Proof-of-concept for characterization of neurodegenerative disorders utilizing two non-REM sleep biomarkers.

Study objective: This proof-of-concept study aimed to determine whether the combined features of two non-rapid eye movement (NREM) sleep biomarkers acquired predominantly in-home could characterize different neurodegenerative disorders. Methods: Sleep spindle duration and non-REM hypertonia (NRH) were evaluated in seven groups including a control group (CG = 61), and participants with isolated REM sleep behavior disorder (iRBD = 19), mild cognitive impairment (MCI = 41), Parkinson disease (PD = 16), Alzheimer disease dementia (ADem = 29), dementia with Lewy Bodies or Parkinson disease dementia (LBD = 19) and progressive supranuclear palsy (PSP = 13). One-way analysis of variance (ANOVA), Mann-Whitney U, intra-class (ICC) and Spearman ranked correlations, Bland-Altman plots and Kappa scores, Chi-square and Fisher exact probability test, and multiple-logistic regression were focused primarily on spindle duration and NRH and the frequencies assigned to the four normal/abnormal spindle duration/NRH combinations. Results: ANOVA identified group differences in age, sleep efficiency, REM, NRH (p < 0.0001) and sleep time (p = 0.015), Spindle duration and NRH each demonstrated good night-to-night reliabilities (ICC = 0.95 and 0.75, Kappa = 0.93 and 0.66, respectively) and together exhibited an association in the PD and LBD groups only (p < 0.01). Abnormal spindle duration was greater in records of PSP (85%) and LBD (84%) patients compared to CG, MCI, PD and ADem (p < 0.025). Abnormal NRH was greater in PSP = 92%, LBD = 79%, and iRBD = 74% compared to MCI = 32%, ADem = 17%, and CG = 16% (p < 0.005).The combination biomarker normal spindle duration/normal NRH was observed most frequently in CG (56%) and MCI (41%). ADem most frequently demonstrated normal spindle duration/normal NRH (45%) and abnormal spindle duration/normal NRH (38%). Normal spindle duration/abnormal NRH was greatest in iRBD = 47%, while abnormal spindle duration/abnormal NRH was predominant in PSP = 85% and LBD = 74%. Conclusion: The NREM sleep biomarkers spindle duration and NRH may be useful in distinguishing patients with different neurodegenerative disorders. Larger prospective cohort studies are needed to determine whether spindle duration and NRH can be combined for prodromal assessment and/or monitoring disease progression.

Autonomic dysregulation during sleep in Parkinsonian spectrum disorders - A proof of concept.

INTRODUCTION: Autonomic dysfunction is common in α-synucleinopathies such as Lewy Body dementias (LBD), Parkinson's disease (PD), and isolated REM Sleep Behavior Disorder (iRBD). We analyzed pulse-rate changes during sleep to index autonomic nervous system (ANS) dysfunction in patients with α-synucleinopathies vs. non-synucleinopathy groups expected to have normal ANS function. METHODS: Patients with LBD (n = 16), PD (PD, n = 14) or iRBD (n = 12) were compared to the non-synucleinopathy groups Alzheimers disease dementia (ADem, n = 26), mild cognitive impairment (MCI, n = 34) or controls (CG, n = 54). Sleep Profiler was used to derive a sleep autonomic activation index (AAI), i.e., ≥6 beat-per-minute increase/decrease, pulse rate coefficient of variation (PR-CV), and automated sleep staging with sleep-spindles and non-REM hypertonia (NRH). Analysis included statistical group comparisons and receiver operating characteristics curves to determine optimal classification of groups. RESULTS: AAI and PR-CV were moderately correlated across all recordings (rs = 0.58, P < 0.0001), except in the LBD and PD groups. AAI but not PR-CV differentiated the LBD, PD and iRBD from non-Parkinsonian groups. AAI was decreased in LBD and PD patients compared to the CG (p < 0.003) and MCI (p < 0.03). AAI decreased based on age and its receiver operating characteristic area under the curve ranged from 0.63 to 0.75. AAI had a weak negative correlation to NRH (rs ≤ -0.26) but not sleep-spindles. CONCLUSION: Synucleinopathy-related ANS dysfunction can reasonably discriminate prodromal and manifest PD/LBD diseased groups from non-synucleinopathies. Further studies incorporating AAI into a multivariate classifier of neurodegenerative disorders based on sleep characteristics acquired in the patient's home are planned.

