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.

Cognitive loading via mental arithmetic modulates effects of blink-related oscillations on precuneus and ventral attention network regions.

Blink-related oscillations (BROs) have been linked with environmental monitoring processes associated with blinking, with cortical activations in the bilateral precuneus. Although BROs have been described under resting and passive fixation conditions, little is known about their characteristics under cognitive loading. To address this, we investigated BRO effects during both mental arithmetic (MA) and passive fixation (PF) tasks using magnetoencephalography (n =20), while maintaining the same sensory environment in both tasks. Our results confirmed the presence of BRO effects in both MA and PF tasks, with similar characteristics including blink-related increase in global field power and blink-related activation of the bilateral precuneus. In addition, cognitive loading due to MA also modulated BRO effects by decreasing BRO-induced cortical activations in key brain regions including the bilateral anterior precuneus. Interestingly, blinking during MA-but not PF-activated regions of the ventral attention network (i.e., right supramarginal gyrus and inferior frontal gyrus), suggesting possible recruitment of these areas for blink processing under cognitive loading conditions. Time-frequency analysis revealed a consistent pattern of BRO-related effects in the precuneus in both tasks, but with task-related functional segregation within the anterior and posterior subregions. Based on these findings, we postulate a potential neurocognitive mechanism for blink processing in the precuneus. This study is the first investigation of BRO effects under cognitive loading, and our results provide compelling new evidence for the important cognitive implications of blink-related processing in the human brain.

Brain vital signs as a quantitative measure of cognition: Methodological implementation in a care home environment.

Introduction: Managing cognitive function in care homes is a significant challenge. Individuals in care have a variety of scores across standard clinical assessments, such as the Mini-Mental Status Exam (MMSE), and many of them have scores that fall within the range associated with dementia. A recent methodological advance, brain vital sign monitoring through auditory event-related potentials, provides an objective and sensitive physiological measurement to track abnormalities, differences, or changes in cognitive function. Taking advantage of point-of-care accessibility, the current study evaluated the methodological feasibility, the assessment of whether a particular research method can be successfully implemented, of quantitatively measuring cognition of care home residents using brain vital signs. Secondarily, the current study examined the relationship between brain vital signs, specifically the cognitive processing associated N400 component, and MMSE scores in care home residents. Materials and methods: Brain vital signs used the established N100 (auditory sensation), P300 (basic attention), and N400 (cognitive processing) event-related potential (ERP) components. A total of 52 residents were enrolled, with all participants evaluated using the MMSE. Participants were assigned into homogeneous groups based on their MMSE scores, and were categorized into low (n = 14), medium (n = 17), and high (n = 13) MMSE groups. Both brain vital sign measures and underlying ERP waveforms were examined. Statistical analyses used partial least squares correlation (PLS) analyses in which both MMSE and age were included as factors, as well as jackknife approaches, to test for significant brain vital sign changes. Results: The current study successfully measured and analyzed standardized, quantifiable brain vital signs in a care home setting. ERP waveform data showed specific N400 changes between MMSE groups as a function of MMSE score. PLS analyses confirmed significant MMSE-related and age-related differences in the N400 amplitude (p < 0.05, corrected). Similarly, the jackknife approach emphasized the N400 latency difference between the low and high MMSE groups. Discussion and conclusion: It was possible to acquire brain vital signs measures in care home residents. Additionally, the current study evaluated brain vital signs relative to MMSE in this group. The comparison revealed significant decreasing in N400 response amplitude (cognitive processing) as a function of both MMSE score and age, as well as a slowing of N400 latency. The findings indicate that objective neurophysiological measures of impairment are detectable in care home residents across the span of MMSE scores. Direct comparison to MMSE- and age-related variables represents a critical initial step ahead of future studies that will investigate relative improvements in sensitivity, validity, reliability and related advantages of brain vital sign monitoring.

Virtual reality simulator: demonstrated use in neurosurgical oncology.

BACKGROUND: The overriding importance of patient safety, the complexity of surgical techniques, and the challenges associated with teaching surgical trainees in the operating room are all factors driving the need for innovative surgical simulation technologies. TECHNICAL DEVELOPMENT: Despite these issues, widespread use of virtual reality simulation technology in surgery has not been fully implemented, largely because of the technical complexities in developing clinically relevant and useful models. This article describes the successful use of the NeuroTouch neurosurgical simulator in the resection of a left frontal meningioma. CONCLUSION: The widespread application of surgical simulation technology has the potential to decrease surgical risk, improve operating room efficiency, and fundamentally change surgical training.

Blink-related EEG oscillations are neurophysiological indicators of subconcussive head impacts in female soccer players: a preliminary study.

