Prognostic Value of Evoked Responses and Event-Related Brain Potentials in

Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling

Background: Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However, such pooling implicitly assumes that the SNR among sensors is equal, which is not necessarily valid. This study presents a novel approach for signal pooling that accounts for differential SNR among sensors. Methods: The new technique involves pooling together signals from multiple EEG channels weighted by their respective SNRs relative to the overall SNR of all channels. We compared ERP responses derived using this new technique with those derived using both individual channels as well as traditional averaged-based channel pooling. The outcomes were evaluated in both simulated data and real data from healthy adult volunteers (n = 37). Responses corresponding to a range of ERP components indexing auditory sensation (N100), attention (P300) and language processing (N400) were evaluated. Results: Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p’s < 0.001). Similarly, results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (p’s < 0.001). Conclusion: These results suggest that the new technique improves the capture of ERP responses relative to traditional techniques. As such, SNR-weighted channel pooling can further enable widespread applications of ERP techniques, especially those that require rapid assessments in noisy out-of-laboratory environments.

Long-term Follow-up of Preoperative Infant Event-related Potentials in School-age Children with Craniosynostosis

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Background:

Previous studies demonstrated impaired auditory processing in children with sagittal and metopic craniosynostosis before surgical correction. This study investigated whether worse presurgical neural response as assessed by event-related potentials (ERP) was predictive of poorer school-age neurocognition.

Methods:

Preoperative infant ERP was recorded in 15 sagittal and 18 metopic patients. Mismatch negativity and P150 paradigms were derived from ERP recordings, as previously published. Of those, 13 sagittal and 13 metopic patients returned for neurocognitive evaluation 6 or more years later. ERP was correlated to neurocognitive outcomes using Spearman's correlations controlling for age. Two-tailed t-tests were used to evaluate the influence of age at the time of surgery (6 months) and morphologic severity on neurocognitive outcomes

Results:

In the sagittal group, no significant correlations were found between preoperative mismatch negativity or P150 amplitudes and neurocognitive outcomes. Although no correlation was found between mismatch negativity and neurocognitive outcome in the metopic group, those with lower P150 amplitudes had higher scores in performance IQ (r = −0.877, P < 0.001) and full-scale IQ (r = −0.893, P < 0.001). Morphologic severity and neurocognitive outcomes showed no relationship in the sagittal or metopic groups. Patients who received surgery at less than 6 months had higher full-scale IQ (109.69 versus 95.92, P = 0.025), visuomotor integration (103.15 versus 90.46, P = 0.041), and visual perception scores (105.69 versus 96.08, P = 0.033).

Conclusions:

Preoperative infant ERP does not correlate with school-age neurocognitive outcomes. Earlier age at the time of surgery was associated with improved neurocognitive outcomes.

Multimodal characterization of the semantic N400 response within a rapid evaluation brain vital sign framework

Background

For nearly four decades, the N400 has been an important brainwave marker of semantic processing. It can be recorded non-invasively from the scalp using electrical and/or magnetic sensors, but largely within the restricted domain of research laboratories specialized to run specific N400 experiments. However, there is increasing evidence of significant clinical utility for the N400 in neurological evaluation, particularly at the individual level. To enable clinical applications, we recently reported a rapid evaluation framework known as “brain vital signs” that successfully incorporated the N400 response as one of the core components for cognitive function evaluation. The current study characterized the rapidly evoked N400 response to demonstrate that it shares consistent features with traditional N400 responses acquired in research laboratory settings—thereby enabling its translation into brain vital signs applications.

Methods

Data were collected from 17 healthy individuals using magnetoencephalography (MEG) and electroencephalography (EEG), with analysis of sensor-level effects as well as evaluation of brain sources. Individual-level N400 responses were classified using machine learning to determine the percentage of participants in whom the response was successfully detected.

Results

The N400 response was observed in both M/EEG modalities showing significant differences to incongruent versus congruent condition in the expected time range (p < 0.05). Also as expected, N400-related brain activity was observed in the temporal and inferior frontal cortical regions, with typical left-hemispheric asymmetry. Classification robustly confirmed the N400 effect at the individual level with high accuracy (89%), sensitivity (0.88) and specificity (0.90).

