Traumatic brain injury (TBI) is a major clinical and public health problem with few therapeutic interventions successfully translated to the clinic. Identifying imaging-based biomarkers characterizing injury severity and predicting long-term functional and cognitive outcomes in TBI patients is crucial for treatment. TBI results in white matter (WM) injuries, which can be detected using diffusion tensor imaging (DTI). Trauma-induced pathologies lead to accumulation of free water (FW) in brain tissue, and standard DTI is susceptible to the confounding effects of FW. In this study, we applied FW DTI to estimate free water volume fraction (FW-VF) in patients with moderate-to-severe TBI and demonstrated its association with injury severity and long-term outcomes. DTI scans and neuropsychological assessments were obtained longitudinally at 3, 6, and 12 months post-injury for 34 patients and once in 35 matched healthy controls. We observed significantly elevated FW-VF in 85 of 90 WM regions in patients compared to healthy controls (p < 0.05). We then presented a patient-specific summary score of WM regions derived using Mahalanobis distance. We observed that MVF at 3 months significantly predicted functional outcome (p = 0.008), executive function (p = 0.005), and processing speed (p = 0.01) measured at 12 months and was significantly correlated with injury severity (p < 0.001). Our findings are an important step toward implementing MVF as a biomarker for personalized therapy and rehabilitation planning for TBI patients.
Get full access to this article
View all access options for this article.
References
1.
HyderA.A., WunderlichC.A., PuvanachandraP., GururajG., and KobusingyeO.C. (2007). The impact of traumatic brain injuries: a global perspective. NeuroRehabilitation, 22, 341–353.
2.
ChuaK.S., NgY.S., YapS.G., and BokC.W. (2007). A brief review of traumatic brain injury rehabilitation. Ann. Acad. Med. Singap. 36, 31–42.
3.
KaurP., and SharmaS. (2018). Recent advances in pathophysiology of traumatic brain injury. Curr. Neuropharmacol. 16, 1224–1238.
4.
BramlettH.M., and DietrichW.D. (2015). Long-term consequences of traumatic brain injury: current status of potential mechanisms of injury and neurological outcomes. J. Neurotrauma, 32, 1834–1848.
5.
LingsmaH.F., RoozenbeekB., SteyerbergE.W., MurrayG.D., and MaasA.I. (2010). Early prognosis in traumatic brain injury: from prophecies to predictions. Lancet Neurol. 9, 543–554.
6.
DijklandS.A., FoksK.A., PolinderS., DippelD.W.J., MaasA.I.R., LingsmaH.F., and SteyerbergE.W. (2020). Prognosis in moderate and severe traumatic brain injury: a systematic review of contemporary models and validation studies. J. Neurotrauma, 37, 1–13.
7.
AndriessenT.M., JacobsB., and VosP.E. (2010). Clinical characteristics and pathophysiological mechanisms of focal and diffuse traumatic brain injury. J. Cell. Mol. Med. 14, 2381–2392.
8.
AmyotF., ArciniegasD.B., BrazaitisM.P., CurleyK.C., Diaz-ArrastiaR., GandjbakhcheA., HerscovitchP., HindsS.R. II, ManleyG.T., PacificoA., RazumovskyA., RileyJ., SalzerW., ShihR., SmirniotopoulosJ.G., and StockerD. (2015). A review of the effectiveness of neuroimaging modalities for the detection of traumatic brain injury. J. Neurotrauma, 32, 1693–1721.
9.
SmithD.H., MeaneyD.F., and ShullW.H. (2003). Diffuse axonal injury in head trauma. J. Head Trauma Rehabil. 18, 307–316.
10.
HudakA.M., PengL., Marquez de la PlataC., ThottakaraJ., MooreC., HarperC., McCollR., BabcockE., and Diaz-ArrastiaR. (2014). Cytotoxic and vasogenic cerebral oedema in traumatic brain injury: assessment with FLAIR and DWI imaging. Brain Inj. 28, 1602–1609.
11.
LafrenayeA.D., McGinnM.J., and PovlishockJ.T. (2012). Increased intracranial pressure after diffuse traumatic brain injury exacerbates neuronal somatic membrane poration but not axonal injury: evidence for primary intracranial pressure-induced neuronal perturbation. J. Cereb. Blood Flow Metab. 32, 1919–1932.
12.
KimJ.J., and GeanA.D. (2011). Imaging for the diagnosis and management of traumatic brain injury. Neurotherapeutics, 8, 39–53.
13.
JhaR.M., KochanekP.M., and SimardJ.M. (2019). Pathophysiology and treatment of cerebral edema in traumatic brain injury. Neuropharmacology, 145, 230–246.