Introduction: Repetitive subconcussive head impacts can lead to subtle neural changes and functional consequences on brain health. However, the objective assessment of these changes remains limited. Resting state blink-related oscillations (BROs), recently discovered neurological responses following spontaneous blinking, are explored in this study to evaluate changes in BRO responses in subconcussive head impacts. Methods: We collected 5-min resting-state electroencephalography (EEG) data from two cohorts of collegiate athletes who were engaged in contact sports (SC) or non-contact sports (HC). Video recordings of all on-field activities were conducted to determine the number of head impacts during games and practices in the SC group. Results: In both groups, we were able to detect a BRO response. Following one season of games and practice, we found a strong association between the number of head impacts sustained by the SC group and increases in delta and beta spectral power post-blink. There was also a significant difference between the two groups in the morphology of BRO responses, including decreased peak-to-peak amplitude of response over left parietal channels and differences in spectral power in delta and alpha frequency range post-blink. Discussion: Our preliminary results suggest that the BRO response may be a useful biomarker for detecting subtle neural changes resulting from repetitive head impacts. The clinical utility of this biomarker will need to be validated through further research with larger sample sizes, involving both male and female participants, using a longitudinal design.

Artificial intelligence in brain MRI analysis of Alzheimer's disease over the past 12 years: A systematic review.

INTRODUCTION: Multiple structural brain changes in Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been revealed on magnetic resonance imaging (MRI). There is a fast-growing effort in applying artificial intelligence (AI) to analyze these data. Here, we review and evaluate the AI studies in brain MRI analysis with synthesis. METHODS: A systematic review of the literature, spanning the years from 2009 to 2020, was completed using the PubMed database. AI studies using MRI imaging to investigate normal aging, mild cognitive impairment, and AD-dementia were retrieved for review. Bias assessment was completed using the PROBAST criteria. RESULTS: 97 relevant studies were included in the review. The studies were typically focused on the classification of AD, MCI, and normal aging (71% of the reported studies) and the prediction of MCI conversion to AD (25%). The best performance was achieved by using the deep learning-based convolution neural network algorithms (weighted average accuracy 89%), in contrast to 76-86% using Logistic Regression, Support Vector Machines, and other AI methods. DISCUSSION: The synthesized evidence is paramount to developing sophisticated AI approaches to reliably capture and quantify multiple subtle MRI changes in the whole brain that exemplify the complexity and heterogeneity of AD and brain aging.

Tracking cognitive changes in new-onset epilepsy: functional imaging challenges.

Functional imaging has potential for tracking changes in cognition during the onset and evolution of epilepsy. Although the concept of imaging such changes over time is an exciting new direction, feasibility remains an open question. The current article outlines a case example in which functional magnetic resonance imaging (fMRI) and event-related potentials (ERPs) were used to monitor memory changes before and after selective temporal lobe resection. From this example, three key methodologic challenges for new-onset epilepsy are identified and discussed. The first challenge relates to the interpretation of results in regions near epileptogenic tissue. We argue that this is best addressed by collecting information from multiple modalities to test for convergent evidence. The second challenge relates to optimizing the methods for sensitivity to detecting changes. In this case, enhanced imaging methods and a region-of-interest approach provide necessary focus. The third and final challenge relates to the practical difficulties of conducting research in new-onset epilepsy cases. We suggest that greater integration of imaging research within the clinical setting is needed.

Distant Sensor Prediction of Event-Related Potentials.

OBJECTIVE: The ability to measure event-related potentials (ERPs) as practical, portable brain vital signs is limited by the physical locations of electrodes. Standard electrode locations embedded within the hair result in challenges to obtaining quality signals in a rapid manner. Moreover, these sites require electrode gel, which can be inconvenient. As electrical activity in the brain is spatially volume distributed, it should be possible to predict ERPs from distant sensor locations at easily accessible mastoid and forehead scalp regions. METHODS: An artificial neural network was trained on ERP signals recorded from below hairline electrode locations (Tp9, Tp10, Af7, Af8 referenced to Fp1, Fp2) to predict signals recorded at the ideal Cz location. RESULTS: The model resulted in mean improvements in intraclass correlation coefficient relative to control for all stimulus types (Standard Tones: +9.74%, Deviant Tones: +3.23%, Congruent Words: +15.25%, Incongruent Words: +25.43%) and decreases in RMS Error (Standard Tones: -26.72%, Deviant Tones: -17.80%, Congruent Words: -28.78%, Incongruent Words: -29.61%) compared to the individual distant channels. Measured vs predicted ERP amplitudes were highly and significantly correlated with control for the N100 (R = 0.5, padj < 0.05), P300 (R = 0.75, padj < 0.01), and N400 (R = 0.75, padj < 0.01) ERPs. CONCLUSION: ERP waveforms at distant channels can be combined using a neural network autoencoder to model the control channel features with better precision than those at individual distant channels. This is the first demonstration of feasibility of predicting evoked potentials and brain vital signs using signals recorded from more distant, practical locations. SIGNIFICANCE: This solves a key engineering challenge for applications that require portability, comfort, and speed of measurement as design priorities for measurement of event-related potentials across a range of individuals, settings, and circumstances.

