Functional Networks in Disorders of Consciousness

Abstract

Severe brain injury may cause disruption of neural networks that sustain arousal and awareness, the two essential components of consciousness. Despite the potentially devastating immediate and long-term consequences, disorders of consciousness (DoC) are poorly understood in terms of their underlying neurobiology, the relationship between pathophysiology and recovery, and the predictors of treatment efficacy. Recent advances in neuroimaging techniques have enabled the study of network connectivity, providing great potential to improve the clinical care of patients with DoC. Initial discoveries in this field were made using positron emission tomography (PET). More recently, functional magnetic resonance (fMRI) techniques have added to our understanding of functional network dynamics in this population. Both methods have shown that whether at rest or performing a goal-oriented task, functional networks essential for processing intrinsic thoughts and extrinsic stimuli are disrupted in patients with DoC compared with healthy subjects. Atypical connectivity has been well established in the default mode network as well as in other cortical and subcortical networks that may be required for consciousness. Moreover, the degree of altered connectivity may be related to the severity of impaired consciousness, and recovery of consciousness has been shown to be associated with restoration of connectivity. In this review, we discuss PET and fMRI studies of functional and effective connectivity in patients with DoC and suggest how this field can move toward clinical application of functional network mapping in the future.

Conflict of interest statement

Conflict of Interest: The authors report no conflict of interest.

Figures

Figure 1

H215O-PET (Positron emission tomography) analysis of functional connectivity in disorders of consciousness (adapted from Laureys et al 2000). The top row shows cortical regions (prefrontal and anterior cingulate cortices) where functional connectivity (indicated by red arrows) with intralaminar nuclei of both thalami (dashed circle) was different between a patient in VS/UWS and healthy subjects. These differences resolved when the patient recovered consciousness. The bottom row shows the metabolic relationship between both thalami and right prefrontal cortex in healthy subjects (green circles), compared with a patient in VS/UWS (red crosses) and after recovery (blue asterisks). This relationship appears to have normalized when the patient recovered from VS/UWS.

Figure 2

Overview of cortical resting state networks whose disruption is implicated in the pathogenesis of DoC. All functional network nodes are from the Yeo 2011 Atlas and rendered using FreeSurfer FreeView visualization software. For the attention networks, the dorsal attention network is comprised of the green nodes and the ventral attention network is comprised of the violet nodes.

Figure 3

Resting-state functional MRI (rsfMRI) analysis of default mode network (DMN) connectivity in a comatose patient. DMN connectivity was identified using a seed in the left posterior cingulate cortex (PCC). The functional connectivity map is superimposed on the patient’s diffusion-weighted images. The patient was a 55-year-old woman who was scanned six days after an aneurysmal subarachnoid hemorrhage, which resulted in intracranial hypertension and bilateral ischemic strokes involving the anterior cerebral artery territories (hyperintensities, arrow). Her Coma Recovery Scale-Revised score was 1 and Glasgow Coma Scale score was 5T (Eyes=1, Motor=3, Verbal=1T) at the time of the scan, indicating coma. She was sedated with a continuous infusion of propofol throughout the scan. Despite her comatose state and administration of propofol, DMN analysis revealed partial preservation of DMN functional connectivity, specifically between the bilateral PCC, precuneus (Pr), inferior parietal lobules (IPL) and retrosplenial cortex (RSC). Connectivity between the PCC and the medial prefrontal cortex (MPFC) was absent. Color maps represent the spatial distribution of positive correlation coefficients thresholded at ≥ 0.3. RsfMRI data were acquired on a 3 Tesla Siemens Skyra MRI scanner (Siemens Medical Solutions; Erlangen, Germany) using a 32-channel head coil. The rsfMRI sequence utilized 3 mm isotropic voxels with TR = 2.4 s and 150 total volumes. Functional connectivity data were processed using CONN (http://www.nitrc.org/projects/conn).

Figure 4

Resting-state functional MRI (rsfMRI) analysis of brainstem-cortical connectivity in a patient whose behavioral diagnosis suggested a vegetative state/unresponsive wakefulness syndrome (VS/UWS; top row). The patient’s connectivity data are compared to a healthy subject’s connectivity results (bottom row). Connectivity was identified using a seed in the ventral tegmental area (VTA), which is a dopaminergic arousal nucleus known to activate the cerebral cortex. The patient was a 46-year-old man who was scanned seven days after an ischemic stroke involving the basilar artery territory, which resulted in infarction of the basis pontis (arrows) and multiple regions of the ponto-mesencephalic tegmentum. His initial exams were consistent with a locked-in syndrome, but at the time of the rsfMRI scan his Coma Recovery Scale-Revised score was 1 and Glasgow Coma Scale score was 4T (Eyes=2, Motor=1, Verbal=1T), indicating VS/UWS. He was sedated with a continuous infusion of low-dose propofol throughout the scan. Despite his behavioral diagnosis of VS/UWS and administration of propofol, connectivity appeared preserved between the VTA and the medial prefrontal cortex (MPFC). This observation was consistent with the neuroanatomic localization of the infarct, which spared the VTA. Color maps represent the spatial distribution of positive correlation coefficients thresholded at ≥ 0.3. RsfMRI acquisition parameters were the same as those reported in Figure 3. Functional connectivity data were processed using CONN (http://www.nitrc.org/projects/conn) and the resulting connectivity maps were superimposed on each subject’s T1-weighted MPRAGE dataset.