Two Coarse Spatial Patterns of Altered Brain Microstructure Predict Post-traumatic Amnesia in the Subacute Stage of Severe Traumatic Brain Injury

Sara H Andreasen, Kasper W Andersen, Virginia Conde, Tim B Dyrby, Oula Puonti, Lars P Kammersgaard, Camilla G Madsen, Kristoffer H Madsen, Ingrid Poulsen, Hartwig R Siebner, Sara H Andreasen, Kasper W Andersen, Virginia Conde, Tim B Dyrby, Oula Puonti, Lars P Kammersgaard, Camilla G Madsen, Kristoffer H Madsen, Ingrid Poulsen, Hartwig R Siebner

Abstract

Introduction: Diffuse traumatic axonal injury (TAI) is one of the key mechanisms leading to impaired consciousness after severe traumatic brain injury (TBI). In addition, preferential regional expression of TAI in the brain may also influence clinical outcome. Aim: We addressed the question whether the regional expression of microstructural changes as revealed by whole-brain diffusion tensor imaging (DTI) in the subacute stage after severe TBI may predict the duration of post-traumatic amnesia (PTA). Method: Fourteen patients underwent whole-brain DTI in the subacute stage after severe TBI. Mean fractional anisotropy (FA) and mean diffusivity (MD) were calculated for five bilateral brain regions: fronto-temporal, parieto-occipital, and midsagittal hemispheric white matter, as well as brainstem and basal ganglia. Region-specific calculation of mean FA and MD only considered voxels that showed no tissue damage, using an exclusive mask with all voxels that belonged to local brain lesions or microbleeds. Mean FA or MD of the five brain regions were entered in separate partial least squares (PLS) regression analyses to identify patterns of regional microstructural changes that account for inter-individual variations in PTA. Results: For FA, PLS analysis revealed two spatial patterns that significantly correlated with individual PTA. The lower the mean FA values in all five brain regions, the longer that PTA lasted. A pattern characterized by lower FA values in the deeper brain regions relative to the FA values in the hemispheric regions also correlated with longer PTA. Similar trends were found for MD, but opposite in sign. The spatial FA changes as revealed by PLS components predicted the duration of PTA. Individual PTA duration, as predicted by a leave-one-out cross-validation analysis, correlated with true PTA values (Spearman r = 0.68, p permutation = 0.008). Conclusion: Two coarse spatial patterns of microstructural damage, indexed as reduction in FA, were relevant to recovery of consciousness after TBI. One pattern expressed was consistent with diffuse microstructural damage across the entire brain. A second pattern was indicative of a preferential damage of deep midline brain structures.

Keywords: diffusion tensor imaging; disorders of consciousness; partial least squares analysis; post-traumatic amnesia; prediction; traumatic brain injury.

Copyright © 2020 Andreasen, Andersen, Conde, Dyrby, Puonti, Kammersgaard, Madsen, Madsen, Poulsen and Siebner.

Figures

Figure 1
Figure 1
Image processing. (A) Freesurfer segmentation. (B) Regions of interest (ROIs) segmentation: Fronto-temporal region (blue); parieto-occipital region (white); corpus callosum and parasagittal white matter (red); brainstem (pons, midbrain, and medulla) (dark purple); basal ganglia (caudate, putamen, and pallidum); and thalamus (bright purple). (C) The ROIs are used to extract regional diffusion tensor imaging indices, i.e., fractional anisotropy (FA) and mean diffusion (MD). (D) Normal-appearing white matter. (E) Manual outline of mass lesions and traumatic micro bleeds. (F) Mask of outlined lesions. (G) FA map. (H) MD map.
Figure 2
Figure 2
Component loadings. Component loadings (Comp) for the two partial least squares regression analyses predicting post-traumatic amnesia (PTA) using fractional anisotropy (FA) (first row) and mean diffusion (MD) (second row). Above each plot is noted the percentages in parentheses of FA and MD variation explained by the respective components, Spearman r correlation and p value between the component scores and PTA. Red-colored bars are the deep mesial brain regions, and the blue-colored bars are the hemispheric brain regions. FT, fronto-temporal region; PO, parieto-occipital region; CC, corpus callosum, cingular and subcingular WM; BS, brainstem (pons, midbrain, and medulla); BT, basal ganglia (caudate, putamen, and pallidum) and thalamus.