Conclusion

The brain vital sign N400 characteristics were highly consistent with features of the previously reported N400 responses acquired using traditional laboratory-based experiments. These results provide important evidence supporting clinical translation of the rapidly acquired N400 response as a potential tool for assessments of higher cognitive functions.

Measuring Depth in Still Water: Electrophysiologic Indicators of Residual Consciousness in the Unresponsive Patient

Searching for evidence of consciousness in outwardly unresponsive patients presents significant clinical challenges as the spectrum of disorders of consciousness has become more clearly defined, with clinical examination, functional MRI, and electrophysiologic tests having complementary roles in the investigation of minimally conscious patients, those in a locked-in state, coma, or in a vegetative state. Serial bedside electrophysiologic testing can probe for higher order cortical responses temporally and spatially propagated through cortical networks, while long-latency event-related potentials may help differentiate patients with coma or vegetative state from a state of residual consciousness. Transcranial magnetic stimulation co-registered to high-density EEG may reveal widespread pulse-stimulated cortical activation of various brain regions. These emerging electrophysiologic techniques show promise as powerful diagnostic, prognostic, and therapeutic tools.

Cognitive Event-Related Potentials during the Sub-Acute Phase of Severe Traumatic Brain Injury and Their Relationship to Outcome

Predicting outcome in the early phase after severe traumatic brain injury (sTBI) is a major clinical challenge, particularly identifying patients with potential for good cognitive outcome. The current single-center prospective study aimed to explore presence and normalization of electroencephalography (EEG)-based event-related potentials (ERPs) in the early phase followings TBI, and their relationship to functional and cognitive outcome 6 months post-injury. Fourteen adult patients (eight males) with sTBI were recruited from the neurointensive care unit (mean age = 38.2 years [standard deviation (SD) = 14.7]; mean lowest Glasgow Coma Scale (GCS) score within first 24 h = 5.4, SD = 1.87). EEG recordings were conducted biweekly at three time-points applying an ERP paradigm encompassing a passive condition involving hearing their own name randomly interspersed between an unfamiliar name (UN), and an active condition with instruction to count their own name. Functional and cognitive outcome 6 months post-injury was measured with Glasgow Outcome Scale-Extended (GOSE) and neuropsychological tests of attention and memory. Ten patients demonstrated a significantly enhanced cognitive P3 in the active counting task compared with passive listening across recordings, and six presented with normalization of P3 in the counting task. Moreover, P3 amplitude to the counting task at the third time-point was positively correlated with both functional outcome (GOSE) and cognition (verbal learning, attentional set-shifting, and switching) 6 months post-injury. ERP can index cognitive capacities in the early phase following sTBI, and the cognitive P3 component in an active design is associated with functional and cognitive outcome, demonstrating that the cognitive P3 may yield valuable information of residual cognition and provide supplementary prognostic information.

Clinical Applications for EPs in the ICU

In critically ill patients, evoked potential (EP) testing is an important tool for measuring neurologic function, signal transmission, and secondary processing of sensory information in real time. Evoked potential measures conduction along the peripheral and central sensory pathways with longer-latency potentials representing more complex thalamocortical and intracortical processing. In critically ill patients with limited neurologic exams, EP provides a window into brain function and the potential for recovery of consciousness. The most common EP modalities in clinical use in the intensive care unit include somatosensory evoked potentials, brainstem auditory EPs, and cortical event-related potentials. The primary indications for EP in critically ill patients are prognostication in anoxic-ischemic or traumatic coma, monitoring for neurologic improvement or decline, and confirmation of brain death. Somatosensory evoked potentials had become an important prognostic tool for coma recovery, especially in comatose survivors of cardiac arrest. In this population, the bilateral absence of cortical somatosensory evoked potentials has nearly 100% specificity for death or persistent vegetative state. Historically, EP has been regarded as a negative prognostic test, that is, the absence of cortical potentials is associated with poor outcomes while the presence cortical potentials are prognostically indeterminate. In recent studies, the presence of middle-latency and long-latency potentials as well as the amplitude of cortical potentials is more specific for good outcomes. Event-related potentials, particularly mismatch negativity of complex auditory patterns, is emerging as an important positive prognostic test in patients under comatose. Multimodality predictive algorithms that combine somatosensory evoked potentials, event-related potentials, and clinical and radiographic factors are gaining favor for coma prognostication.

