Evidence map›Paper›PMID 35288611›Full record

ArticleScientific reports2022

Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue.

Miriam Menzel, Jan A Reuter, David Gräßel, Irene Costantini, Katrin Amunts, Markus Axer

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 16% 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

10 citing papers in PubMed, 14 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 3 institutions in 2 countries.

Miriam Menzel *Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich, Germany. m.menzel@fz-juelich.de.
Jan A Reuter *Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich, Germany.
David GräßelInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich, Germany.
Irene CostantiniEuropean Laboratory for Non-Linear Spectroscopy, University of Florence, Florence, Italy.
Katrin AmuntsInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich, Germany.
Markus AxerInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich GmbH, Jülich, Germany.
Forschungszentrum Jülich · DEDüsseldorf University Hospital · DEUniversity of Florence · IT

Funding

Vervet Research Colony as a Biomedical ResourceP40OD010965 · OD · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Matthew Jorgensen · 2012 to 2026
$14.6M
Postnatal Development of Cortical Receptors and White Matter TractsR01MH092311 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI WOODS, ROGER P · 2012 to 2016
$2.2M
NIH HHS P40 OD010965NIH HHS R01MH092311NIMH NIH HHS R01 MH092311
6 · The paper itself

Abstract

The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclinations is more challenging because they are derived from the amplitude of the birefringence signals, which depends e.g. on the amount of nerve fibres. One possibility to improve the accuracy is to consider the average transmitted light intensity (transmittance weighting). The current procedure requires effortful manual adjustment of parameters and anatomical knowledge. Here, we introduce an automated, optimised computation of the fibre inclinations, allowing for a much faster, reproducible determination of fibre orientations in 3D-PLI. Depending on the degree of myelination, the algorithm uses different models (transmittance-weighted, unweighted, or a linear combination), allowing to account for regionally specific behaviour. As the algorithm is parallelised and GPU optimised, it can be applied to large data sets. Moreover, it only uses images from standard 3D-PLI measurements without tilting, and can therefore be applied to existing data sets from previous measurements. The functionality is demonstrated on unstained coronal and sagittal histological sections of vervet monkey and rat brains.

Indexed as

BrainImaging, Three-DimensionalAlgorithmsAnimalsAxonsChlorocebus aethiopsNerve FibersRats

Identifiers

PMID35288611
PMCPMC8921329
OpenAlexW3217691998

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.