Non-REM sleep with hypertonia in Parkinsonian Spectrum Disorders: A pilot investigation.

INTRODUCTION: From an ongoing multicenter effort toward differentiation of Parkinsonian spectrum disorders (PSD) from other types of neurodegenerative disorders, the sleep biomarker non-rapid-eye-movement sleep with hypertonia (NRH) emerged. METHODS: This study included in the PSD group patients with dementia with Lewy bodies/Parkinson disease dementia (DLB/PDD = 16), Parkinson disease (PD = 16), and progressive supranuclear palsy (PSP = 13). The non-PSD group included patients with Alzheimer disease dementia (AD = 24), mild cognitive impairment (MCI = 35), and a control group with normal cognition (CG = 61). In-home, multi-night Sleep Profiler studies were conducted in all participants. Automated algorithms detected NRH, characterized by elevated frontopolar electromyographic power. Between-group differences in NRH were evaluated using Logistic regression, Mann-Whitney U and Chi-squared tests. RESULTS: NRH was greater in the PSD group compared to non-PSD (13.9 ± 11.0% vs. 3.1 ± 4.7%, P < 0.0001). The threshold NRH≥5% provided the optimal between-group differentiation (AUC = 0.78, P < 0.001). NRH was independently associated with the PSD group after controlling for age, sex, and SSRI/SNRI use (P < 0.0001). The frequencies of abnormal NRH by subgroup were PSP = 92%, DLB/PDD = 81%, PD = 56%, MCI = 26%, AD = 17%, and CG = 16%. The odds of abnormal NRH in each PSD subgroup ranged from 3.7 to 61.2 compared to each non-PSD subgroup. The night-to-night and test-retest intraclass correlations were excellent (0.78 and 0.84, both P < 0.0001). CONCLUSIONS: In this pilot study, NRH appeared to be a novel candidate sleep biomarker for PSD-related neurodegeneration. Future studies in larger cohorts are needed to confirm these findings, understand the etiology of NRH magnitude/duration, and determine whether it is an independent prodromal marker for specific neurodegenerative pathologies.

Intermittent brain network reconfigurations and the resistance to social media influence.

Since its development, social media has grown as a source of information and has a significant impact on opinion formation. Individuals interact with others and content via social media platforms in a variety of ways, but it remains unclear how decision-making and associated neural processes are impacted by the online sharing of informational content, from factual to fabricated. Here, we use EEG to estimate dynamic reconfigurations of brain networks and probe the neural changes underlying opinion change (or formation) within individuals interacting with a simulated social media platform. Our findings indicate that the individuals who changed their opinions are characterized by less frequent network reconfigurations while those who did not change their opinions tend to have more flexible brain networks with frequent reconfigurations. The nature of these frequent network configurations suggests a fundamentally different thought process between intervals in which individuals are easily influenced by social media and those in which they are not. We also show that these reconfigurations are distinct to the brain dynamics during an in-person discussion with strangers on the same content. Together, these findings suggest that brain network reconfigurations may not only be diagnostic to the informational context but also the underlying opinion formation.

Perceived effects of cannabis: Generalizability of changes in driving performance.

