cells had excitatory ERFs and many also had suppressive sub-regions of their ERFs. We show that the nonlinearities in observed responses arise largely from activation of presynaptic interneurons. When synaptic transmission was blocked, the number of sub-regions of the ERF was reduced, usually to a single excitatory ERF. This suggests that direct cell activation can be modeled accurately by a one-dimensional model with linear interactions between electrodes, whereas indirect stimulation due to summated presynaptic responses is nonlinear.

Indexed as

Computer SimulationAction PotentialsAlgorithmsAnimalsElectric StimulationElectrodesLightModels, NeurologicalNeuronsPresynaptic TerminalsRatsReproducibility of ResultsRetinaRetinal Ganglion CellsSignal-To-Noise RatioSoftware

Identifiers

PMID29432411
PMCPMC5825175
OpenAlexW2785572299

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.