Evidence map›Paper›PMID 28993204›Full record

ArticleJournal of neuroscience methods2018

A stepwise neuron model fitting procedure designed for recordings with high spatial resolution: Application to layer 5 pyramidal cells.

Tuomo Mäki-Marttunen, Geir Halnes, Anna Devor, Christoph Metzner, Anders M Dale, Ole A Andreassen, Gaute T Einevoll

Abstract readValidation Study
In one paragraph

Article in Journal of neuroscience methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Article
  2. Pre- and Post-synaptic Mechanisms of Neuronal Inhibition Assessed Through Biochemically Detailed Modeling of GABAThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
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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

7 authors.

Tuomo Mäki-MarttunenNORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Simula Research Laboratory, Lysaker, Norway. Electronic address: tuomo@simula.no.
Geir HalnesFaculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway.
Anna DevorDepartment of Neurosciences, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Christoph MetznerBiocomputation Research Group, University of Hertfordshire, Hatfield, UK.
Anders M DaleDepartment of Neurosciences, University of California San Diego, La Jolla, CA, USA; Department of Radiology, University of California, San Diego, La Jolla, CA, USA.
Ole A AndreassenNORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway.
Gaute T EinevollFaculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway; Department of Physics, University of Oslo, Oslo, Norway.

Funding

Spatiotemporal Brain Imaging: Microscopic &System LevelR01EB000790 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DALE, ANDERS M · 2002 to 2015
$15.3M
MICROSCOPIC FOUNDATION OF MULTIMODAL HUMAN IMAGINGR01MH111359 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI DALE, ANDERS M, DEVOR, ANNA · 2016 to 2020
$5.6M
NIBIB NIH HHS R01 EB000790NIMH NIH HHS R01 MH111359
6 · The paper itself

Abstract

backgroundRecent progress in electrophysiological and optical methods for neuronal recordings provides vast amounts of high-resolution data. In parallel, the development of computer technology has allowed simulation of ever-larger neuronal circuits. A challenge in taking advantage of these developments is the construction of single-cell and network models in a way that faithfully reproduces neuronal biophysics with subcellular level of details while keeping the simulation costs at an acceptable level. NEW

methodIn this work, we develop and apply an automated, stepwise method for fitting a neuron model to data with fine spatial resolution, such as that achievable with voltage sensitive dyes (VSDs) and Ca

resultWe apply our method to simulated data from layer 5 pyramidal cells (L5PCs) and construct a model with reduced neuronal morphology. We connect the reduced-morphology neurons into a network and validate against simulated data from a high-resolution L5PC network model. COMPARISON WITH EXISTING

methodsOur approach combines features from several previously applied model-fitting strategies. The reduced-morphology neuron model obtained using our approach reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model.

conclusionsThe network models produced using our method are cost-efficient and predict that interconnected L5PCs are able to amplify delta-range oscillatory inputs across a large range of network sizes and topologies, largely due to the medium after hyperpolarization mediated by the Ca

Indexed as

Models, NeurologicalAnimalsAutomation, LaboratoryCalciumCerebral CortexComputer SimulationDendritesImage Processing, Computer-AssistedIon ChannelsMembrane PotentialsPattern Recognition, AutomatedPotassiumPyramidal CellsSynapsesVoltage-Sensitive Dye ImagingCalciumIon ChannelsPotassiumAutomated fitting methodsBiophysically detailed modelingModel fitting using imaging dataMulti-compartmental neuron modelsParameter peeling

Identifiers

PMID28993204
PMCPMC5705475

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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.