Neuroscience Bulletin

, Volume 34, Issue 4, pp 668–678 | Cite as

White Matter Deficits Underlying the Impaired Consciousness Level in Patients with Disorders of Consciousness

  • Xuehai Wu
  • Jiaying Zhang
  • Zaixu Cui
  • Weijun Tang
  • Chunhong Shao
  • Jin Hu
  • Jianhong Zhu
  • Yao Zhao
  • Lu Lu
  • Gang Chen
  • Georg Northoff
  • Gaolang Gong
  • Ying Mao
  • Yong He
Original Article


In this study, we aimed to (1) identify white matter (WM) deficits underlying the consciousness level in patients with disorders of consciousness (DOCs) using diffusion tensor imaging (DTI), and (2) evaluate the relationship between DTI metrics and clinical measures of the consciousness level in DOC patients. With a cohort of 8 comatose, 8 unresponsive wakefulness syndrome/vegetative state, and 14 minimally conscious state patients and 25 patient controls, we performed group comparisons of the DTI metrics in 48 core WM regions of interest (ROIs), and examined the clinical relevance using correlation analysis. We identified multiple abnormal WM ROIs in DOC patients compared with normal controls, and the DTI metrics in these ROIs were significantly correlated with clinical measures of the consciousness level. Therefore, our findings suggested that multiple WM tracts are involved in the impaired consciousness levels in DOC patients and demonstrated the clinical relevance of DTI for DOC patients.


Disorder of consciousness White matter Diffusion tensor imaging Brain injury 



This work was supported by the Natural Science Foundation of China (81571025), International Cooperation Project from Shanghai Science Foundation (18410711300), the National Science Foundation for Distinguished Young Scholars of China (81025013), National Basic Research Development Program (973 Program) of China (2012CB720700, 2010CB945500, 2012CB966300, and 2009CB941100), the National Natural Science Foundation of China (81322021), the Beijing Nova Program (Z121110002512032), the Project for National 985 Engineering of China (985III-YFX0102), the “Dawn Tracking” Program of Shanghai Education Commission (10GG01), the Shanghai Natural Science Foundation (08411952000 and 10ZR1405400), the National Natural Science Young Foundation in China (81201033), the grants of Shanghai Health Bureau (20114358), the National High-Technology Development Project (863 Project) of China (2015AA020501), the Program for New Century Excellent Talents in University of China (NCET-10-0356), and the National Program for the Support of Top-Notch Young Professionals. Dr. Georg Northoff is supported by the Michael Smith Foundation, the CRC, and the CIHR. Jiaying Zhang is supported by the China Scholarship Council.

Compliance with ethical standards

Conflict of interest

All authors claim that there are no conflicts of interest.

Supplementary material

12264_2018_253_MOESM1_ESM.pdf (690 kb)
Supplementary material 1 (PDF 690 kb)