Figure 3
Figure 3
Correlation between predicted and true post-traumatic amnesia (PTA) scores. This figure shows the correlation between predicted and true z score normalized PTA scores using partial least square regression with leave-one-out cross-validation. The rows represent analyses performed with fractional anisotropy (FA), FA and mean diffusion (MD), FA and micro bleed (MB) volume, and FA and MB count, respectively. The first column shows the correlation between true and predicted PTA scores and lists the Spearman correlation value as well as the permutation test p-value. Columns 2–4 show the component loadings for components 1–3, respectively. Red colored bars are the deep mesial brain regions, and the blue colored bars are the hemispheric brain regions. BS, brainstem (pons, midbrain, and medulla); BT, basal ganglia (caudate, putamen, and pallidum) and thalamus; CC, corpus callosum, cingular and subcingular white matter; FT, fronto-temporal region; PO, parieto-occipital region.

References

    1. Dewan MC, Rattani A, Gupta S, Baticulon RE, Hung YC, Punchak M, et al. . Estimating the global incidence of traumatic brain injury. J Neurosurg. (2018) 130:1080–97. 10.3171/2017.10.JNS17352
    1. Gennarelli TA, Thibault LE, Adams JH, Graham DI, Thompson CJ, Marcincin RP. Diffuse axonal injury and traumatic coma in primates. Ann Neurol. (1982) 12:564–74. 10.1002/ana.410120611
    1. Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ, Bruno MA, Boveroux P, Schnakers C, et al. . Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain. (2010) 133:161–71. 10.1093/brain/awp313
    1. Giacino JT, Fins JJ, Laureys S, Schiff ND. Disorders of consciousness after acquired brain injury: the state of the science. Nat Rev Neurol. (2014) 10:99–114. 10.1038/nrneurol.2013.279
    1. Ponsford JL, Spitz G, McKenzie D. Using post-traumatic amnesia to predict outcome after traumatic brain injury. J Neurotrauma. (2016) 33:997–1004. 10.1089/neu.2015.4025
    1. Di Perri C, Stender J, Laureys S, Gosseries O. Functional neuroanatomy of disorders of consciousness. Epilepsy Behav. (2014) 30:28–32. 10.1016/j.yebeh.2013.09.014
    1. Schnakers C, Vanhaudenhuyse A, Giacino J, Ventura M, Boly M, Majerus S, et al. . Diagnostic accuracy of the vegetative and minimally conscious state: clinical consensus versus standardized neurobehavioral assessment. BMC Neurol. (2009) 9:35. 10.1186/1471-2377-9-35
    1. Hulkower MB, Poliak DB, Rosenbaum SB, Zimmerman ME, Lipton ML. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am J Neuroradiol. (2013) 34:2064–74. 10.3174/ajnr.A3395
    1. Magnoni S, Mac Donald CL, Esparza TJ, Conte V, Sorrell J, Macri M, et al. . Quantitative assessments of traumatic axonal injury in human brain: concordance of microdialysis and advanced MRI. Brain. (2015) 138:2263–77. 10.1093/brain/awv152
    1. Hannawi Y, Stevens RD. Mapping the connectome following traumatic brain injury. Curr Neurol Neurosci Rep. (2016) 16:44. 10.1007/s11910-016-0642-9
    1. Hunter JV, Wilde EA, Tong KA, Holshouser BA. Emerging imaging tools for use with traumatic brain injury research. J Neurotrauma. (2012) 29:654–71. 10.1089/neu.2011.1906
    1. Wu YC, Mustafi SM, Harezlak J, Kodiweera C, Flashman LA, McAllister TW. Hybrid diffusion imaging in mild traumatic brain injury. J Neurotrauma. (2018) 35:2377–90. 10.1089/neu.2017.5566
    1. Castaño-Leon AM, Cicuendez M, Navarro-Main B, Munarriz PM, Paredes I, Cepeda S, et al. . Sixto Obrador SENEC prize 2019: utility of diffusion tensor imaging as a prognostic tool in moderate to severe traumatic brain injury. Part I. Analysis of DTI metrics performed during the early subacute stage. Neurocirugia. (2020). [Epub ahead of print]. 10.1016/j.neucie.2020.03.001
    1. Mukherjee P, Miller JH, Shimony JS, Philip JV, Nehra D, Snyder AZ, et al. . Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR Am J Neuroradiol. (2002) 23:1445–56.