OBJECTIVE: The objective of this analysis was to determine the generalizability of the relationship between different samples of a driver's perceived state after cannabis use and related performance while operating a motor vehicle. METHODS: Data were collected from 52 subjects in a study examining the effects of cannabis on driving performance. Data were analyzed using the SAS GLM Select procedure, using stepwise selection, with subjective effects, dosing condition (placebo vs. 6.18% delta-9-tetrahydrocannabinol [THC]), and driving context as independent measures. Correlation matrices of measures of driving performance against subjective responses and dosing condition used Pearson's and Spearman's test statistics, respectively. Results were compared to a prior study from a sample of 10 subjects. RESULTS: Subjective perceptions of acute cannabis impairment remain significant predictors of driving performance and explain individual variability in driving performance degradation as well as the data, beyond that which can be explained by acute use of cannabis alone. However, the significant subjective predictors of driving performance differ between the current and prior studies. To better understand these differences, correlations between subjective effects and performance measures were evaluated, which revealed that most correlations matched directionally (e.g., an increase in "good drug effect" was correlated with an increase in standard deviation of lane position [SDLP]). When there was a mismatch, 1 or more correlations were insignificant. Dosing condition and "stoned" were perfectly consistent; "high" and "sedated" contained 1 mismatch; and "anxious," "good drug effect" and "restless" contained 3 or more mismatches. CONCLUSIONS: The results indicate that across both studies, differences in the perceived effects of cannabis are reflected in changes in both lateral and longitudinal control beyond the acute effects of cannabis, which may help explain individual variability in response to acute intoxication. However, the generalizability of these findings is lacking, as shown by inconsistencies in when and where subjective effects were significant. Other factors such as frequency of use, usage type, the evolving profile of a cannabis user, as well as other individual differences should be considered to explain this additional variability.

Alterations in Electroencephalography Theta as Candidate Biomarkers of Acute Cannabis Intoxication.

The trend toward cannabis legalization in the United States over the past two decades has unsurprisingly been accompanied by an increase in the number of cannabis users and use patterns that potentially pose wider risks to the public like driving under the influence. As such, it is becoming increasingly important to develop methods to accurately quantify cannabis intoxication and its associated impairments on cognitive and motor function. Electroencephalography (EEG) offers a non-invasive method for quantitatively assessing neurophysiological biomarkers of intoxication and impairment with a high degree of temporal resolution. Twelve healthy, young recreational cannabis users completed a series of neurocognitive tasks with concurrent EEG acquisition using the ABM STAT X24 EEG headset in a within-subject counterbalanced design. The 1-h testbed consisted of resting state tasks and tests of attention and memory. Spectral densities were computed for resting state tasks, and event-related potentials (ERPs) were obtained for the attention and memory tasks. Theta band power (3-5 Hz) was decreased during cannabis intoxication compared to placebo during resting state tasks, as were average P400 and late positive potential (LPP) amplitudes during attention and memory tasks. Cannabis intoxication was also associated with elevated frontal coherence and diminished anterior-posterior coherence in the Theta frequency band. This work highlights the utility of EEG to identify and quantify neurophysiological biomarkers from recordings obtained during a short neurocognitive testbed as a method for profiling cannabis intoxication. These biomarkers may prove efficacious in distinguishing intoxicated from non-intoxicated individuals in lab and real-world settings.

Perceived effects of cannabis and changes in driving performance under the influence of cannabis.

OBJECTIVE: Reports indicate that cannabis users will adapt their driving to compensate for the perceived drug effects of cannabis. This analysis examined the relationship between driver perceptions of their state contrasted with objective measures of their performance while operating a motor vehicle. METHODS: Data was collected from ten subjects in a study examining the effects of cannabis on driving performance. Driving performance was collected on the NADS quarter-cab miniSim, a limited field of view non-motion simulator, approximately two hours after cannabis inhalation. Driving measures of both lateral and longitudinal control were included in our analysis. Subjective measures of the effects of cannabis were collected at peak and prior to driving, using visual analog scales. Data were analyzed using the SAS GLM Select procedure with subjective effect, dosing condition (placebo vs 6.9% THC), and driving event as independent measures. The stepwise selection method was used. RESULTS: The analysis of each of the subjective effects showed significant differences between the placebo and the active cannabis dosed conditions. While we found variance in difference between group means, there was greater variability between subject values. We found that subjective measures were predictive of variance in driver inputs, such as steering frequency and steering reversal rate. Variance in SDLP and other driving performance measures, however, were predicted by dosing condition. CONCLUSIONS: Overall, some of the effects perceived by the driver were better related to changes in driver inputs rather than the presence of cannabis itself. Changes in performance measures such as SDLP are better explained by dosing condition. Thus, driver's perceptions may result in changes to driving behavior that could mitigate the effect of cannabis. For both lateral and longitudinal control, an increasing perception of stimulation produced a positive effect on performance. Our results provide a better understanding of how different strains of cannabis, which produce different subjective experiences for users, could impact driving safety. Specifically, we found drug effects that produce more stimulation results in less impact on driving, while those that produce a more stoned or high feeling results in a greater negative effect on driving.

