Substitute or complement? Defining the relative place of EEG and fMRI in the detection of voluntary brain reactions

Covert narrative capacity: Mental life in patients thought to lack consciousness

Despite the apparent absence of external signs of consciousness, a significant proportion of behaviorally nonresponsive patients can respond to commands by willfully modulating their brain activity. However, little is known about the mental life of these patients. We discuss a recent innovative approach, which sheds light on the preserved cognitive capacities of these patients, including executive function, theory of mind, and the experience of affective states. This research represents a fundamental shift in our understanding of these patients, and has important implications for both their continued treatment and care. Moreover, this research marks out avenues for future inquiry into the residual cognitive capacities of these patients.

Somatosensory attention identifies both overt and covert awareness in disorders of consciousness

OBJECTIVE

Some patients diagnosed with disorders of consciousness retain sensory and cognitive abilities beyond those apparent from their overt behavior. Characterizing these covert abilities is crucial for diagnosis, prognosis, and medical ethics. This multimodal study investigates the relationship between electroencephalographic evidence for perceptual/cognitive preservation and both overt and covert markers of awareness.

METHODS

Fourteen patients with severe brain injuries were evaluated with an electroencephalographic vibrotactile attention task designed to identify a hierarchy of residual somatosensory and cognitive abilities: (1) somatosensory steady-state evoked responses, (2) bottom-up attention orienting (P3a event-related potential), and (3) top-down attention (P3b event-related potential). Each patient was also assessed with a clinical behavioral scale and 2 functional magnetic resonance imaging assessments of covert command following.

RESULTS

Six patients produced only sensory responses, with no evidence of cognitive event-related potentials. A further 8 patients demonstrated reliable bottom-up attention-orienting responses (P3a). No patient showed evidence of top-down attention (P3b). Only those patients who followed commands, whether overtly with behavior or covertly with functional neuroimaging, also demonstrated event-related potential evidence of attentional orienting.

INTERPRETATION

Somatosensory attention-orienting event-related potentials differentiated patients who could follow commands from those who could not. Crucially, this differentiation was irrespective of whether command following was evident through overt external behavior, or through covert functional neuroimaging methods. Bedside electroencephalographic methods may corroborate more expensive and challenging methods such as functional neuroimaging, and thereby assist in the accurate diagnosis of awareness. Ann Neurol 2016;80:412-423.

Brain-computer interfaces for patients with disorders of consciousness

The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness. For the small subset of patients who retain awareness, brain-computer interfaces could serve as tools for communication and environmental control. Here we review the existing literature concerning the sensory and cognitive abilities of patients with disorders of consciousness with respect to existing brain-computer interface designs. We highlight the challenges of device development for this special population and address some of the most promising approaches for future investigations.

Bedside Evaluation of the Functional Organization of the Auditory Cortex in Patients with Disorders of Consciousness

To measure the level of residual cognitive function in patients with disorders of consciousness, the use of electrophysiological and neuroimaging protocols of increasing complexity is recommended. This work presents an EEG-based method capable of assessing at an individual level the integrity of the auditory cortex at the bedside of patients and can be seen as the first cortical stage of this hierarchical approach. The method is based on two features: first, the possibility of automatically detecting the presence of a N100 wave and second, in showing evidence of frequency processing in the auditory cortex with a machine learning based classification of the EEG signals associated with different frequencies and auditory stimulation modalities. In the control group of twelve healthy volunteers, cortical frequency processing was clearly demonstrated. EEG recordings from two patients with disorders of consciousness showed evidence of partially preserved cortical processing in the first patient and none in the second patient. From these results, it appears that the classification method presented here reliably detects signal differences in the encoding of frequencies and is a useful tool in the evaluation of the integrity of the auditory cortex. Even though the classification method presented in this work was designed for patients with disorders of consciousness, it can also be applied to other pathological populations.

"Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients

Given the fact that clinical bedside examinations can have a high rate of misdiagnosis, machine learning techniques based on neuroimaging and electrophysiological measurements are increasingly being considered for comatose patients and patients with unresponsive wakefulness syndrome, a minimally conscious state or locked-in syndrome. Machine learning techniques have the potential to move from group-level statistical results to personalized predictions in a clinical setting. They have been applied for the purpose of (1) detecting changes in brain activation during functional tasks, equivalent to a behavioral command-following test and (2) estimating signs of consciousness by analyzing measurement data obtained from multiple subjects in resting state. In this review, we provide a comprehensive overview of the literature on both approaches and discuss the translation of present findings to clinical practice. We found that most studies struggle with the difficulty of establishing a reliable behavioral assessment and fluctuations in the patient's levels of arousal. Both these factors affect the training and validation of machine learning methods to a considerable degree. In studies involving more than 50 patients, small to moderate evidence was found for the presence of signs of consciousness or good outcome, where one study even showed strong evidence for good outcome.