Detection of event-related potentials in individual subjects using support vector machines

Event-related potentials (ERPs) are tiny electrical brain responses in the human electroencephalogram that are typically not detectable until they are isolated by a process of signal averaging. Owing to the extremely smallsize of ERP components (ranging from less than 1 μV to tens of μV), compared to background brain rhythms, statistical analyses of ERPs are predominantly carried out in groups of subjects. This limitation is a barrier to the translation of ERP-based neuroscience to applications such as medical diagnostics. We show here that support vector machines (SVMs) are a useful method to detect ERP components in individual subjects with a small set of electrodes and a small number of trials for a mismatch negativity (MMN) ERP component. Such a reduced experiment setup is important for clinical applications. One hundred healthy individuals were presented with an auditory pattern containing pattern-violating deviants to evoke the MMN. Two-class SVMs were then trained to classify averaged ERP waveforms in response to the standard tone (tones that match the pattern) and deviant tone stimuli (tones that violate the pattern). The influence of kernel type, number of epochs, electrode selection, and temporal window size in the averaged waveform were explored. When using all electrodes, averages of all available epochs, and a temporal window from 0 to 900-ms post-stimulus, a linear SVM achieved 94.5 % accuracy. Further analyses using SVMs trained with narrower, sliding temporal windows confirmed the sensitivity of the SVM to data in the latency range associated with the MMN.

A rapid event-related potential (ERP) method for point-of-care evaluation of brain function: development of the Halifax Consciousness Scanner

BACKGROUND

Event-related potentials (ERPs) may provide a non-invasive index of brain function for a range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges that prevent full integration into clinical settings.

NEW METHOD

To translate ERP capabilities from the lab to clinical applications, we have developed methods like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation of brain functional status. The present study describes the ERP components evoked from auditory tones and speech stimuli. ERP results were obtained using a 5-min test in 100 healthy individuals. The HCS sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory, and language perception, respectively. Component detection was examined at group and individual levels, and evaluated across both statistical and classification approaches.

RESULTS

All ERP components were robustly detected at the group level. At the individual level, nonparametric statistical analyses showed reduced accuracy relative to support vector (SVM) machine classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%; ENE: 91.8%; and N400: 92.3%.

CONCLUSIONS

A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning classification improved detection accuracy across a large healthy control sample. Translating ERPs into clinical applications is increasingly possible at the individual level.

Traumatic brain injury detection using electrophysiological methods

Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: (1) Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. (2) ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are non-disclosing. (3) The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including non-linear dynamical analysis, complexity measures, analysis of causal interactions, graph theory, and information dynamics. (4) Reports of high specificity in qEEG evaluations of TBI must be interpreted with care. High specificities have been reported in carefully constructed clinical studies in which healthy controls were compared against a carefully selected TBI population. The published literature indicates, however, that similar abnormalities in qEEG measures are observed in other neuropsychiatric disorders. While it may be possible to distinguish a clinical patient from a healthy control participant with this technology, these measures are unlikely to discriminate between, for example, major depressive disorder, bipolar disorder, or TBI. The specificities observed in these clinical studies may well be lost in real world clinical practice. (5) The absence of specificity does not preclude clinical utility. The possibility of use as a longitudinal measure of treatment response remains. However, efficacy as a longitudinal clinical measure does require acceptable test-retest reliability. To date, very few test-retest reliability studies have been published with qEEG data obtained from TBI patients or from healthy controls. This is a particular concern because high variability is a known characteristic of the injured central nervous system.

Real-time feature extraction of P300 component using adaptive nonlinear principal component analysis

BACKGROUND

The electroencephalography (EEG) signals are known to involve the firings of neurons in the brain. The P300 wave is a high potential caused by an event-related stimulus. The detection of P300s included in the measured EEG signals is widely investigated. The difficulties in detecting them are that they are mixed with other signals generated over a large brain area and their amplitudes are very small due to the distance and resistivity differences in their transmittance.

