Combination of diffusion tensor and functional magnetic resonance imaging during recovery from the vegetative state

Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain.
BMC Neurology (Impact Factor: 2.49). 09/2010; 10:77. DOI: 10.1186/1471-2377-10-77
Source: PubMed

ABSTRACT The rate of recovery from the vegetative state (VS) is low. Currently, little is known of the mechanisms and cerebral changes that accompany those relatively rare cases of good recovery. Here, we combined functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) to study the evolution of one VS patient at one month post-ictus and again twelve months later when he had recovered consciousness.
fMRI was used to investigate cortical responses to passive language stimulation as well as task-induced deactivations related to the default-mode network. DTI was used to assess the integrity of the global white matter and the arcuate fasciculus. We also performed a neuropsychological assessment at the time of the second MRI examination in order to characterize the profile of cognitive deficits.
fMRI analysis revealed anatomically appropriate activation to speech in both the first and the second scans but a reduced pattern of task-induced deactivations in the first scan. In the second scan, following the recovery of consciousness, this pattern became more similar to that classically described for the default-mode network. DTI analysis revealed relative preservation of the arcuate fasciculus and of the global normal-appearing white matter at both time points. The neuropsychological assessment revealed recovery of receptive linguistic functioning by 12-months post-ictus.
These results suggest that the combination of different structural and functional imaging modalities may provide a powerful means for assessing the mechanisms involved in the recovery from the VS.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: BACKGROUND: Progress in neuroimaging has yielded new powerful tools which, potentially, can be applied to clinical populations, improve the diagnosis of neurological disorders and predict outcome. At present, the diagnosis of consciousness disorders is limited to subjective assessment and objective measurements of behavior, with an emerging role for neuroimaging techniques. In this review we focus on white matter alterations measured using Diffusion Tensor Imaging on patients with consciousness disorders, examining the most common diffusion imaging acquisition protocols and considering the main issues related to diffusion imaging analyses. We conclude by considering some of the remaining challenges to overcome, the existing knowledge gaps and the potential role of neuroimaging in understanding the pathogenesis and clinical features of disorders of consciousness.
    Frontiers in Human Neuroscience 01/2015; 8. DOI:10.3389/fnhum.2014.01028 · 2.90 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In the past 15 years, rapid technological development in the field of neuroimaging has led to a resurgence of interest in the study of consciousness. However, the neural bases of consciousness and the boundaries of unconscious processing remain poorly understood. Anesthesia combined with functional neuroimaging presents a unique approach for studying neural responses as a function of consciousness. In this review we summarize findings from functional neuroimaging studies that have used anesthetic drugs to study cognition at different levels of conscious awareness. We relate the results to those of psychophysical studies of cognition and explore their potential usefulness in interpreting clinical findings from studies of non-responsive patients. Copyright © 2014 Elsevier Ltd. All rights reserved.
    Trends in Cognitive Sciences 01/2015; 19(2). DOI:10.1016/j.tics.2014.12.005 · 21.15 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The intrinsic connectivity of the default mode network has been associated with the level of consciousness in patients with severe brain injury. Especially medial parietal regions are considered to be highly involved in impaired consciousness. To better understand what aspect of this intrinsic architecture is linked to consciousness, we applied spectral dynamic causal modeling to assess effective connectivity within the default mode network in patients with disorders of consciousness. We included 12 controls, 12 patients in minimally conscious state and 13 in vegetative state in this study. For each subject, we first defined the four key regions of the default mode network employing a subject-specific independent component analysis approach. The resulting regions were then included as nodes in a spectral dynamic causal modeling analysis in order to assess how the causal interactions across these regions as well as the characteristics of neuronal fluctuations change with the level of consciousness. The resulting pattern of interaction in controls identified the posterior cingulate cortex as the main driven hub with positive afferent but negative efferent connections. In patients, this pattern appears to be disrupted. Moreover, the vegetative state patients exhibit significantly reduced self-inhibition and increased oscillations in the posterior cingulate cortex compared to minimally conscious state and controls. Finally, the degree of self-inhibition and strength of oscillation in this region is correlated with the level of consciousness. These findings indicate that the equilibrium between excitatory connectivity towards posterior cingulate cortex and its feedback projections is a key aspect of the relationship between alterations in consciousness after severe brain injury and the intrinsic functional architecture of the default mode network. This impairment might be principally due to the disruption of the mechanisms underlying self-inhibition and neuronal oscillations in the posterior cingulate cortex. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 01/2015; 53. DOI:10.1016/j.neuroimage.2015.01.037 · 6.13 Impact Factor

Full-text (5 Sources)

Available from
Jun 3, 2014