Disorders of consciousness (DOC) may be characterized by the degree at which consciousness is impaired, and include for example vegetative state (VS) and minimally conscious state (MCS) patients. Using a reliable marker as a measure of the level of consciousness in such patients is of utmost necessity and importance for their appropriate diagnosis and prognosis. Identification of VS and MCS states based on their behaviors sometimes leads to incorrect inferences due to the influence of a range of factors like motor impairment, fluctuating arousal levels and rapidly habituating responses to name a few.1 The extent of damage in the thalamus, a structure known for its role in arousal regulation, may provide an imaging biomarker to better differentiate between VS and MCS. In this study, we manually segmented the thalamus from T1-weighted brain MRI images in a large cohort of 19 VS and 23 MCS subjects that were examined using the French version of the Coma Recovery Scale Revised (CRS-R).2 This scale is the most trustworthy behavioural diagnosis tool3 for patients with DOC available. The aim was to determine whether a relationship between thalamus volume and consciousness level exists. Results show that total thalamic volume tends to decrease over time after a severe brain injury. Moreover, for subjects in chronic state, the thalamic volume seems to differ with respect to the degree of consciousness that was diagnosed. Finally, for these same chronic patients, the total thalamic volume is varying linearly as a function of the CRS-R score obtained, indicating that thalamic volume may be used as a biomarker to measure the level of consciousness.
Disorders of consciousness (DOC) are a consequence of a variety of severe brain injuries. DOC commonly results in anatomical brain modifications, which can affect cortical and sub-cortical brain structures. Postmortem studies suggest that severity of brain damage correlates with level of impairment in DOC. In-vivo studies in neuroimaging mainly focus in alterations on single structures. Recent evidence suggests that rather than one, multiple brain regions can be simultaneously affected by this condition. In other words, DOC may be linked to an underlying cerebral network of structural damage. Recently, geometrical spatial relationships among key sub-cortical brain regions, such as left and right thalamus and brain stem, have been used for the characterization of this network. This approach is strongly supported on automatic segmentation processes, which aim to extract regions of interests without human intervention. Nevertheless, patients with DOC usually present massive structural brain changes. Therefore, segmentation methods may highly influence the characterization of the underlying cerebral network structure. In this work, we evaluate the level of characterization obtained by using the spatial relationships as descriptor of a sub-cortical cerebral network (left and right thalamus) in patients with DOC, when different segmentation approaches are used (FSL, Free-surfer and manual segmentation). Our results suggest that segmentation process may play a critical role for the construction of robust and reliable structural characterization of DOC conditions.
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