EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect

Covert Consciousness in Acute Brain Injury Revealed by Automated Pupillometry and Cognitive Paradigms

BACKGROUND

Identifying covert consciousness in intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC) is crucial for treatment decisions, but sensitive low-cost bedside markers are missing. We investigated whether automated pupillometry combined with passive and active cognitive paradigms can detect residual consciousness in ICU patients with DoC.

METHODS

We prospectively enrolled clinically low-response or unresponsive patients with traumatic or nontraumatic DoC from ICUs of a tertiary referral center. Age-matched and sex-matched healthy volunteers served as controls. Patients were categorized into clinically unresponsive (coma or unresponsive wakefulness syndrome) or clinically low-responsive (minimally conscious state or better). Using automated pupillometry, we recorded pupillary dilation to passive (visual and auditory stimuli) and active (mental arithmetic) cognitive paradigms, with task-specific success criteria (e.g., ≥ 3 of 5 pupillary dilations on five consecutive mental arithmetic tasks).

RESULTS

We obtained 699 pupillometry recordings at 178 time points from 91 ICU patients with brain injury (mean age 60 ± 13.8 years, 31% women, and 49.5% nontraumatic brain injuries). Recordings were also obtained from 26 matched controls (59 ± 14.8 years, 38% women). Passive paradigms yielded limited distinctions between patients and controls. However, active paradigms enabled discrimination between different states of consciousness. With mental arithmetic of moderate complexity, ≥ 3 pupillary dilations were seen in 17.8% of clinically unresponsive patients and 50.0% of clinically low-responsive patients (odds ratio 4.56, 95% confidence interval 2.09-10.10; p < 0.001). In comparison, 76.9% healthy controls responded with ≥ 3 pupillary dilations (p = 0.028). Results remained consistent across sensitivity analyses using different thresholds for success. Spearman's rank analysis underscored the robust association between pupillary dilations during mental arithmetic and consciousness levels (rho = 1, p = 0.017). Notably, one behaviorally unresponsive patient demonstrated persistent command-following behavior 2 weeks before overt signs of awareness, suggesting prolonged cognitive motor dissociation.

CONCLUSIONS

Automated pupillometry combined with mental arithmetic can identify cognitive efforts, and hence covert consciousness, in ICU patients with acute DoC.

Recurrence quantification analysis during a mental calculation task

The identification of brain dynamical changes under different cognitive conditions with noninvasive techniques such as electroencephalography (EEG) is relevant for the understanding of their underlying neural mechanisms. The comprehension of these mechanisms has applications in the early diagnosis of neurological disorders and asynchronous brain computer interfaces. In both cases, there are no reported features that could describe intersubject and intra subject dynamics behavior accurately enough to be applied on a daily basis. The present work proposes the use of three nonlinear features (recurrence rate, determinism, and recurrence times) extracted from recurrence quantification analysis (RQA) to describe central and parietal EEG power series complexity in continuous alternating episodes of mental calculation and rest state. Our results demonstrate a consistent mean directional change of determinism, recurrence rate, and recurrence times between conditions. Increasing values of determinism and recurrence rate were present from the rest state to mental calculation, whereas recurrence times showed the opposite pattern. The analyzed features in the present study showed statistically significant changes between rest and mental calculation states in both individual and population analysis. In general, our study described mental calculation EEG power series as less complex systems in comparison to the rest state. Moreover, ANOVA showed stability of RQA features along time.

Test-retest reproducibility comparison in resting and the mental task states: A sensor and source-level EEG spectral analysis

Previous test-retest analysis of EEG mostly focused on eyes open and eyes closed resting-state. However, less attention was paid to the EEG during the subject-driven mental imaginary task state. In the current study, we compared the test-retest reproducibility of EEG spectrum in three mental imaginary task states (i.e. performed mental arithmetic, recalled the events of their day, and silently sang lyrics) and two resting states (i.e. eyes open and closed) during three EEG sessions. The resting state with eyes closed has the highest reproducibility, while the resting state with eyes opened has the lowest reproducibility for the spectral features of EEG signals at the sensor level. However, the reproducibility during eyes-open ranked higher among the five states at the source level. Moreover, the mental arithmetic state has the highest reproducibility among all the three task states. And its reproducibility in certain rhythms (theta, gamma, etc) was higher than the resting states. The reproducibility of the EEG spectrum was also investigated from the perspective of large-scale brain networks. The dorsal attention network showed the highest reproducibility in a wide frequency range of the alpha and beta rhythms. Our study suggests the importance of task selection based on the target brain region and the target frequency band. This may provide some suggestions for future researchers to choose appropriate experimental paradigms and provide a guideline on EEG study for the basic and clinical applications.

Detecting Brain Activity Following a Verbal Command in Patients With Disorders of Consciousness

Background

The accurate assessment of patients with disorders of consciousness (DOC) is a challenge to most experienced clinicians. As a potential clinical tool, functional magnetic resonance imaging (fMRI) could detect residual awareness without the need for the patients’ actual motor responses.