METHODS

A novel real-time feature extraction method for detecting P300 waves by combining an adaptive nonlinear principal component analysis (ANPCA) and a multilayer neural network is proposed. The measured EEG signals are first filtered using a sixth-order band-pass filter with cut-off frequencies of 1 Hz and 12 Hz. The proposed ANPCA scheme consists of four steps: pre-separation, whitening, separation, and estimation. In the experiment, four different inter-stimulus intervals (ISIs) are utilized: 325 ms, 350 ms, 375 ms, and 400 ms.

RESULTS

The developed multi-stage principal component analysis method applied at the pre-separation step has reduced the external noises and artifacts significantly. The introduced adaptive law in the whitening step has made the subsequent algorithm in the separation step to converge fast. The separation performance index has varied from -20 dB to -33 dB due to randomness of source signals. The robustness of the ANPCA against background noises has been evaluated by comparing the separation performance indices of the ANPCA with four algorithms (NPCA, NSS-JD, JADE, and SOBI), in which the ANPCA algorithm demonstrated the shortest iteration time with performance index about 0.03. Upon this, it is asserted that the ANPCA algorithm successfully separates mixed source signals.

CONCLUSIONS

The independent components produced from the observed data using the proposed method illustrated that the extracted signals were clearly the P300 components elicited by task-related stimuli. The experiment using 350 ms ISI showed the best performance. Since the proposed method does not use down-sampling and averaging, it can be used as a viable tool for real-time clinical applications.

"Cognitive" visual acuity estimation based on the event-related potential P300 component

OBJECTIVE

An objective assessment of sensory and sensuo-cognitive function, based on physiological signals rather than on the behavioral response of a patient, is often advisable, albeit challenging. Evoked potentials are frequently used as an objective measure, but usually fail to detect defects beyond primary sensory areas, including those of psychogenic origin. Here we assess whether the event-related P300 component may be used to measure "cognitive" visual acuity.

METHODS

A visual oddball paradigm was used to elicit P300 responses in subjects with normal or artificially blurred vision. Probe stimuli consisted of infrequently presented gratings with spatial frequencies in the range of 2.2-16.2 cycles per degree, which could be either target or non-target stimuli depending on their orientation. Frequent stimuli were homogeneously grey fields.

RESULTS

Without blur, rare stimuli of all spatial frequencies produced reliable P300 responses. Blur abolished the P300 to fine gratings consistently in 10 of 11 subjects. The drop in P300 amplitude was steep, rather than gradual, between visible and invisible gratings.

CONCLUSION

The P300 is sensitive to identify the resolution threshold and thus may serve as a tool for estimating acuity in cases of visual impairments.

SIGNIFICANCE

The study presents a tool for the objective assessment of acuity in cases of higher-level visual impairments. The concept can most likely be extended to other sensory modalities.

Early prognosis in traumatic brain injury: from prophecies to predictions

Traumatic brain injury (TBI) is a heterogeneous condition that encompasses a broad spectrum of disorders. Outcome can be highly variable, particularly in more severely injured patients. Despite the association of many variables with outcome, prognostic predictions are notoriously difficult to make. Multivariable analysis has identified age, clinical severity, CT abnormalities, systemic insults (hypoxia and hypotension), and laboratory variables as relevant factors to include in models to predict outcome in individual patients. Advances in statistical modelling and the availability of large datasets have facilitated the development of prognostic models that have greater performance and generalisability. Two prediction models are currently available, both of which have been developed on large datasets with state-of-the-art methods, and offer new opportunities. We see great potential for their use in clinical practice, research, and policy making, as well as for assessment of the quality of health-care delivery. Continued development, refinement, and validation is advocated, together with assessment of the clinical impact of prediction models, including treatment response.

Early somatosensory evoked potential grades in comatose traumatic brain injury patients predict cognitive and functional outcome

OBJECTIVES

To relate early somatosensory evoked potential grades from comatose traumatic brain injury patients to neuropsychological and functional outcome 1 yr later; to determine the day (within the first week after traumatic brain injury) that somatosensory evoked potential grade best correlates with outcome; to determine whether somatosensory evoked potential grade improvement in the first week after traumatic brain injury is associated with improved outcome.

DESIGN

Prospective cohort study.

SETTING

Critical care unit at a university hospital.