Elevated Inter-Brain Coherence Between Subjects With Concordant Stances During Discussion of Social Issues.

Social media platforms offer convenient, instantaneous social sharing on a mass scale with tremendous impact on public perceptions, opinions, and behavior. There is a need to understand why information spreads including the human motivations, cognitive processes, and neural dynamics of large-scale sharing. This study introduces a novel approach for investigating the effect social media messaging and in-person discussion has on the inter-brain dynamics within small groups of participants. The psychophysiological impact of information campaigns and narrative messaging within a closed social media environment was assessed using 24-channel wireless EEG. Data were acquired from three- or four-person groups while subjects debated contemporary social issues framed by four scenarios of varying controversy: (a) investing in ethical vs. unethical corporations, (b) selecting travel destination based on social awareness, (c) determining verdict in a murder trial and the punishment of life in prison or death penalty, and (d) decision to vaccinate. Pre-/post-scenario questionnaires assess the effects of the social media information. Inter-brain coherence between subject pairs on each social issue discussed by subjects was analyzed by concordance, agreement vs. disagreement, and by group unanimity, unanimous vs. not unanimous. Subject pairs that agreed on the social issues raised in the scenarios had significantly greater inter-brain coherence in gamma frequency range than disagreeing pairs over cortical regions known to be involved in social interactions. These effects were magnified when comparing groups where subject pairs were unanimous in their stance on the social issues for some but not all scenarios. While there was considerable overlap between scenarios in what EEG channels were significant, there was enough variability to indicate the possibility of scenario-specific effects on inter-brain coherence.

Resting state EEG biomarkers of cognitive decline associated with Alzheimer's disease and mild cognitive impairment.

In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.

EEG event related potentials in sustained, focused and divided attention tasks: Potential biomarkers for cognitive impairment in HIV patients.

OBJECTIVES: The objective of this study was to assess the usability of event-related-potentials (ERPs) during sustained, focused, and divided attention tasks as biomarkers for cognitive decline in HIV patients. METHODS: EEG was acquired using a mobile/wireless 9-channel system in 39 persons with HIV, with well-controlled immune function and 63 healthy control participants (HCs) during three ERP tasks: sustained attention, focused attention, and divided attention. RESULTS: The HIV-group evidenced smaller late positive potential (LPP) and larger P200 amplitudes across the tasks compared to the HC group. P200 amplitude was correlated (r = 0.56) with the estimated duration of infection. Both groups showed higher P200 and LPP amplitudes in response to infrequent stimuli; this effect was not significantly different between groups. In the sustained attention task, the HIV-group showed significantly slower reaction time than controls while maintaining the same level of accuracy. In the divided attention task, the HIV-group showed a trend towards faster/less accurate responses. CONCLUSIONS: HIV seropositive participants receiving anti-retroviral treatment (ART) demonstrated significantly larger P200 amplitude during three different attention tasks. This may reflect attentional deficits characterized by over-attending to non-target/distracting stimuli. SIGNIFICANCE: These findings demonstrate the potential benefits of EEG-ERP metrics derived from attention tasks as neurocognitive biomarkers for HIV. This approach may reveal underlying causes of attentional deficits in HIV patients.

EEG biomarkers acquired during a short, straight-line simulated drive to predict impairment from cannabis intoxication.

