Extending communication for patients with disorders of consciousness

Evaluation of consciousness rehabilitation via neuroimaging methods

Accurate evaluation of patients with disorders of consciousness (DoC) is crucial for personalized treatment. However, misdiagnosis remains a serious issue. Neuroimaging methods could observe the conscious activity in patients who have no evidence of consciousness in behavior, and provide objective and quantitative indexes to assist doctors in their diagnosis. In the review, we discussed the current research based on the evaluation of consciousness rehabilitation after DoC using EEG, fMRI, PET, and fNIRS, as well as the advantages and limitations of each method. Nowadays single-modal neuroimaging can no longer meet the researchers` demand. Considering both spatial and temporal resolution, recent studies have attempted to focus on the multi-modal method which can enhance the capability of neuroimaging methods in the evaluation of DoC. As neuroimaging devices become wireless, integrated, and portable, multi-modal neuroimaging methods will drive new advancements in brain science research.

Language Assessment in Patients with Disorders of Consciousness

The assessment of residual language abilities in patients with disorders of consciousness (DoC) after severe brain injury is particularly challenging due to their limited behavioral repertoire. Moreover, associated language impairment such as receptive aphasia may lead to an underestimation of actual consciousness levels. In this review, we examine past research on the assessment of residual language processing in DoC patients, and we discuss currently available tools for identifying language-specific abilities and their prognostic value. We first highlight the need for validated and sensitive bedside behavioral assessment tools for residual language abilities in DoC patients. As regards neuroimaging and electrophysiological methods, the tasks involving higher level linguistic commands appear to be the most informative about level of consciousness and have the best prognostic value. Neuroimaging methods should be combined with the most appropriate behavioral tools in multimodal assessment protocols to assess receptive language abilities in DoC patients in the most complete and sensitive manner.

Residual implicit and explicit language abilities in patients with disorders of consciousness: A systematic review

Language assessment in post-comatose patients is difficult due to their limited behavioral repertoire; yet associated language deficits might lead to an underestimation of consciousness levels in unresponsive wakefulness syndrome (UWS) or minimally conscious state (MCS; -/+) diagnoses. We present a systematic review of studies from 2002 assessing residual language abilities with neuroimaging, electrophysiological or behavioral measures in patients with severe brain injury. Eighty-five articles including a total of 2278 patients were assessed for quality. The median percentages of patients showing residual implicit language abilities (i.e., cortical responses to specific words/sentences) were 33 % for UWS, 50 % for MCS- and 78 % for MCS + patients, whereas explicit language abilities (i.e., command-following using brain-computer interfaces) were reported in 20 % of UWS, 33 % of MCS- and 50 % of MCS + patients. Cortical responses to verbal stimuli increased along with consciousness levels and the progressive recovery of consciousness after a coma was paralleled by the reappearance of both implicit and explicit language processing. This review highlights the importance of language assessment in patients with disorders of consciousness.

Electrophysiological and Neuroimaging Studies - During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review

A severe brain injury may lead to a disorder of consciousness (DOC) such as coma, vegetative state (VS), minimally conscious state (MCS) or locked-in syndrome (LIS). Till date, the diagnosis of DOC relies only on clinical evaluation or subjective scoring systems such as Glasgow coma scale, which fails to detect subtle changes and thereby results in diagnostic errors. The high rate of misdiagnosis and inability to predict the recovery of consciousness for DOC patients have created a huge research interest in the assessment of consciousness. Researchers have explored the use of various stimulation and neuroimaging techniques to improve the diagnosis. In this article, we present the important findings of resting-state as well as sensory stimulation methods and highlight the stimuli proven to be successful in the assessment of consciousness. Primarily, we review the literature based on (a) application/non-use of stimuli (i.e., sensory stimulation/resting state-based), (b) type of stimulation used (i.e., auditory, visual, tactile, olfactory, or mental-imagery), (c) electrophysiological signal used (EEG/ERP, fMRI, PET, EMG, SCL, or ECG). Among the sensory stimulation methods, auditory stimulation has been extensively used, since it is easier to conduct for these patients. Olfactory and tactile stimulation have been less explored and need further research. Emotionally charged stimuli such as subject’s own name or narratives in a familiar voice or subject’s own face/family pictures or music result in stronger responses than neutral stimuli. Studies based on resting state analysis have employed measures like complexity, power spectral features, entropy and functional connectivity patterns to distinguish between the VS and MCS patients. Resting-state EEG and fMRI are the state-of-the-art techniques and have a huge potential in predicting the recovery of coma patients. Further, EMG and mental-imagery based studies attempt to obtain volitional responses from the VS patients and thus could detect their command-following capability. This may provide an effective means to communicate with these patients. Recent studies have employed fMRI and PET to understand the brain-activation patterns corresponding to the mental imagery. This review promotes our knowledge about the techniques used for the diagnosis of patients with DOC and attempts to provide ideas for future research.

Network Mapping of Connectivity Alterations in Disorder of Consciousness: Towards Targeted Neuromodulation

Disorder of consciousness (DoC) refers to a group of clinical conditions that may emerge after brain injury, characterized by a varying decrease in the level of consciousness that can last from days to years. An understanding of its neural correlates is crucial for the conceptualization and application of effective therapeutic interventions. Here we propose a quantitative meta-analysis of the neural substrate of DoC emerging from functional magnetic resonance (fMRI) and positron emission tomography (PET) studies. We also map the relevant networks of resulting areas to highlight similarities with Resting State Networks (RSNs) and hypothesize potential therapeutic solutions leveraging network-targeted noninvasive brain stimulation. Available literature was reviewed and analyzed through the activation likelihood estimate (ALE) statistical framework to describe resting-state or task-dependent brain activation patterns in DoC patients. Results show that task-related activity is limited to temporal regions resembling the auditory cortex, whereas resting-state fMRI data reveal a diffuse decreased activation affecting two subgroups of cortical (angular gyrus, middle frontal gyrus) and subcortical (thalamus, cingulate cortex, caudate nucleus) regions. Clustering of their cortical functional connectivity projections identify two main altered functional networks, related to decreased activity of (i) the default mode and frontoparietal networks, as well as (ii) the anterior salience and visual/auditory networks. Based on the strength and topography of their connectivity profile, biophysical modeling of potential brain stimulation solutions suggests the first network as the most feasible target for tES, tDCS neuromodulation in DoC patients.

