4.7 Article

Diffusion weighted imaging distinguishes the vegetative state from the minimally conscious state

期刊

NEUROIMAGE
卷 54, 期 1, 页码 103-112

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2010.08.035

关键词

Vegetative state; Minimally conscious state; Diffusion tensor imaging; Magnetic resonance imaging

资金

  1. National Institute for Health Research Biomedical Research Centre at Cambridge
  2. UK Department of Health Technology Platform
  3. U.K. Medical Research Council [U.1055.01.002.00007.01]
  4. James S. McDonnell Foundation
  5. Spanish Ministry for Education [AP2006-00862]
  6. Spanish Ministry of Science and Innovation [SAF2007-66077]
  7. Medical Research Council [MC_U105559847, G9439390, MC_EX_G0800771, G0600986, G0001237] Funding Source: researchfish
  8. National Institute for Health Research [NF-SI-0508-10327] Funding Source: researchfish
  9. MRC [G9439390, MC_U105559847, MC_EX_G0800771, G0001237, G0600986] Funding Source: UKRI

向作者/读者索取更多资源

The vegetative (VS) and minimally conscious (MCS) states are currently distinguished on the basis of exhibited behaviour rather than underlying pathology. Although previous histopathological studies have documented different degrees of diffuse axonal injury as well as damage to the thalami and brainstem regions in VS and MCS, these differences have not been assessed in vivo, and therefore, do not provide a measurable pathological marker to aid clinical diagnosis. Currently, the diagnostic decision-making process is highly subjective and prone to error. Indeed, previous work has suggested that up to 43% of patients in this group may be misdiagnosed. We used diffusion tensor imaging (DTI) to study the neuropathology of 25 vegetative and minimally conscious patients in vivo and to identify measures that could potentially distinguish the patients in these two groups. Mean diffusivity (MD) maps of the subcortical white matter, brainstem and thalami were generated. The MCS and VS patients differed significantly in subcortical white matter and thalamic regions, but appeared not to differ in the brainstem. Moreover, the DTI results predicted scores on the Coma Recovery Scale (p<0.001) and successfully classified the patients in to their appropriate diagnostic categories with an accuracy of 95%. The results suggest that this method may provide an objective and highly accurate method for classifying these challenging patient populations and may therefore complement the behavioural assessment to inform the diagnostic decision making process. (C) 2010 Elsevier Inc. All rights reserved.

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