    1. Nusbaum AE. Diffusion tensor MR imaging of gray matter in different multiple sclerosis phenotypes. AJNR Am J Neuroradiol. (2002) 23:899–900.
    1. Laitinen T, Sierra A, Pitkanen A, Grohn O. Diffusion tensor MRI of axonal plasticity in the rat hippocampus. Neuroimage. (2010) 51:521–30. 10.1016/j.neuroimage.2010.02.077
    1. Newcombe V, Chatfield D, Outtrim J, Vowler S, Manktelow A, Cross J, et al. . Mapping traumatic axonal injury using diffusion tensor imaging: correlations with functional outcome. PLoS ONE. (2011) 6:e19214. 10.1371/journal.pone.0019214
    1. Weston PS, Simpson IJ, Ryan NS, Ourselin S, Fox NC. Diffusion imaging changes in grey matter in Alzheimer's disease: a potential marker of early neurodegeneration. Alzheimers Res Ther. (2015) 7:47. 10.1186/s13195-015-0132-3
    1. Karlsen RH, Einarsen C, Moe HK, Håberg AK, Vik A, Skandsen T, et al. . Diffusion kurtosis imaging in mild traumatic brain injury and postconcussional syndrome. J Neurosci Res. (2019) 97:568–81. 10.1002/jnr.24383
    1. Newcombe VF, Correia MM, Ledig C, Abate MG, Outtrim JG, Chatfield D, et al. . Dynamic changes in white matter abnormalities correlate with late improvement and deterioration following TBI: a diffusion tensor imaging study. Neurorehabil Neural Repair. (2016) 30:49–62. 10.1177/1545968315584004
    1. Ware JB, Hart T, Whyte J, Rabinowitz A, Detre JA, Kim J. Inter-subject variability of axonal injury in diffuse traumatic brain injury. J Neurotrauma. (2017) 34:2243–53. 10.1089/neu.2016.4817
    1. Laureys S, Schiff ND. Coma and consciousness: paradigms. (re)framed by neuroimaging. Neuroimage. (2012) 61:478–91. 10.1016/j.neuroimage.2011.12.041
    1. Tononi G, Koch C. Consciousness: here, there and everywhere? Philos Trans R Soc Lond B Biol Sci. (2015) 370:1–18. 10.1098/rstb.2014.0167
    1. Demertzi A, Tagliazucchi E, Dehaene S, Deco G, Barttfeld P, Raimondo F, et al. . Human consciousness is supported by dynamic complex patterns of brain signal coordination. Sci Adv. (2019) 5:eaat7603. 10.1126/sciadv.aat7603
    1. Irimia A, Wang B, Aylward SR, Prastawa MW, Pace DF, Gerig G, et al. . Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction. Neuroimage Clin. (2012) 1:1–17. 10.1016/j.nicl.2012.08.002
    1. Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. (2012) 61:1000–16. 10.1016/j.neuroimage.2012.03.072
    1. Fagerholm ED, Hellyer PJ, Scott G, Leech R, Sharp DJ. Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain. (2015) 138:1696–709. 10.1093/brain/awv075
    1. Tariq M, Schneider T, Alexander DC, Gandini Wheeler-Kingshott CA, Zhang H. Bingham-NODDI: mapping anisotropic orientation dispersion of neurites using diffusion MRI. Neuroimage. (2016) 133:207–23. 10.1016/j.neuroimage.2016.01.046
    1. Solmaz B, Tunc B, Parker D, Whyte J, Hart T, Rabinowitz A, et al. . Assessing connectivity related injury burden in diffuse traumatic brain injury. Hum Brain Mapp. (2017) 38:2913–22. 10.1002/hbm.23561
    1. Wooten DW, Ortiz-Terán L, Zubcevik N, Zhang X, Huang C, Sepulcre J, et al. . Multi-modal signatures of tau pathology, neuronal fiber integrity, and functional connectivity in traumatic brain injury. J Neurotrauma. (2019) 36:3233–43. 10.1089/neu.2018.6178
    1. Ommaya AK. Head injury mechanisms and the concept of preventive management: a review and critical synthesis. J Neurotrauma. (1995) 12:527–46. 10.1089/neu.1995.12.527