Neuroimaging Studies on Disorders of Consciousness: A Meta-Analytic Evaluation

Neuroimaging tools could open a window on residual neurofunctional activity in the absence of detectable behavioural responses in patients with disorders of consciousness (DOC). Nevertheless, the literature on this topic is characterised by a large heterogeneity of paradigms and methodological approaches that can undermine the reproducibility of the results. To explicitly test whether task-related functional magnetic resonance imaging (fMRI) can be used to systematically detect neurofunctional differences between different classes of DOC, and whether these differences are related with a specific category of cognitive tasks (either active or passive), we meta-analyzed 22 neuroimaging studies published between 2005 and 2017 using the Activation Likelihood Estimate method. The results showed that: (1) active and passive tasks rely on well-segregated patterns of activations; (2) both unresponsive wakeful syndrome and patients in minimally conscious state activated a large portion of the dorsal-attentional network; (3) shared activations between patients fell mainly in the passive activation map (7492 voxels), while only 48 voxels fell in a subcortical region of the active-map. Our results suggest that DOCs can be described along a continuum—rather than as separated clinical categories—and characterised by a widespread dysfunction of brain networks rather than by the impairment of a well functionally anatomically defined one.

The clinical utility of fMRI for identifying covert awareness in the vegetative state: a comparison of sensitivity between 3T and 1.5T

In the last few years, mental imagery fMRI paradigms have been used successfully to identify covert command-following and awareness in some patients who are thought to be entirely vegetative. However, to date there is only evidence supporting their use at magnetic fields of 3T, which limits their applicability in clinical settings where lower field strengths are typically used. Here, we test the 'gold standard' fMRI paradigm for detecting residual awareness in non-responsive patients by comparing its sensitivity at 1.5T and 3T in the same group of healthy volunteers. We were able to successfully detect brain activity showing command-following in most participants at both 3T and 1.5T, with similar reliability. These results demonstrate that fMRI assessment of covert awareness is clinically viable and therefore justify a broader use of these methods in standard assessments in severely brain injured patients.

Comparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness

Current research aims at identifying voluntary brain activation in patients who are behaviorally diagnosed as being unconscious, but are able to perform commands by modulating their brain activity patterns. This involves machine learning techniques and feature extraction methods such as applied in brain computer interfaces. In this study, we try to answer the question if features/classification methods which show advantages in healthy participants are also accurate when applied to data of patients with disorders of consciousness. A sample of healthy participants (N = 22), patients in a minimally conscious state (MCS; N = 5), and with unresponsive wakefulness syndrome (UWS; N = 9) was examined with a motor imagery task which involved imagery of moving both hands and an instruction to hold both hands firm. We extracted a set of 20 features from the electroencephalogram and used linear discriminant analysis, k-nearest neighbor classification, and support vector machines (SVM) as classification methods. In healthy participants, the best classification accuracies were seen with coherences (mean = .79; range = .53−.94) and power spectra (mean = .69; range = .40−.85). The coherence patterns in healthy participants did not match the expectation of central modulated -rhythm. Instead, coherence involved mainly frontal regions. In healthy participants, the best classification tool was SVM. Five patients had at least one feature-classifier outcome with p0.05 (none of which were coherence or power spectra), though none remained significant after false-discovery rate correction for multiple comparisons. The present work suggests the use of coherences in patients with disorders of consciousness because they show high reliability among healthy subjects and patient groups. However, feature extraction and classification is a challenging task in unresponsive patients because there is no ground truth to validate the results.

EEG-response consistency across subjects in an active oddball task

The active oddball paradigm is a candidate task for voluntary brain activation. Previous research has focused on group effects, and has largely overlooked the potential problem of interindividual differences. Interindividual variance causes problems with the interpretation of group-level results. In this study we want to demonstrate the degree of consistency in the active oddball task across subjects, in order to answer the question of whether this task is able to reliably detect conscious target processing in unresponsive patients. We asked 18 subjects to count rare targets and to ignore frequent standards and rare distractors in an auditory active oddball task. Event-related-potentials (ERPs) and time-frequency data were analyzed with permutation-t-tests on a single subject level. We plotted the group-average ERPs and time-frequency data, and evaluated the numbers of subjects showing significant differences between targets and distractors in certain time-ranges. The distinction between targets/distractors and standards was found to be significant in the time-range of the P300 in all participants. In contrast, significant differences between targets and distractors in the time-range of the P3a/b were found in 8 subjects, only. By including effects in the N1 and in a late negative component there remained 2 subjects who did not show a distinction between targets and distractors in the ERP. While time-frequency data showed prominent effects for target/distractor vs. standard, significant differences between targets and distractors were found in 2 subjects, only. The results suggest that time-frequency- and ERP-analysis of the active oddball task may not be sensitive enough to detect voluntary brain activation in unresponsive patients. In addition, we found that time-frequency analysis was even less informative than ERPs about the subject’s task performance. Despite suggesting the use of more sensitive paradigms and/or analysis techniques, the present results give further evidence that electroencephalographic research should rely more strongly on single-subject analysis because interpretations of group-effects may be misleading.