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dc.contributor.authorLaksari, Kaveh
dc.contributor.authorFanton, Michael
dc.contributor.authorWu, Lyndia C
dc.contributor.authorNguyen, Taylor H
dc.contributor.authorKurt, Mehmet
dc.contributor.authorGiordano, Chiara
dc.contributor.authorKelly, Eoin
dc.contributor.authorO'Keeffe, Eoin
dc.contributor.authorWallace, Eugene
dc.contributor.authorDoherty, Colin
dc.contributor.authorCampbell, Matthew
dc.contributor.authorTiernan, Stephen
dc.contributor.authorGrant, Gerald
dc.contributor.authorRuan, Jesse
dc.contributor.authorBarbat, Saeed
dc.contributor.authorCamarillo, David B
dc.date.accessioned2020-03-18T20:30:17Z
dc.date.available2020-03-18T20:30:17Z
dc.date.issued2020-02-04
dc.identifier.citationLaksari, K., Fanton, M., Wu, L. C., Nguyen, T. H., Kurt, M., Giordano, C., ... & Campbell, M. (2020). Multi-directional dynamic model for traumatic brain injury detection. Journal of Neurotrauma. 10.1089/neu.2018.6340en_US
dc.identifier.issn0897-7151
dc.identifier.pmid31856650
dc.identifier.doi10.1089/neu.2018.6340
dc.identifier.urihttp://hdl.handle.net/10150/637740
dc.description.abstractGiven the worldwide adverse impact of traumatic brain injury (TBI) on the human population, its diagnosis and prediction are of utmost importance. Historically, many studies have focused on associating head kinematics to brain injury risk. Recently, there has been a push toward using computationally expensive finite element (FE) models of the brain to create tissue deformation metrics of brain injury. Here, we develop a new brain injury metric, the brain angle metric (BAM), based on the dynamics of a 3 degree-of-freedom lumped parameter brain model. The brain model is built based on the measured natural frequencies of an FE brain model simulated with live human impact data. We show that it can be used to rapidly estimate peak brain strains experienced during head rotational accelerations that cause mild TBI. In our data set, the simplified model correlates with peak principal FE strain (R2 = 0.82). Further, coronal and axial brain model displacement correlated with fiber-oriented peak strain in the corpus callosum (R2 = 0.77). Our proposed injury metric BAM uses the maximum angle predicted by our brain model and is compared against a number of existing rotational and translational kinematic injury metrics on a data set of head kinematics from 27 clinically diagnosed injuries and 887 non-injuries. We found that BAM performed comparably to peak angular acceleration, translational acceleration, and angular velocity in classifying injury and non-injury events. Metrics that separated time traces into their directional components had improved model deviance compare with those that combined components into a single time trace magnitude. Our brain model can be used in future work to rapidly approximate the peak strain resulting from mild to moderate head impacts and to quickly assess brain injury risk.en_US
dc.language.isoenen_US
dc.publisherMARY ANN LIEBERT, INCen_US
dc.rights© Mary Ann Liebert, Inc.en_US
dc.subjectbrain injuryen_US
dc.subjectconcussionen_US
dc.subjectinjury criterionen_US
dc.subjectinjury predictionen_US
dc.titleMulti-Directional Dynamic Model For Traumatic Brain Injury Detectionen_US
dc.typeArticleen_US
dc.identifier.eissn1557-9042
dc.contributor.departmentUniv Arizona, Dept Biomed Engnen_US
dc.identifier.journalJOURNAL OF NEUROTRAUMAen_US
dc.description.note12 month embargo; published online: 4 February 2020en_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleJournal of neurotrauma
dc.source.countryUnited States


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