Methods

We adopted a simple active fMRI motor paradigm (hand raising) to detect residual awareness in these patients. Twenty-nine patients were recruited. They met the diagnosis of minimally conscious state (MCS) (male = 6, female = 2; n = 8), vegetative state/unresponsive wakefulness syndrome (VS/UWS) (male = 17, female = 4; n = 21).

Results

We analyzed the command-following responses for robust evidence of statistically reliable markers of motor execution, similar to those found in 15 healthy controls. Of the 29 patients, four (two MCS, two VS/UWS) could adjust their brain activity to the “hand-raising” command, and they showed activation in motor-related regions (which could not be discovered in the own-name task).

Conclusion

Longitudinal behavioral assessments showed that, of these four patients, two in a VS/UWS recovered to MCS and one from MCS recovered to MCS+ (i.e., showed command following). In patients with no response to hand raising task, six VS/UWS and three MCS ones showed recovery in follow-up procedure. The simple active fMRI “hand-raising” task can elicit brain activation in patients with DOC, similar to those observed in healthy volunteers. Activity of the motor-related network may be taken as an indicator of high-level cognition that cannot be discerned through conventional behavioral assessment.

Automated pupillometry to detect command following in neurological patients: a proof-of-concept study

Background

Levels of consciousness in patients with acute and chronic brain injury are notoriously underestimated. Paradigms based on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) may detect covert consciousness in clinically unresponsive patients but are subject to logistical challenges and the need for advanced statistical analysis.

Methods

To assess the feasibility of automated pupillometry for the detection of command following, we enrolled 20 healthy volunteers and 48 patients with a wide range of neurological disorders, including seven patients in the intensive care unit (ICU), who were asked to engage in mental arithmetic.

Results

Fourteen of 20 (70%) healthy volunteers and 17 of 43 (39.5%) neurological patients, including 1 in the ICU, fulfilled prespecified criteria for command following by showing pupillary dilations during ≥4 of five arithmetic tasks. None of the five sedated and unconscious ICU patients passed this threshold.

Conclusions

Automated pupillometry combined with mental arithmetic appears to be a promising paradigm for the detection of covert consciousness in people with brain injury. We plan to build on this study by focusing on non-communicating ICU patients in whom the level of consciousness is unknown. If some of these patients show reproducible pupillary dilation during mental arithmetic, this would suggest that the present paradigm can reveal covert consciousness in unresponsive patients in whom standard investigations have failed to detect signs of consciousness.

On the Feasibility of Using an Ear-EEG to Develop an Endogenous Brain-Computer Interface

Brain-computer interface (BCI) studies based on electroencephalography (EEG) measured around the ears (ear-EEGs) have mostly used exogenous paradigms involving brain activity evoked by external stimuli. The objective of this study is to investigate the feasibility of ear-EEGs for development of an endogenous BCI system that uses self-modulated brain activity. We performed preliminary and main experiments where EEGs were measured on the scalp and behind the ears to check the reliability of ear-EEGs as compared to scalp-EEGs. In the preliminary and main experiments, subjects performed eyes-open and eyes-closed tasks, and they performed mental arithmetic (MA) and light cognitive (LC) tasks, respectively. For data analysis, the brain area was divided into four regions of interest (ROIs) (i.e., frontal, central, occipital, and ear area). The preliminary experiment showed that the degree of alpha activity increase of the ear area with eyes closed is comparable to those of other ROIs (occipital > ear > central > frontal). In the main experiment, similar event-related (de)synchronization (ERD/ERS) patterns were observed between the four ROIs during MA and LC, and all ROIs showed the mean classification accuracies above 70% required for effective binary communication (MA vs. LC) (occipital = ear = central = frontal). From the results, we demonstrated that ear-EEG can be used to develop an endogenous BCI system based on cognitive tasks without external stimuli, which allows the usability of ear-EEGs to be extended.

Towards using fNIRS recordings of mental arithmetic for the detection of residual cognitive activity in patients with disorders of consciousness (DOC)

BACKGROUND

Recently, fNIRS has been proposed as a promising approach for awareness detection, and a possible method to establish basic communication in patients with disorders of consciousness (DOC).

AIM

Using fNIRS, the present study evaluated the applicability of auditory presented mental-arithmetic tasks in this respect.

METHODS

We investigated the applicability of active attention to serial subtractions for awareness detection in ten healthy controls (HC, 21-32 y/o), by comparing the measured patterns to patterns induced by self-performance of the same task. Furthermore, we examined the suitability of ignoring the given task as additional control signal to implement a two-class brain-computer interface (BCI) paradigm. Finally, we compared our findings in HC with recordings in one DOC patient (78 y/o).

RESULTS AND CONCLUSION

Results of the HC revealed no differences between the self-performance and the attention condition, making the attention task suitable for awareness detection. However, there was no general difference between the ignore and attend condition, making the tasks less suitable for BCI control. Despite inconsistent correlations between the patient data and the HC group, single runs of the patient recordings revealed task-synchronous patterns - however, we cannot conclude whether the measured activation derives from instruction based task performance and thus awareness.