Accessing knowledge of the 'here and now': a new technique for capturing electromagnetic markers of orientation processing.

OBJECTIVE: The ability to orient with respect to the current context (e.g. current time or location) is crucial for daily functioning, and is used to measure overall cognitive health across many frontline clinical assessments. However, these tests are often hampered by their reliance on verbal probes (e.g. 'What city are we in?') in evaluating orientation. Objective, physiology-based measures of orientation processing are needed, but no such measures are currently in existence. We report the initial development of potential brainwave-based markers of orientation processing as characterized using electroencephlography (EEG) and magnetoencephalography (MEG). APPROACH: An auditory stimulus sequence embedded with words corresponding to orientation-relevant (i.e. related to the 'here and now') and orientation-irrelevant (i.e. unrelated to the current context) conditions was used to elicit orientation processing responses. EEG/MEG data, in concert with clinical assessments, were collected from 29 healthy adults. Analysis at sensor and source levels identified and characterized neural signals related to orientation processing. MAIN RESULTS: Orientation-irrelevant stimuli elicited increased negative amplitude in EEG-derived event-related potential (ERP) waveforms during the 390-570 ms window (p < 0.05), with cortical activations across the left frontal, temporal, and parietal regions. These effects are consistent with the well-known N400 response to semantic incongruence. In contrast, ERP responses to orientation-relevant stimuli exhibited increased positive amplitude during the same interval (p < 0.05), with activations across the bilateral temporal and parietal regions. Importantly, these differential responses were robust at the individual level, with machine-learning classification showing high accuracy (89%), sensitivity (0.88) and specificity (0.90). SIGNIFICANCE: This is the first demonstration of a neurotechnology platform that elicits, captures, and evaluates electrophysiological markers of orientation processing. We demonstrate neural responses to orientation stimuli that are validated across EEG and MEG modalities and robust at the individual level. The extraction of physiology-based markers through this technique may enable improved objective brain functional evaluation in clinical applications.

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.

Improved localization accuracy in magnetic source imaging using a 3-D laser scanner.

Brain source localization accuracy in magnetoencephalography (MEG) requires accuracy in both digitizing anatomical landmarks and coregistering to anatomical magnetic resonance images (MRI). We compared the source localization accuracy and MEG-MRI coregistration accuracy of two head digitization systems-a laser scanner and the current standard electromagnetic digitization system (Polhemus)-using a calibrated phantom and human data. When compared using the calibrated phantom, surface and source localization accuracy for data acquired with the laser scanner improved over the Polhemus by 141% and 132%, respectively. Laser scan digitization reduced MEG source localization error by 1.38 mm on average. In human participants, a laser scan of the face generated a 1000-fold more points per unit time than the Polhemus head digitization. An automated surface-matching algorithm improved the accuracy of MEG-MRI coregistration over the equivalent manual procedure. Simulations showed that the laser scan coverage could be reduced to an area around the eyes only while maintaining coregistration accuracy, suggesting that acquisition time can be substantially reduced. Our results show that the laser scanner can both reduce setup time and improve localization accuracy, in comparison to the Polhemus digitization system.

Towards brain first-aid: a diagnostic device for conscious awareness.

When the brain is damaged, evaluating an individual's level of awareness can be a major diagnostic challenge (Is he or she in there?). Existing tests typically rely on behavioral indicators, which are incorrect in as many as one out of every two cases. The current paper presents a diagnostic device that addresses this problem. The technology circumvents behavioral limitations through noninvasive brain wave measurements (electroencephalography, or EEG). Unlike traditional EEG, the device is designed for point-of-care use by incorporating a portable, user-friendly, and stable design. It uses a novel software algorithm that automates subject stimulation, data acquisition/analysis, and the reporting of results. The test provides indicators for five identifiable levels of neural processing: sensation, perception, attention, memory, and language. The results are provided as rapidly obtained diagnostic, reliability, validity, and prognostic scores. The device can be applied to a wide variety of patients across a host of different environments. The technology is designed to be wireless-enabled for remote monitoring and assessment capabilities. In essence, the device is developed to scan for conscious awareness in order to optimize subsequent patient care.