    1. Gennarrelli TA, Graham DI. Neuropathology of the head injury. Semin Clin Neuropsychiatry. (1998) 3:160–75.
    1. Levin HS, Mendelsohn D, Lily MA, Yeakley J, Song J, Scheibell RS. Magnetic resonance imaging in relation to functional outcome of pediatric closed head injury: a test of the Ommaya-Gennarelli model. Neurosurgery. (1997) 433–41. 10.1097/00006123-199703000-00002
    1. Conde V, Andreasen SH, Petersen TH, Larsen KB, Madsen K, Andersen KW, et al. . Alterations in the brain's connectome during recovery from severe traumatic brain injury: protocol for a longitudinal prospective study. BMJ Open. (2017) 7:e016286. 10.1136/bmjopen-2017-016286
    1. Maas AI, Harrison-Felix CL, Menon D, Adelson PD, Balkin T, Bullock R, et al. . Standardizing data collection in traumatic brain injury. J Neurotrauma. (2011) 28:177–87. 10.1089/neu.2010.1617
    1. Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet. (1974) 2:81–4. 10.1016/S0140-6736(74)91639-0
    1. Maas AI, Hukkelhoven CW, Marshall LF, Steyerberg EW. Prediction of outcome in traumatic brain injury with computed tomographic characteristics: a comparison between the computed tomographic classification and combinations of computed tomographic predictors. Neurosurgery. (2005) 57:1173–82; discussion 1173-1182. 10.1227/01.NEU.0000186013.63046.6B
    1. Andreasen SH, Andersen KW, Conde V, Dyrby TB, Puonti O, Kammersgaard LP, et al. . Limited colocalization of microbleeds and microstructural changes after severe traumatic brain injury. J Neurotrauma. (2019) 37:581–92. 10.1089/neu.2019.6608
    1. Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn Reson Med. (2016) 76:1574–81. 10.1002/mrm.26054
    1. Andersson JL, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. (2003) 20:870–88. 10.1016/S1053-8119(03)00336-7
    1. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al. . Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. (2004) 23 (Suppl. 1):S208–219. 10.1016/j.neuroimage.2004.07.051
    1. Brooks DN, McKinlay W. Personality and behavioural change after severe blunt head injury–a relative's view. J Neurol Neurosurg Psychiatry. (1983) 46:336–44. 10.1136/jnnp.46.4.336
    1. Nakase-Richardson R, Sepehri A, Sherer M, Yablon SA, Evans C, Mani T. Classification schema of posttraumatic amnesia duration-based injury severity relative to 1-year outcome: analysis of individuals with moderate and severe traumatic brain injury. Arch Phys Med Rehabil. (2009) 90:17–9. 10.1016/j.apmr.2008.06.030
    1. Levin HS, O'Donnell VM, Grossman RG. The Galveston Orientation and Amnesia Test. A practical scale to assess cognition after head injury. J Nerv Ment Dis. (1979) 167:675–84. 10.1097/00005053-197911000-00004
    1. Bode RK, Heinemann AW, Semik P. Measurement properties of the Galveston Orientation and Amnesia Test (GOAT) and improvement patterns during inpatient rehabilitation. J Head Trauma Rehabil. (2000) 15:637–55. 10.1097/00001199-200002000-00004
    1. Hankemeier A, Rollnik JD. The Early Functional Abilities (EFA) scale to assess neurological and neurosurgical early rehabilitation patients. BMC Neurol. (2015) 15:207. 10.1186/s12883-015-0469-z
    1. Poulsen I, Kreiner S, Engberg AW. Validation of the early functional abilities scale: an assessment of four dimensions in early recovery after traumatic brain injury. J Rehabil Med. (2018) 50:165–72. 10.2340/16501977-2300
    1. Keith RA, Granger CV, Hamilton BB, Sherwin FS. The functional independence measure: a new tool for rehabilitation. Adv Clin Rehabil. (1987) 1:6–18.