14.
McKeeA.C., and DaneshvarD.H. (2015). The neuropathology of traumatic brain injury. Handb. Clin. Neurol. 127, 45–66.
15.
DonkinJ.J., and VinkR. (2010). Mechanisms of cerebral edema in traumatic brain injury: therapeutic developments. Curr. Opin. Neurol. 23, 293–299.
16.
XiongY., MahmoodA., and ChoppM. (2018). Current understanding of neuroinflammation after traumatic brain injury and cell-based therapeutic opportunities. Chin. J. Traumatol. 21, 137–151.
17.
WallaceE.J., MathiasJ.L., and WardL. (2018). Diffusion tensor imaging changes following mild, moderate and severe adult traumatic brain injury: a meta-analysis. Brain Imaging Behav. 12, 1607–1621.
18.
ShentonM.E., HamodaH.M., SchneidermanJ.S., BouixS., PasternakO., RathiY., VuM.A., PurohitM.P., HelmerK., KoerteI., LinA.P., WestinC.F., KikinisR., KubickiM., SternR.A., and ZafonteR. (2012). A review of magnetic resonance imaging and diffusion tensor imaging findings in mild traumatic brain injury. Brain Imaging Behav. 6, 137–192.
PasternakO., SochenN., GurY., IntratorN., and AssafY. (2009). Free water elimination and mapping from diffusion MRI. Magn. Reson. Med. 62, 717–730.
21.
HulkowerM.B., PoliakD.B., RosenbaumS.B., ZimmermanM.E., and LiptonM.L. (2013). A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am. J. Neuroradiol. 34, 2064–2074.
22.
MahalanobisP.C. (1936). On the generalized distance in statistics. Presented at the Proceedings of the National Institute of Sciences, Calcutta.
23.
TaylorP.N., Moreira da SilvaN., BlamireA., WangY., and ForsythR. (2020). Early deviation from normal structural connectivity: a novel intrinsic severity score for mild TBI. Neurology, 94, e1021–e1026.
24.
JacksonW.T., NovackT.A., and DowlerR.N. (1998). Effective serial measurement of cognitive orientation in rehabilitation: the Orientation Log. Arch. Phys. Med. Rehabil. 79, 718–720.
25.
PonsfordJ.L., SpitzG., and McKenzieD. (2016). Using post-traumatic amnesia to predict outcome after traumatic brain injury. J. Neurotrauma, 33, 997–1004.
26.
HudakA.M., CaesarR.R., FrolA.B., KruegerK., HarperC.R., TemkinN.R., DikmenS.S., CarlileM., MaddenC., and Diaz-ArrastiaR. (2005). Functional outcome scales in traumatic brain injury: a comparison of the Glasgow Outcome Scale (Extended) and the Functional Status Examination. J. Neurotrauma, 22, 1319–1326.
LezakM. ( 2004). Neuropsychological Assessment. Academic: New York.
29.
RabinowitzA.R., HartT., WhyteJ., and KimJ. (2018). Neuropsychological recovery trajectories in moderate to severe traumatic brain injury: influence of patient characteristics and diffuse axonal injury. J. Int. Neuropsychol. Soc. 24, 237–246.
30.
ReitanR.M., and WolfsonD. (1985). The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. Neuropsychology Press: Tuscon, AZ.
31.
DelisD.C., KaplanE.F., and KramerJ.H. (2001). Delis-Kaplan Executive Function System (D-KEFS). Psychological Corporation: San Antonio, TX.
32.
BentonL., HamsherK., and SivanA. (1994). Controlled Oral Word Association Test Multilingual Aphasia Examination. AJA Associates: Iowa City, IA.
DoshiJ., ErusG., OuY., ResnickS.M., GurR.C., GurR.E., SatterthwaiteT.D., FurthS., and DavatzikosC.; Alzheimer's Neuroimaging Initiative. (2016). MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection. NeuroImage, 127, 186–195.
36.
ManjonJ.V., CoupeP., ConchaL., BuadesA., CollinsD.L., and RoblesM. (2013). Diffusion weighted image denoising using overcomplete local PCA. PLoS One, 8, e73021.
37.
AnderssonJ.L.R., and SotiropoulosS.N. (2016). An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage, 125, 1063–1078.
38.
Le BihanD., ManginJ.F., PouponC., ClarkC.A., PappataS., MolkoN., and ChabriatH. (2001). Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging, 13, 534–546.
39.
GaryfallidisE., BrettM., AmirbekianB., RokemA., van der WaltS., DescoteauxM., Nimmo-SmithI., and DipyC. (2014). Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8.
