Evidence map›Paper›PMID 36125606›Full record

ArticleAnnals of biomedical engineering2022

Concussion Prone Scenarios: A Multi-Dimensional Exploration in Impact Directions, Brain Morphology, and Network Architectures Using Computational Models.

Taotao Wu, Jared A Rifkin, Adam C Rayfield, Erin D Anderson, Matthew B Panzer, David F Meaney

Abstract read
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In one paragraph

Article in Annals of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

8 citing papers in PubMed, 12 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 2 institutions in 1 country.

Taotao WuDepartment of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 S 33rd St, Philadelphia, PA, 19104, USA.
Jared A RifkinDepartment of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA.
Adam C RayfieldDepartment of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 S 33rd St, Philadelphia, PA, 19104, USA.
Erin D AndersonDepartment of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 S 33rd St, Philadelphia, PA, 19104, USA.
Matthew B PanzerDepartment of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA.
David F MeaneyDepartment of Bioengineering, University of Pennsylvania, 240 Skirkanich Hall, 210 S 33rd St, Philadelphia, PA, 19104, USA. dmeaney@seas.upenn.edu.ORCID http://orcid.org/0000-0002-0954-4122
University of Pennsylvania · USUniversity of Virginia · US

Funding

Paul G. Allen Frontiers Group 12347
6 · The paper itself

Abstract

While individual susceptibility to traumatic brain injury (TBI) has been speculated, past work does not provide an analysis considering how physical features of an individual's brain (e.g., brain size, shape), impact direction, and brain network features can holistically contribute to the risk of suffering a TBI from an impact. This work investigated each of these features simultaneously using computational modeling and analyses of simulated functional connectivity. Unlike the past studies that assess the severity of TBI based on the quantification of brain tissue damage (e.g., principal strain), we approached the brain as a complex network in which neuronal oscillations orchestrate to produce normal brain function (estimated by functional connectivity) and, to this end, both the anatomical damage location and its topological characteristics within the brain network contribute to the severity of brain function disruption and injury. To represent the variations in the population, we analyzed a publicly available database of brain imaging data and selected five distinct network architectures, seven different brain sizes, and three uniaxial head rotational conditions to study the consequences of 74 virtual impact scenarios. Results show impact direction produces the most significant change in connections across brain areas (structural connectome) and the functional coupling of activity across these brain areas (functional connectivity). Axial rotations were more injurious than those with sagittal and coronal rotations when the head kinematics were the same for each condition. When the impact direction was held constant, brain network architecture showed a significantly different vulnerability across axial and sagittal, but not coronal rotations. As expected, brain size significantly affected the expected change in structural and functional connectivity after impact. Together, these results provided groupings of predicted vulnerability to impact-a subgroup of male brain architectures exposed to axial impacts were most vulnerable, while a subgroup of female brain architectures was the most tolerant to the sagittal impacts studied. These findings lay essential groundwork for subject-specific analyses of concussion and provide invaluable guidance for designing personalized protection equipment.

Indexed as

Brain ConcussionBrain InjuriesBrain Injuries, TraumaticBrainComputer SimulationFemaleHumansMaleFinite element modelFunctional connectivityKuramoto modelSubject-specific analysis

Identifiers

PMID36125606
OpenAlexW4296489432

What OpenQuestion holds

Textmetadata
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.