Mild traumatic brain injury (mTBI) initiating long-term effects on white matter integrity resembles brain-aging changes, implying an aging process accelerated by mTBI. This longitudinal study aims to investigate the mTBI-induced acceleration of the brain-aging process by developing a neuroimaging model to predict brain age. The brain-age prediction model was defined using relevance vector regression based on fractional anisotropy from diffusion tensor imaging of 523 healthy individuals. The model was used to estimate the brain-predicted age difference (brain-PAD) between the chronological and estimated brain age in 116 acute mTBI patients and 63 healthy controls. Fifty patients were followed for 6 ∼ 12 months to evaluate the longitudinal changes in brain-PAD. We investigated whether brain-PAD was greater in patients of older age, post-concussion complaints, and apolipoprotein E (APOE) ɛ4 genotype, and whether it had the potential to predict neuropsychological outcomes. The brain-age prediction model predicted brain age accurately (r = 0.96). The brains of mTBI patients in the acute phase were estimated to be “older,” with greater brain-PAD (2.59 ± 5.97 years) than the healthy controls (0.12 ± 3.19 years) (p < 0.05), and remained stable 6–12 month post-injury (2.50 ± 4.54 years). Patients who were older or who had post-concussion complaints, rather than APOE ɛ4 genotype, had greater brain-PADs (p < 0.001, p = 0.024). Additionally, brain-PAD in the acute phase predicted information processing speed at the 6 ∼ 12 month follow-up (r = -0.36, p = 0.01). In conclusion, mTBI accelerates the brain-aging process, and brain-PAD may be capable of evaluating aging-associated issues post-injury, such as increased risks of neurodegeneration.
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References
1.
LevinH.S., and Diaz-ArrastiaR.R. (2015). Diagnosis, prognosis, and clinical management of mild traumatic brain injury. Lancet Neurol. 14, 506–517.
2.
Mac DonaldC.L., BarberJ., AndreJ., PanksC., ZalewskiK., and TemkinN. (2019). Longitudinal neuroimaging following combat concussion: sub-acute, 1 year and 5 years post-injury. Brain commun. 1, fcz031.
3.
GardnerR.C., BurkeJ.F., NettiksimmonsJ., KaupA., BarnesD.E., and YaffeK. (2014). Dementia risk after traumatic brain injury vs nonbrain trauma: the role of age and severity. JAMA Neurol. 71, 1490–1497.
4.
FannJ.R., RibeA.R., PedersenH.S., Fenger-GrønM., ChristensenJ., BenrosM.E., and VestergaardM. (2018). Long-term risk of dementia among people with traumatic brain injury in Denmark: a population-based observational cohort study. Lancet Psychiatry, 5, 424–431.
5.
CoxS.R., RitchieS.J., Tucker-DrobE.M., LiewaldD.C., HagenaarsS.P., DaviesG., WardlawJ.M., GaleC.R., BastinM.E., and DearyI.J. (2016). Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat. Commun. 7, 1–13.
6.
GoodC.D., JohnsrudeI.S., AshburnerJ., HensonR.N., FristonK.J., and FrackowiakR.S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage, 14, 21–36.
7.
WangJ., KnolM.J., TiulpinA., DubostF., de BruijneM., VernooijM.W., AdamsH.H., IkramM.A., NiessenW.J., and RoshchupkinG.V. (2019). Gray matter age prediction as a biomarker for risk of dementia. Proc. Natl. Acad. Sci. 116, 21,213–21,218.
8.
SchnackH.G., Van HarenN.E., NieuwenhuisM., Hulshoff PolH.E., CahnW., and KahnR.S. (2016). Accelerated brain aging in schizophrenia: a longitudinal pattern recognition study. Am. J. Psychiatry, 173, 607–616.
9.
ColeJ.H., AnnusT., WilsonL.R., RemtullaR., HongY.T., FryerT.D., Acosta-CabroneroJ., Cardenas-BlancoA., SmithR., and MenonD.K. (2017). Brain-predicted age in Down syndrome is associated with beta amyloid deposition and cognitive decline. Neurobiol. Aging, 56, 41–49.
