Evidence map›Paper›PMID 35519616›Full record

ArticleFrontiers in bioengineering and biotechnology2022

A Finite Element Model of Cerebral Vascular Injury for Predicting Microbleeds Location.

Harry Duckworth, Adriana Azor, Nikolaus Wischmann, Karl A Zimmerman, Ilaria Tanini, David J Sharp, Mazdak Ghajari

Open access · goldAbstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.6field-weighted citation impact, top 9% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

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  4. Multimodal characterization of intracranial biomechanics in a 3D biofidelic head surrogate.Journal of the mechanical behavior of biomedical materials · 2026
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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 at 3 institutions in 2 countries.

Harry DuckworthHEAD Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Adriana AzorHEAD Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Nikolaus WischmannHEAD Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Karl A ZimmermanHEAD Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Ilaria TaniniHEAD Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
David J SharpThe Computational, Cognitive and Clinical Neuroimaging Laboratory, Imperial College London, London, United Kingdom.
Mazdak GhajariHEAD Lab, Dyson School of Design Engineering, Imperial College London, London, United Kingdom.
Dyson (United Kingdom) · GBWellcome Centre for Human Neuroimaging · GBImperial College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Finite Element (FE) models of brain mechanics have improved our understanding of the brain response to rapid mechanical loads that produce traumatic brain injuries. However, these models have rarely incorporated vasculature, which limits their ability to predict the response of vessels to head impacts. To address this shortcoming, here we used high-resolution MRI scans to map the venous system anatomy at a submillimetre resolution. We then used this map to develop an FE model of veins and incorporated it in an anatomically detailed FE model of the brain. The model prediction of brain displacement at different locations was compared to controlled experiments on post-mortem human subject heads, yielding over 3,100 displacement curve comparisons, which showed fair to excellent correlation between them. We then used the model to predict the distribution of axial strains and strain rates in the veins of a rugby player who had small blood deposits in his white matter, known as microbleeds, after sustaining a head collision. We hypothesised that the distribution of axial strain and strain rate in veins can predict the pattern of microbleeds. We reconstructed the head collision using video footage and multi-body dynamics modelling and used the predicted head accelerations to load the FE model of vascular injury. The model predicted large axial strains in veins where microbleeds were detected. A region of interest analysis using white matter tracts showed that the tract group with microbleeds had 95th percentile peak axial strain and strain rate of 0.197 and 64.9 s

Indexed as

cerebral vasculature injurycerebrovasculaturefinite element modelmicrobleedsmicrohaemorrhagesmultibody simulationtraumatic brain injury

Identifiers

PMID35519616
PMCPMC9065595
OpenAlexW4224242167

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.