Evidence map›Paper›PMID 33093823›Full record

ArticleFrontiers in cellular neuroscience2020

Integration of Within-Cell Experimental Data With Multi-Compartmental Modeling Predicts H-Channel Densities and Distributions in Hippocampal OLM Cells.

Vladislav Sekulić, Feng Yi, Tavita Garrett, Alexandre Guet-McCreight, J Josh Lawrence, Frances K Skinner

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cellular neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 23% 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

5 citing papers in PubMed, 13 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

6 authors at 5 institutions in 2 countries.

Vladislav SekulićKrembil Research Institute, University Health Network, Toronto, ON, Canada.
Feng YiDepartment of Biomedical and Pharmaceutical Sciences, Center for Biomolecular Structure and Dynamics, Center for Structural and Functional Neuroscience, University of Montana, Missoula, MT, United States.
Tavita GarrettNeuroscience Graduate Program and Vollum Institute, Oregon Health & Science University, Portland, OR, United States.
Alexandre Guet-McCreightKrembil Research Institute, University Health Network, Toronto, ON, Canada.
J Josh LawrenceDepartment of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
Frances K SkinnerKrembil Research Institute, University Health Network, Toronto, ON, Canada.
Krembil Research InstituteTexas Tech University Health Sciences Center · USUniversity of Montana · USUniversity of Toronto · CAVollum Institute · US

Funding

THE ROLE OF PULMONARY SUBPOPULATIONS AND TH2 IMMUNITY IN BALB/C SILICOSIS MODELP20RR017670 · NCRR · UNIVERSITY OF MONTANA · PI ROBERTS, KEVAN · 2002 to 2011
$22.0M
Understanding the molecular mechanism of pathogenesis of prion diseasesP20RR015583 · NCRR · UNIVERSITY OF MONTANA · PI KAVANAUGH, MICHAEL PATRICK · 2000 to 2009
$17.6M
Differential modes of cholinergic transmission onto cellular hippocampal targetsR01NS069689 · NINDS · UNIVERSITY OF MONTANA · PI LAWRENCE, JOHN JOSHUA · 2010 to 2014
$1.5M
NCRR NIH HHS P20 RR015583NCRR NIH HHS P20 RR017670NINDS NIH HHS R01 NS069689
6 · The paper itself

Abstract

Determining biophysical details of spatially extended neurons is a challenge that needs to be overcome if we are to understand the dynamics of brain function from cellular perspectives. Moreover, we now know that we should not average across recordings from many cells of a given cell type to obtain quantitative measures such as conductance since measures can vary multiple-fold for a given cell type. In this work we examine whether a tight combination of experimental and computational work can address this challenge. The oriens-lacunosum/moleculare (OLM) interneuron operates as a "gate" that controls incoming sensory and ongoing contextual information in the CA1 of the hippocampus, making it essential to understand how its biophysical properties contribute to memory function. OLM cells fire phase-locked to the prominent hippocampal theta rhythms, and we previously used computational models to show that OLM cells exhibit high or low theta spiking resonance frequencies that depend respectively on whether their dendrites have hyperpolarization-activated cation channels (h-channels) or not. However, whether OLM cells actually possess dendritic h-channels is unknown at present. We performed a set of whole-cell recordings of OLM cells from mouse hippocampus and constructed three multi-compartment models using morphological and electrophysiological parameters extracted from the same OLM cell, including per-cell pharmacologically isolated h-channel currents. We found that the models best matched experiments when h-channels were present in the dendrites of each of the three model cells created. This strongly suggests that h-channels must be present in OLM cell dendrites and are not localized to their somata. Importantly, this work shows that a tight integration of model and experiment can help tackle the challenge of characterizing biophysical details and distributions in spatially extended neurons. Full spiking models were built for two of the OLM cells, matching their current clamp cell-specific electrophysiological recordings. Overall, our work presents a technical advancement in modeling OLM cells. Our models are available to the community to use to gain insight into cellular dynamics underlying hippocampal function.

Indexed as

dendriteelectrophysiologyh-channelshippocampusinhibitory cellinterneuronmulti-compartment model

Identifiers

PMID33093823
PMCPMC7527636
OpenAlexW3087499178

What OpenQuestion holds

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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.