10.
ColeJ.H., UnderwoodJ., CaanM.W.A., FrancescoD.D., ZoestR.A.V., LeechR., WitF.W.N.M., PortegiesP., GeurtsenG.J., and SchmandB.A. (2017). Increased brain-predicted aging in treated HIV disease. Neurology, 88, 1349–1357.
11.
ColeJ.H. and FrankeK. (2017). Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40, 681–690.
12.
GaserC., FrankeK., KlöppelS., KoutsoulerisN., SauerH., and Alzheimers Disease Neuroimaging Initiative (2013). BrainAGE in mild cognitive impaired patients: predicting the conversion to Alzheimer's disease. PloS One 8, e67346.
13.
KoutsoulerisN., DavatzikosC., BorgwardtS., GaserC., BottlenderR., FrodlT., FalkaiP., Riecher-RösslerA., MöllerH.-J., and ReiserM. (2014). Accelerated brain aging in schizophrenia and beyond: a neuroanatomical marker of psychiatric disorders. Schizophr. Bull. 40, 1140–1153.
14.
LiangH., ZhangF., and NiuX. (2019). Investigating systematic bias in brain age estimation with application to post-traumatic stress disorders. Hum. Brain Mapp. 40, 3143–3152.
15.
ColeJ.H., LeechR., SharpD.J., and Alzheimer's Disease NeuroimagingInitiative (2015). Prediction of brain age suggests accelerated atrophy after traumatic brain injury. Ann. Neurol. 77, 571–581.
16.
SmithD.H., MeaneyD.F., and ShullW.H. (2003). Diffuse axonal injury in head trauma. J. Head Trauma Rehabil. 18, 307–316.
17.
Mac DonaldC.L., JohnsonA.M., CooperD., NelsonE.C., WernerN.J., ShimonyJ.S., SnyderA.Z., RaichleM.E., WitherowJ.R., FangR., FlahertyS.F., and BrodyD.L. (2011). Detection of blast-related traumatic brain injury in U.S. military personnel. N. Engl. J. Med. 364, 2091–2100.
18.
LiptonM.L., GellellaE., LoC., GoldT., ArdekaniB.A., ShiftehK., BelloJ.A., and BranchC.A. (2008). Multifocal white matter ultrastructural abnormalities in mild traumatic brain injury with cognitive disability: a voxel-wise analysis of diffusion tensor imaging. J. Neurotrauma, 25, 1335–1342.
19.
SharpD.J., ScottG., and LeechR. (2014). Network dysfunction after traumatic brain injury. Nat. Rev. Neurol. 10, 156.
20.
SmithD.H., JohnsonV.E., and StewartW. (2013). Chronic neuropathologies of single and repetitive TBI: substrates of dementia?. Nat. Rev. Neurol. 9, 211.
TippingM.E. (2001). Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211–244.
23.
CarrollL., CassidyJ.D., PelosoP., BorgJ., Von HolstH., HolmL., PaniakC., and PépinM. (2004). Prognosis for mild traumatic brain injury: results of the WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J. Rehabil. Med. 36, 84–105.
24.
JagustW. (2013). Vulnerable neural systems and the borderland of brain aging and neurodegeneration. Neuron, 77, 219–234.
25.
NicollJ.A., RobertsG.W., and GrahamD.I. (1995). Apolipoprotein E ɛ4 allele is associated with deposition of amyloid β-protein following head injury. Nat. Med. 1, 135–137.
26.
HolmL., David CassidyJ., CarrollL., and BorgJ. (2005). Summary of the WHO collaborating centre for neurotrauma task force on mild traumatic brain injury. J. Rehabil. Med. 37, 137–141.
27.
World Health Organization (1992). The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines. World Health Organization: Geneva.
28.
