Network analysis applied to post-concussion symptoms in two mild traumatic brain injury samples.

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dc.citation.volume14
dc.contributor.authorFaulkner JW
dc.contributor.authorTheadom A
dc.contributor.authorSnell DL
dc.contributor.authorWilliams MN
dc.contributor.editorAndelic N
dc.coverage.spatialSwitzerland
dc.date.accessioned2024-08-05T02:47:38Z
dc.date.available2024-08-05T02:47:38Z
dc.date.issued2023-07-20
dc.description.abstractOBJECTIVE: A latent disease explanation cannot exclusively explain post-concussion symptoms after mild traumatic brain injury (mTBI). Network analysis offers an alternative form of explanation for relationships between symptoms. The study aimed to apply network analysis to post-concussion symptoms in two different mTBI cohorts; an acute treatment-seeking sample and a sample 10 years post-mTBI. METHOD: The treatment-seeking sample (n = 258) were on average 6 weeks post-injury; the 10 year post mTBI sample (n = 193) was derived from a population-based incidence and outcomes study (BIONIC). Network analysis was completed on post-concussion symptoms measured using the Rivermead Post-Concussion Questionnaire. RESULTS: In the treatment-seeking sample, frustration, blurred vision, and concentration difficulties were central to the network. These symptoms remained central in the 10 year post mTBI sample. A Network Comparison Test revealed evidence of a difference in network structure across the two samples (p = 0.045). However, the only symptoms that showed significant differences in strength centrality across samples were irritability and restlessness. CONCLUSION: The current findings suggest that frustration, blurred vision and concentration difficulties may have an influential role in the experience and maintenance of post-concussion symptoms. The impact of these symptoms may remain stable over time. Targeting and prioritising the management of these symptoms may be beneficial for mTBI rehabilitation.
dc.description.confidentialfalse
dc.edition.edition2023
dc.format.pagination1226367-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/37545717
dc.identifier.citationFaulkner JW, Theadom A, Snell DL, Williams MN. (2023). Network analysis applied to post-concussion symptoms in two mild traumatic brain injury samples.. Front Neurol. 14. (pp. 1226367-).
dc.identifier.doi10.3389/fneur.2023.1226367
dc.identifier.eissn1664-2295
dc.identifier.elements-typejournal-article
dc.identifier.issn1664-2295
dc.identifier.number1226367
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/71197
dc.languageeng
dc.publisherFrontiers Media S.A.
dc.publisher.urihttps://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2023.1226367/full#h12
dc.relation.isPartOfFront Neurol
dc.rights(c) 2023 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectconcussion
dc.subjectmild traumatic brain injury
dc.subjectnetwork analysis
dc.subjectoutcomes
dc.subjectpost-concussion symptoms
dc.titleNetwork analysis applied to post-concussion symptoms in two mild traumatic brain injury samples.
dc.typeJournal article
pubs.elements-id479718
pubs.organisational-groupOther
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