    1. Corrigan JD, Smith-Knapp K, Granger CV. Validity of the functional independence measure for persons with traumatic brain injury. Arch Phys Med Rehabil. (1997) 78:828–34. 10.1016/S0003-9993(97)90195-7
    1. Madden DJ, Bennett IJ, Burzynska A, Potter GG, Chen NK, Song AW. Diffusion tensor imaging of cerebral white matter integrity in cognitive aging. Biochim Biophys Acta. (2012) 1822:386–400. 10.1016/j.bbadis.2011.08.003
    1. Sampaio-Baptista C, Johansen-Berg H. White matter plasticity in the adult brain. Neuron. (2017) 96:1239–51. 10.1016/j.neuron.2017.11.026
    1. Boulesteix AL, Strimmer K. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform. (2007) 8:32–44. 10.1093/bib/bbl016
    1. Kohavi R. A study of cross-validation and bootstrap for accuracy eastimation and model selection. In: International Joint Conference on Articial Intelligence (IJCAI) Montreal, QC: (1995). p. 1137−43.
    1. Huisman TA, Schwamm LH, Schaefer PW, Koroshetz WJ, Shetty-Alva N, Ozsunar Y, et al. . Diffusion tensor imaging as potential biomarker of white matter injury in diffuse axonal injury. AJNR Am J Neuroradiol. (2004) 25:370−6.
    1. Bendlin BB, Ries ML, Lazar M, Alexander AL, Dempsey RJ, Rowley HA, et al. . Longitudinal changes in patients with traumatic brain injury assessed with diffusion-tensor and volumetric imaging. Neuroimage. (2008) 42:503–14. 10.1016/j.neuroimage.2008.04.254
    1. Sidaros A, Engberg AW, Sidaros K, Liptrot MG, Herning M, Petersen P, et al. . Diffusion tensor imaging during recovery from severe traumatic brain injury and relation to clinical outcome: a longitudinal study. Brain. (2008) 131:559–72. 10.1093/brain/awm294
    1. Wang J. Longitudinal changes of structural connectivity in traumatic axonal injury. Neurology. (2011) 77:818–26. 10.1212/WNL.0b013e31822c61d7
    1. Betz J, Zhuo J, Roy A, Shanmuganathan K, Gullapalli RP. Prognostic value of diffusion tensor imaging parameters in severe traumatic brain injury. J Neurotrauma. (2012) 29:1292–305. 10.1089/neu.2011.2215
    1. Wallace EJ, Mathias JL, Ward L. The relationship between diffusion tensor imaging findings and cognitive outcomes following adult traumatic brain injury: A meta-analysis. Neurosci Biobehav Rev. (2018) 92:93–103. 10.1016/j.neubiorev.2018.05.023
    1. Andersen KW, Lasič SLH, Nilsson M, Topgaard D, Sellebjerg F, Szczepankiewicz F, et al. Disentangling white-matter damage from physiological fiber orientation dispersion in multiple sclerosis. Brain Commun. (2020) 14:114–23. 10.1093/braincomms/fcaa077
    1. Homos MD, Ali MT, Osman MF, Nabil DM. DTI metrics reflecting microstructural changes of normal appearing deep grey matter in multiple sclerosis. Egypt J Radiol Nucl Med. (2017) 48:1005–8. 10.1016/j.ejrnm.2017.04.012
    1. Beaulieu C. The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed. (2002) 15:435–55. 10.1002/nbm.782
    1. Behrens TE, Johansen-Berg H. Multiple fibers: beyond the diffusion tensor. In: Diffusion MRI. From Quantitative Measurement to in-vivo Neuroanatomy. 1st ed Cambridge, MA: Academic Press; Elsevier; (2009).