PATIENTS

Median nerve somatosensory evoked potentials were obtained from 81 comatose patients with traumatic brain injury. Somatosensory evoked potential grades were calculated from results obtained on days 1, 3, and 7 after traumatic brain injury. Glasgow Outcome Scale, Barthel Index, Rivermead Head Injury Follow-up Questionnaire, General Health Questionnaire, Stroop Color-Word Test, Paced Auditory Serial Addition Task, and Symbol-Digit Modalities Test scores were obtained 1 yr after injury.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Somatosensory evoked potential grade on days 1, 3, and 7 related significantly with Glasgow Outcome Scale and Barthel scores (day 3 better than day 1) but did not relate with Rivermead Head Injury Follow-up Questionnaire or General Health Questionnaire scores. Day 3 and day 7 somatosensory evoked potential grades related significantly with Stroop scores. Day 3 somatosensory evoked potential grades related significantly with Symbol-Digit Modalities Test scores. Patients with bilaterally present but abnormal somatosensory evoked potentials, whose somatosensory evoked potential grade improved between days 1 and 3, had marginally better functional outcome than those without somatosensory evoked potential grade improvement.

CONCLUSIONS

Day 3 somatosensory evoked potential grade related to information-processing speed, working memory, and the ability to attend to tasks 1 yr after traumatic brain injury. Day 3 somatosensory evoked potential grade had the strongest relationship with functional outcome. Somatosensory evoked potential grades were not related to emotional well-being.

Coma

Coma is a state of unarousable unconsciousness due to dysfunction of the brain's ascending reticular activating system (ARAS), which is responsible for arousal and the maintenance of wakefulness. Anatomically and physiologically the ARAS has a redundancy of pathways and neurotransmitters; this may explain why coma is usually transient (seldom lasting more than 3 weeks). Emergence from coma is succeeded by outcomes ranging from the vegetative state to complete recovery, depending on the severity of damage to the cerebral cortex, the thalamus, and their integrated function. The clinical and laboratory assessments of the comatose patient are reviewed here, along with an analysis of how various conditions (structural brain lesions, metabolic and toxic disorders, trauma, infections, seizures, hypothermia, and hyperthermia) produce coma. Management issues include the determination of the cause and reversibility (prognosis) of neurological impairment, support of the patient, definitive treatment when possible, and the ethical considerations for those situations where marked disability is predicted with certainty.

Fast stimulus sequences improve the efficiency of event-related potential P300 recordings

The P300 is an easily recorded component of the event-related potential (ERP). Yet, it is desirable to reduce the recording duration, for instance in patient examinations. A limiting factor is the time between stimuli that is necessary for the ERP to return to baseline. We explored whether this time could be reduced, despite an overlap of responses to successive stimuli, by presenting visual stimuli at a fast rate of 4.7 s(-1)using a standard oddball paradigm. Rare stimuli occurred at a probability of 14%. The P300 was isolated by subtracting the responses to the frequent stimuli from those to the rare stimuli, thereby eliminating the influence of response overlap. We compared the efficiency of fast stimulation to that of conventionally slow stimulation by assessing the signal-to-noise ratio of the P300 amplitude. Two presentation durations of individual stimuli, namely 53 ms and 93 ms, were tested. Not unexpectedly, P300 amplitudes were smaller for the fast sequence. However, the signal-to-noise ratio improved significantly by more than 50% due to the larger number of trials within a given time interval. When targeting a given signal-to-noise ratio, fast stimulation allows for a reduction in recording time of around 35%. Median peak times were 16-56 ms shorter for the fast stimulus sequence. Topography was comparable for fast and slow stimulation, suggesting a similar functional composition of the respective responses. Fast stimulation may thus be used to replace less efficient slow stimulation schemes in clinical diagnosis and for certain experimental questions.

Prognostic value of evoked and event-related potentials in moderate to severe brain injury

Clinicians are often expected to project patients' clinical outcomes to allow effective planning for future care. This can be a challenge in patients with moderate to severe traumatic brain injury (TBI) who are often unable to participate reliably in clinical evaluations. With recent advances in computer instrumentation and signal processing, evoked potentials and event-related potentials show increasing promise as powerful tools for prognosticating the trajectory of recovery and ultimate outcome from the TBI. Short- and middle-latency evoked potentials can now effectively predict coma outcomes in patients with acute TBI. Long-latency event-related potential components hold promise in predicting recovery of higher order cognitive abilities.