  1. 1.
    Owen AM, Coleman MR, Boly M, Davis MH, Laureys S, Pickard JD. Detecting awareness in the vegetative state. Science 2006, 313: 1402.CrossRefPubMedGoogle Scholar
  2. 2.
    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.CrossRefPubMedGoogle Scholar
  3. 3.
    Boly M, Tshibanda L, Vanhaudenhuyse A, Noirhomme Q, Schnakers C, Ledoux D, et al. Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp 2009, 30: 2393–2400.CrossRefPubMedGoogle Scholar
  4. 4.
    Boly M, Faymonville M-E, Schnakers C, Peigneux P, Lambermont B, Phillips C, et al. Perception of pain in the minimally conscious state with PET activation: an observational study. Lancet Neurol 2008, 7: 1013–1020.CrossRefPubMedGoogle Scholar
  5. 5.
    Vanhaudenhuyse A, Noirhomme Q, Tshibanda LJ-F, Bruno M-A, Boveroux P, Schnakers C, et al. Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain 2010, 133: 161–171.CrossRefPubMedGoogle Scholar
  6. 6.
    Qin P, Wu X, Huang Z, Duncan NW, Tang W, Wolff A, et al. How are different neural networks related to consciousness? Ann Neurol 2015, 78: 594–605.CrossRefPubMedGoogle Scholar
  7. 7.
    Wu X, Zou Q, Hu J, Tang W, Mao Y, Gao L, et al. Intrinsic functional connectivity patterns predict consciousness level and recovery outcome in acquired brain injury. J Neurosci 2015, 35: 12932–12946.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B 1996, 111: 209–219.CrossRefPubMedGoogle Scholar
  9. 9.
    Voss HU, Uluğ AM, Dyke JP, Watts R, Kobylarz EJ, McCandliss BD, et al. Possible axonal regrowth in late recovery from the minimally conscious state. J Clin Invest 2006, 116: 2005–2011.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Fernández-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: 335–343.CrossRefPubMedGoogle Scholar
  11. 11.
    Fernández-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: 103–112.CrossRefPubMedGoogle Scholar
  12. 12.
    Lant ND, Gonzalez-Lara LE, Owen AM, Fernández-Espejo D. Relationship between the anterior forebrain mesocircuit and the default mode network in the structural bases of disorders of consciousness. Neuroimage Clin 2015, 10: 27–35.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Newcombe VFJ, Williams GB, Scoffings D, Cross J, Carpenter TA, Pickard JD, et al. Aetiological differences in neuroanatomy of the vegetative state: insights from diffusion tensor imaging and functional implications. J Neurol Neurosurg Psychiatr 2010, 81: 552–561.CrossRefGoogle Scholar
  14. 14.
    Posner JB, Plum F. Plum and Posner’s Diagnosis of Stupor and Coma. 4th ed. New York: Oxford University Press, 2007.Google Scholar
  15. 15.
    PVS TM-STF on. Medical aspects of the persistent vegetative state. New Engl J Med 1994, 330: 1572–1579.CrossRefGoogle Scholar
  16. 16.
    Giacino JT, Ashwal S, Childs N, Cranford R, Jennett B, Katz DI, et al. The minimally conscious state: definition and diagnostic criteria. Neurology 2002, 58: 349–353.CrossRefPubMedGoogle Scholar
  17. 17.
    Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet 1974, 2: 81–84.CrossRefPubMedGoogle Scholar
  18. 18.
    Giacino JT, Kalmar K, Whyte J. The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil 2004, 85: 2020–2029.CrossRefPubMedGoogle Scholar
  19. 19.
    Cui Z, Zhong S, Xu P, Gong G, He Y. PANDA: a pipeline toolbox for analyzing brain diffusion images. Front Hum Neurosci 2013, 7: 42.PubMedPubMedCentralGoogle Scholar
  20. 20.
    Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage 2012, 62: 782–790.CrossRefPubMedGoogle Scholar
  21. 21.
    Winklewski PJ, Sabisz A, Naumczyk P, Jodzio K, Szurowska E, Szarmach A. Understanding the physiopathology behind axial and radial diffusivity changes—What do we know? Front Neurol 2018, 9: 92.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Mori S, Oishi K, Jiang H, Jiang L, Li X, Akhter K, et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. Neuroimage 2008, 40: 570–582.CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Faria AV, Zhang J, Oishi K, Li X, Jiang H, Akhter K, et al. Atlas-based analysis of neurodevelopment from infancy to adulthood using diffusion tensor imaging and applications for automated abnormality detection. Neuroimage 2010, 52: 415–428.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage 2003, 20: 1714–1722.CrossRefPubMedGoogle Scholar
  25. 25.
    Kier EL, Staib LH, Davis LM, Bronen RA. MR imaging of the temporal stem: anatomic dissection tractography of the uncinate fasciculus, inferior occipitofrontal fasciculus, and Meyer’s loop of the optic radiation. Am J Neuroradiol 2004, 25: 677–691.PubMedGoogle Scholar
  26. 26.
    Burzynska AZ, Jiao Y, Knecht AM, Fanning J, Awick EA, Chen T, et al. White matter integrity declined over 6-months, but dance intervention improved integrity of the fornix of older adults. Front Aging Neurosci 2017, 9: 59.CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Moruzzi G, Magoun HW. Brain stem reticular formation and activation of the EEG. Electroencephalogr Clin Neurophysiol 1949, 1: 455–473.CrossRefPubMedGoogle Scholar
  28. 28.
    Northoff G. Unlocking the Brain: Volume 2: Consciousness. New York: Oxford University Press, 2014.Google Scholar
  29. 29.
    Parvizi J, Damasio AR. Neuroanatomical correlates of brainstem coma. Brain 2003, 126: 1524–1536.CrossRefPubMedGoogle Scholar
  30. 30.
    Starzl TE, Taylor CW, Magoun HW. Ascending conduction in reticular activating system, with special reference to the diencephalon. J Neurophysiol 1951, 14: 461–477.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Delano-Wood L, Bangen KJ, Sorg SF, Clark AL, Schiehser DM, Luc N, et al. Brainstem white matter integrity is related to loss of consciousness and postconcussive symptomatology in veterans with chronic mild to moderate traumatic brain injury. Brain Imaging Behav 2015, 9: 500–512.CrossRefPubMedGoogle Scholar
  32. 32.
    Monti MM, Vanhaudenhuyse A, Coleman MR, Boly M, Pickard JD, Tshibanda L, et al. Willful modulation of brain activity in disorders of consciousness. New Engl J Med 2010, 362: 579–589.CrossRefPubMedGoogle Scholar
  33. 33.
    Laureys S, Schiff ND. Coma and consciousness: Paradigms (re)framed by neuroimaging. Neuroimage 2012, 61: 478–491.CrossRefPubMedGoogle Scholar
  34. 34.
    Wedeen VJ, Wang RP, Schmahmann JD, Benner T, Tseng WYI, Dai G, et al. Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 2008, 41: 1267–1277.CrossRefPubMedGoogle Scholar
  35. 35.
    Dai JK, Wang SX, Shan D, Niu HC, Lei H. Super-resolution track-density imaging reveals fine anatomical features in tree shrew primary visual cortex and hippocampus. Neurosci Bull 2017, 1: 1–11.Google Scholar
  36. 36.
    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.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Shanghai Institutes for Biological Sciences, CAS and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Neurosurgical DepartmentShanghai Huashan Hospital, Fudan UniversityShanghaiChina
  2. 2.State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
  3. 3.Centre for Medical Image Computing, Department of Computer ScienceUniversity College LondonLondonUK
  4. 4.Radiological DepartmentShanghai Huashan Hospital, Fudan UniversityShanghaiChina
  5. 5.Psychiatry DepartmentShanghai Huashan Hospital, Fudan UniversityShanghaiChina
  6. 6.Huajia HospitalShanghaiChina
  7. 7.Scientific and Statistical Computing CoreNational Institute of Mental Health, National Institutes of Health, Department of Health and Human ServicesBethesdaUSA
  8. 8.Institute of Mental Health ResearchUniversity of OttawaOttawaCanada

Personalised recommendations