{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:01:34Z","timestamp":1770832894323,"version":"3.50.1"},"reference-count":35,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2020,2,27]],"date-time":"2020-02-27T00:00:00Z","timestamp":1582761600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004440","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["096646\/Z\/11\/Z"],"award-info":[{"award-number":["096646\/Z\/11\/Z"]}],"id":[{"id":"10.13039\/100004440","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007229","name":"Bijzonder Onderzoeksfonds","doi-asserted-by":"publisher","award":["01N00214"],"award-info":[{"award-number":["01N00214"]}],"id":[{"id":"10.13039\/501100007229","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000990","name":"Australian Catholic University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000990","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann Clin Transl Neurol"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Traumatic brain injury (TBI) is a heterogeneous disease with multiple neurological deficits that evolve over time. It is also associated with an increased incidence of neurodegenerative diseases. Accordingly, clinicians need better tools to predict a patient\u2019s long\u2010term prognosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Diffusion\u2010weighted and anatomical MRI data were collected from 17 adolescents (mean age\u00a0=\u00a015y8mo) with moderate\u2010to\u2010severe TBI and 19 healthy controls. Using a network diffusion model (NDM), we examined the effect of progressive deafferentation and gray matter thinning in young TBI patients. Moreover, using a novel automated inference method, we identified several injury epicenters in order to determine the neural degenerative patterns in each TBI patient.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We were able to identify the subject\u2010specific patterns of degeneration in each patient. In particular, the hippocampus, temporal cortices, and striatum were frequently found to be the epicenters of degeneration across the TBI patients. Orthogonal transformation of the predicted degeneration, using principal component analysis, identified distinct spatial components in the temporal\u2013hippocampal network and the cortico\u2010striatal network, confirming the vulnerability of these networks to injury. The NDM model, best predictive of the degeneration, was significantly correlated with time since injury, indicating that NDM can potentially capture the pathological progression in the chronic phase of TBI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>These findings suggest that network spread may help explain patterns of distant gray matter thinning, which would be consistent with Wallerian degeneration of the white matter connections (i.e., \u201cdiaschisis\u201d) from diffuse axonal injuries and multifocal contusive injuries, and the neurodegenerative patterns of abnormal protein aggregation and transmission, which are hallmarks of brain changes in TBI. NDM approaches could provide highly subject\u2010specific biomarkers relevant for disease monitoring and personalized therapies in TBI.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acn3.50984","type":"journal-article","created":{"date-parts":[[2020,2,27]],"date-time":"2020-02-27T15:31:11Z","timestamp":1582817471000},"page":"270-279","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Network diffusion modeling predicts neurodegeneration in traumatic brain injury"],"prefix":"10.1002","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0043-7531","authenticated-orcid":false,"given":"Govinda R.","family":"Poudel","sequence":"first","affiliation":[{"name":"Mary MacKillop Institute for Health Research Australian Catholic University Melbourne VIC Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6715-1060","authenticated-orcid":false,"given":"Juan 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