Location of Subcortical Microbleeds and Recovery of Consciousness After Severe Traumatic Brain Injury

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Abstract

Background

In patients with severe traumatic brain injury (TBI), coma is associated with impaired subcortical arousal mechanisms. However, it is unknown which nuclei involved in arousal (arousal nuclei) are implicated in coma pathogenesis and are compatible with coma recovery.

Methods

We mapped an atlas of arousal nuclei in the brainstem, thalamus, hypothalamus, and basal forebrain onto 3 tesla susceptibility-weighted images (SWI) in 12 patients with acute severe TBI who presented in coma and recovered consciousness within 6 months. We assessed the spatial distribution and volume of SWI microbleeds and evaluated the association of microbleed volume with the duration of unresponsiveness and functional recovery at 6 months.

Results

There was no single arousal nucleus affected by microbleeds in all patients. Rather, multiple combinations of microbleeds in brainstem, thalamic, and hypothalamic arousal nuclei were associated with coma and were compatible with recovery of consciousness. Microbleeds were frequently detected in the midbrain (100%), thalamus (83%), and pons (75%). Within the brainstem, the microbleed incidence was largest within the mesopontine tegmentum (e.g., pedunculotegmental nucleus, mesencephalic reticular formation) and ventral midbrain (e.g., substantia nigra, ventral tegmental area). Brainstem arousal nuclei were partially affected by microbleeds, with microbleed volume not exceeding 35% of brainstem nucleus volume on average. Compared to microbleed volume within nonarousal brainstem regions, the microbleed volume within arousal brainstem nuclei accounted for a larger proportion of variance in the duration of unresponsiveness and 6-month Glasgow Outcome Scale–Extended scores.

Conclusion

These results suggest resilience of arousal mechanisms in the human brain after severe TBI.

Related collections

Most cited references 45

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An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.

Rahul Desikan, Florent Ségonne, Bruce Fischl … (2006)

In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

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Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature.

Christophe Destrieux, Bruce Fischl, Anders Dale … (2010)

Precise localization of sulco-gyral structures of the human cerebral cortex is important for the interpretation of morpho-functional data, but requires anatomical expertise and is time consuming because of the brain's geometric complexity. Software developed to automatically identify sulco-gyral structures has improved substantially as a result of techniques providing topologically correct reconstructions permitting inflated views of the human brain. Here we describe a complete parcellation of the cortical surface using standard internationally accepted nomenclature and criteria. This parcellation is available in the FreeSurfer package. First, a computer-assisted hand parcellation classified each vertex as sulcal or gyral, and these were then subparcellated into 74 labels per hemisphere. Twelve datasets were used to develop rules and algorithms (reported here) that produced labels consistent with anatomical rules as well as automated computational parcellation. The final parcellation was used to build an atlas for automatically labeling the whole cerebral cortex. This atlas was used to label an additional 12 datasets, which were found to have good concordance with manual labels. This paper presents a precisely defined method for automatically labeling the cortical surface in standard terminology. Copyright 2010 Elsevier Inc. All rights reserved.