Evidence map›Paper›PMID 32796937›Full record

ArticleScientific reports2020

A multimodal computational pipeline for 3D histology of the human brain.

Matteo Mancini, Adrià Casamitjana, Loic Peter, Eleanor Robinson, Shauna Crampsie, David L Thomas, Janice L Holton, Zane Jaunmuktane, Juan Eugenio Iglesias

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Review
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  11. Three-dimensional histology reveals dissociable human hippocampal long-axis gradients of Alzheimer's pathology.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
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  19. Micro-CT-Based Bone Microarchitecture Analysis of the Murine Skull.Methods in molecular biology (Clifton, N.J.) · 2022
    Article
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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.

Matteo ManciniCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK. m.mancini2@bsms.ac.uk.
Adrià CasamitjanaCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Loic PeterCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Eleanor RobinsonCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK.
Shauna CrampsieQueen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK.
David L ThomasNeuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, UK.
Janice L HoltonQueen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK.
Zane JaunmuktaneQueen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, University College London, London, UK.
Juan Eugenio IglesiasCentre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK. e.iglesias@ucl.ac.uk.

Funding

Wellcome TrustWellcome Trust 213722/Z/18/Z
6 · The paper itself

Abstract

Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of whole human hemispheres from two donors.

Indexed as

Aged, 80 and overBrainHumansImaging, Three-DimensionalMagnetic Resonance ImagingMultimodal ImagingNeuroimaging

Identifiers

PMID32796937
PMCPMC7429828

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Registered trials

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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.