    1. Douglas DB, Iv M, Douglas PK, Anderson A, Vos SB, Bammer R, et al. . Diffusion tensor imaging of TBI: potentials and challenges. Top Magn Reson Imaging. (2015) 24:241–51. 10.1097/RMR.0000000000000062
    1. Johnson VE, Stewart W, Smith DH. Axonal pathology in traumatic brain injury. Exp Neurol. (2013) 246:35–43. 10.1016/j.expneurol.2012.01.013
    1. Grassi DC, Conceicao DMD, Leite CDC, Andrade CS. Current contribution of diffusion tensor imaging in the evaluation of diffuse axonal injury. Arq Neuropsiquiatr. (2018) 76:189–99. 10.1590/0004-282x20180007
    1. Ommaya AK, Gennarelli TA. Cerebral concussion and traumatic unconsciousness. Correlation of experimental and clinical observations of blunt head injuries. Brain. (1974) 97:633–54. 10.1093/brain/97.1.633
    1. Adams JH. Diffuse axonal injury in head injury: definition, diagnosis and grading. Histopathol Vol. (1989) 15:49–59. 10.1111/j.1365-2559.1989.tb03040.x
    1. Parvizi J, Damasio AR. Neuroanatomical correlates of brainstem coma. Brain. (2003) 126:1524–36. 10.1093/brain/awg166
    1. Fuller PM, Sherman D, Pedersen NP, Saper CB, Lu J. Reassessment of the structural basis of the ascending arousal system. J Comp Neurol. (2011) 519:933–56. 10.1002/cne.22559
    1. Patrick PD, Mabry JL, Gurka MJ, Buck ML, Boatwright E, Blackman JA. MRI patterns in prolonged low response states following traumatic brain injury in children and adolescents. Brain Inj. (2007) 21:63–8. 10.1080/02699050601111401
    1. Mannion RJ, Cross J, Bradley P, Coles JP, Chatfield D, Carpenter A, et al. . Mechanism-based MRI classification of traumatic brainstem injury and its relationship to outcome. J Neurotrauma. (2007) 24:128–35. 10.1089/neu.2006.0127
    1. Laureys S, Celesia GG, Cohadon F, Lavrijsen J, Leon-Carrion J, Sannita WG, et al. . Unresponsive wakefulness syndrome: a new name for the vegetative state or apallic syndrome. BMC Med. (2010) 8:68. 10.1186/1741-7015-8-68
    1. Schiff ND. Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends Neurosci. (2010) 33:1–9. 10.1016/j.tins.2009.11.002
    1. Fernandez-Espejo D, Soddu A, Cruse D, Palacios EM, Junque C, Vanhaudenhuyse A, et al. . A role for the default mode network in the bases of disorders of consciousness. Ann Neurol. (2012) 72:335–43. 10.1002/ana.23635
    1. Lant ND, Gonzalez-Lara LE, Owen AM, Fernandez-Espejo D. Relationship between the anterior forebrain mesocircuit and the default mode network in the structural bases of disorders of consciousness. Neuroimage Clin. (2016) 10:27–35. 10.1016/j.nicl.2015.11.004
    1. Fernandez-Espejo D, Bekinschtein T, Monti MM, Pickard JD, Junque C, Coleman MR, et al. . Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state. Neuroimage. (2011) 54:103–12. 10.1016/j.neuroimage.2010.08.035
    1. Griffin AD, Turtzo LC, Parikh GY, Tolpygo A, Lodato Z, Moses AD, et al. . Traumatic microbleeds suggest vascular injury and predict disability in traumatic brain injury. Brain. (2019) 142:3550–64. 10.1093/brain/awz290
    1. Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, et al. . Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. (2012) 117:1300–10. 10.1097/ALN.0b013e3182755558
    1. Cicuendez M, Castano-Leon A, Ramos A, Hilario A, Gomez PA, Lagares A. The added prognostic value of MR imaging in traumatic brain injury: the importance of TAI lesions when performing an ordinal logistic regression. J Neuroradiol. (2018) 46:299–306. 10.1016/j.neurad.2018.08.001
    1. Maeda Y, Ichikawa R, Misawa J, Shibuya A, Hishiki T, Maeda T, et al. . External validation of the TRISS, CRASH, and IMPACT prognostic models in severe traumatic brain injury in Japan. PLoS ONE. (2019) 14:e0221791. 10.1371/journal.pone.0221791
    1. Smith AB, Smirniotopoulos JG, Rushing EJ, Goldstein SJ. Bilateral thalamic lesions. AJR Am J Roentgenol. (2009) 192:W53–62. 10.2214/AJR.08.1585
    1. Koch C, Massimini M, Boly M, Tononi G. Neural correlates of consciousness: progress and problems. Nat Rev Neurosci. (2016) 17:307–21. 10.1038/nrn.2016.22

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