RyanJ.J., and WardL.C. (1999). Validity, reliability, and standard errors of measurement for two seven-subtest short forms of the Wechsler Adult Intelligence Scale—III. Psychol. Assess. 11, 207.
29.
BowieC.R., and HarveyP.D. (2006). Administration and interpretation of the Trail Making Test. Nat. Protoc. 1, 2277.
30.
MerisaariH., TuulariJ.J., KarlssonL., ScheininN.M., ParkkolaR., SaunavaaraJ., LähdesmäkiT., LehtolaS.J., KeskinenM., and LewisJ.D. (2019). Test–retest reliability of Diffusion Tensor Imaging metrics in neonates. NeuroImage, 197, 598–607.
31.
BrightM.G., and MurphyK. (2013). Reliable quantification of BOLD fMRI cerebrovascular reactivity despite poor breath-hold performance. NeuroImage, 83, 559–568.
SavjaniR.R., TaylorB.A., AcionL., WildeE.A., and JorgeR.E. (2017). Accelerated changes in cortical thickness measurements with age in military service members with traumatic brain injury. J. Neurotrauma, 34, 3107–3116.
34.
ColeJ.H., and FrankeK. (2017). Predicting age using neuroimaging: innovative brain ageing biomarkers. Trends Neurosci. 40, 681–690.
35.
D'EspositoM., VerfaellieM., AlexanderM.P., and KatzD.I. (1995). Amnesia following traumatic bilateral fornix transection. Neurology, 45, 1546–1550.
36.
JacquemontT., FallaniF.D.V., BertrandA., EpelbaumS., RoutierA., DuboisB., HampelH., DurrlemanS., ColliotO., and Initiative, A.s.D.N. (2017). Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment. Neurobiol. Aging, 55, 177–189.
37.
de GrootM., CremersL.G., IkramM.A., HofmanA., KrestinG.P., van der LugtA., NiessenW.J., and VernooijM.W. (2015). White matter degeneration with aging: longitudinal diffusion MR imaging analysis. Radiology, 279, 532–541.
38.
IbaM., GuoJ.L., McBrideJ.D., ZhangB., TrojanowskiJ.Q., and LeeV.M.-Y. (2013). Synthetic tau fibrils mediate transmission of neurofibrillary tangles in a transgenic mouse model of Alzheimer's-like tauopathy. J. Neurosci. 33, 1024–1037.
39.
AhmedZ., CooperJ., MurrayT.K., GarnK., McNaughtonE., ClarkeH., ParhizkarS., WardM.A., CavalliniA., and JacksonS. (2014). A novel in vivo model of tau propagation with rapid and progressive neurofibrillary tangle pathology: the pattern of spread is determined by connectivity, not proximity. Acta Neuropathol. 127, 667–683.
40.
WeinerM.W., HarveyD., HayesJ., LandauS.M., AisenP.S., PetersenR.C., TosunD., VeitchD.P., Jack JrC.R., and DecarliC. (2017). Effects of traumatic brain injury and posttraumatic stress disorder on development of Alzheimer's disease in Vietnam veterans using the Alzheimer's Disease Neuroimaging Initiative: preliminary report. Alzheimers Dement. 3, 177–188.
41.
O'SullivanM., JonesD.K., SummersP., MorrisR., WilliamsS., and MarkusH. (2001). Evidence for cortical “disconnection”. as a mechanism of age-related cognitive decline. Neurology, 57, 632–638.
42.
MeythalerJ.M., PeduzziJ.D., EleftheriouE., and NovackT.A. (2001). Current concepts: diffuse axonal injury–associated traumatic brain injury. Arch. Phys. Med. Rehabil. 82, 1461–1471.
43.
JohnsonV.E., StewartW., and SmithD.H. (2012). Widespread tau and amyloid-beta pathology many years after a single traumatic brain injury in humans. Brain Pathol. 22, 142–149.
44.
PetzoldA. (2005). Neurofilament phosphoforms: surrogate markers for axonal injury, degeneration and loss. J. Neurol. Sci. 233, 183–198.
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