AvantsB.B., EpsteinC.L., GrossmanM., and GeeJ.C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41.
42.
OishiK., FariaA.V., and MoriS. (2010). JHU-MNI-ss Atlas.Johns Hopkins University School of Medicine, Department of Radiology, Center for Brain Imaging Science: Baltimore, MD.
43.
BenjaminiY., and HochbergY. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. Royal Stat. Soc. Ser. B, 57, 289–300.
44.
SeaboldS., and PerktoldJ. (2010). statsmodels: econometric and statistical modeling with python. Presented at the Proceedings of the 9th Python in Science Conference (SciPy 2010), Austin,TX.
45.
SmithD.H., HicksR., and PovlishockJ.T. (2013). Therapy development for diffuse axonal injury. J. Neurotrauma, 30, 307–323.
46.
Rodriguez-GrandeB., IchkovaA., LemarchantS., and BadautJ. (2017). Early to long-term alterations of CNS barriers after traumatic brain injury: considerations for drug development. AAPS J. 19, 1615–1625.
47.
JohnsonV.E., StewartJ.E., BegbieF.D., TrojanowskiJ.Q., SmithD.H., and StewartW. (2013). Inflammation and white matter degeneration persist for years after a single traumatic brain injury. Brain, 136, 28–42.
48.
MaxwellW.L., MacKinnonM.A., StewartJ.E., and GrahamD.I. (2010). Stereology of cerebral cortex after traumatic brain injury matched to the Glasgow Outcome Score. Brain, 133, 139–160.
49.
DingK., Marquez de la PlataC., WangJ.Y., MumphreyM., MooreC., HarperC., MaddenC.J., McCollR., WhittemoreA., DevousM.D., and Diaz-ArrastiaR. (2008). Cerebral atrophy after traumatic white matter injury: correlation with acute neuroimaging and outcome. J. Neurotrauma, 25, 1433–1440.
50.
PasternakO., KubickiM., and ShentonM.E. (2016). In vivo imaging of neuroinflammation in schizophrenia. Schizophr. Res. 173, 200–212.
51.
DumontM., RoyM., JodoinP.M., MorencyF.C., HoudeJ.C., XieZ., BauerC., SamadT.A., Van DijkK.R.A., GoodmanJ.A., and DescoteauxM.; Alzheimer's Disease NeuroimagingInitiative. (2019). Free water in white matter differentiates MCI and AD from control subjects. Front. Aging Neurosci. 11, 270.
52.
EddeM., TheaudG., RheaultF., DilharreguyB., HelmerC., DartiguesJ.F., AmievaH., AllardM., DescoteauxM., and CathelineG. (2020). Free water: a marker of age-related modifications of the cingulum white matter and its association with cognitive decline. PLoS One, 15, e0242696.
53.
OforiE., PasternakO., PlanettaP.J., BurciuR., SnyderA., FeboM., GoldeT.E., OkunM.S., and VaillancourtD.E. (2015). Increased free water in the substantia nigra of Parkinson's disease: a single-site and multi-site study. Neurobiol. Aging, 36, 1097–1104.
JiF., PasternakO., LiuS., LokeY.M., ChooB.L., HilalS., XuX., IkramM.K., VenketasubramanianN., ChenC.L., and ZhouJ. (2017). Distinct white matter microstructural abnormalities and extracellular water increases relate to cognitive impairment in Alzheimer's disease with and without cerebrovascular disease. Alzheimers Res. Ther. 9, 63.
56.
GuoJ.Y., LeshT.A., NiendamT.A., RaglandJ.D., TullyL.M., and CarterC.S. (2021). Brain free water alterations in first-episode psychosis: a longitudinal analysis of diagnosis, course of illness, and medication effects. Psychol. Med. 51, 1001–1010.
57.
GullettJ.M., O'SheaA., LambD.G., PorgesE.C., O'SheaD.M., PasternakO., CohenR.A., and WoodsA.J. (2020). The association of white matter free water with cognition in older adults. NeuroImage, 219, 117040.
58.
PalaciosE.M., OwenJ.P., YuhE.L., WangM.B., VassarM.J., FergusonA.R., Diaz-ArrastiaR., GiacinoJ.T., OkonkwoD.O., RobertsonC.S., SteinM.B., TemkinN., JainS., McCreaM., MacDonaldC.L., LevinH.S., ManleyG.T., and MukherjeeP.; TRACK-TBIInvestigators. (2020). The evolution of white matter microstructural changes after mild traumatic brain injury: a longitudinal DTI and NODDI study. Sci. Adv. 6, eaaz6892.
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