OBJECTIVE: As cannabis use becomes more widely accepted, there is growing interest in its effects on brain function, specifically how it may impact daily functional activities such as driving, operating machinery, and other safety-related tasks. There are currently no validated methods for quantifying impairment from acute cannabis intoxication. The objective of this study was to identify neurophysiological correlates associated with driving simulator performance in subjects who were acutely intoxicated with cannabis. These signatures could help create an EEG-based profile of impairment due to acute cannabis intoxication. METHODS: Each subject completed a three-visit study protocol. Subjects were consented and screened on the first visit. On the second and third visits, subjects were administered either 500 mg of cannabis with 6.7% delta-9-tetrahydrocannabinol (THC) or placebo using a Volcano© Digit Vaporizer in a counterbalanced fashion. EEG was acquired from subjects as they performed a series of neurocognitive tasks and an approximately 45-minute simulated drive that included a rural straight-away absent of any other cars or obstacles during the final 10 minutes.EEG data was acquired using a STAT X24 wireless sensor headset during a simulated driving scenario from 10 subjects during the THC and placebo visits. Metrics of driving performance were extracted from the driving simulator and synchronized with EEG data using a common clock. RESULTS: A within-subjects analysis showed that the standard deviation of lane position (SDLP) was significantly worse and heart rate was elevated during the dosed visit compared to the placebo visit. Consistent with our prior findings, EEG power in the Theta frequency band (4-7 Hz) in the dosed condition was significantly decreased from the placebo condition. Theta power was negatively correlated with the SDLP driving performance metric, while there were no significant correlations between any EEG measure and SDLP in the placebo condition. CONCLUSIONS: These results, in combination with prior work on the effect of cannabis intoxication during neurocognitive tasks, suggest that neurophysiological signatures associated with acute cannabis intoxication are robust and consistent across tasks, and that these signatures are significantly correlated with impaired performance in a driving simulator. Taken together, EEG data acquired during a short neurocognitive testbed and during a simulated drive may provide specific profiles of impairment associated with acute cannabis intoxication. Further research is needed to establish the impaired cognitive processes associated with these EEG biomarkers.

Neurophysiological Biomarkers of Parkinson's Disease.

BACKGROUND: There is a need for reliable and robust Parkinson's disease biomarkers that reflect severity and are sensitive to disease modifying investigational therapeutics. OBJECTIVE: To demonstrate the utility of EEG as a reliable, quantitative biomarker with potential as a pharmacodynamic endpoint for use in clinical assessments of neuroprotective therapeutics for Parkison's disease. METHODS: A multi modal study was performed including aquisition of resting state EEG data and dopamine transporter PET imaging from Parkinson's disease patients off medication and compared against age-matched controls. RESULTS: Qualitative and test/retest analysis of the EEG data demonstrated the reliability of the methods. Source localization using low resolution brain electromagnetic tomography identified significant differences in Parkinson's patients versus control subjects in the anterior cingulate and temporal lobe, areas with established association to Parkinson's disease pathology. Changes in cortico-cortical and cortico-thalamic coupling were observed as excessive EEG beta coherence in Parkinson's disease patients, and correlated with UPDRS scores and dopamine transporter activity, supporting the potential for cortical EEG coherence to serve as a reliable measure of disease severity. Using machine learning approaches, an EEG discriminant function analysis classifier was identified that parallels the loss of dopamine synapses as measured by dopamine transporter PET. CONCLUSION: Our results support the utility of EEG in characterizing alterations in neurophysiological oscillatory activity associated with Parkinson's disease and highlight potential as a reliable method for monitoring disease progression and as a pharmacodynamic endpoint for Parkinson's disease modification therapy.

Correlation of EEG biomarkers of cannabis with measured driving impairment.

Objective: The objective of this study was to use electroencephalogram (EEG) biomarkers derived from a short, easily administered neurocognitive testbed to determine acute cannabis intoxication and its effect on driving performance in a driving simulator.Methods: The data analyzed were from a study examining the relationship between psychomotor task performance, EEG data, and driving performance in a simulator. EEG data were collected using a STAT® X-24 EEG Wireless Sensor Headset, which was worn during the psychomotor and driving tasks. Driving data were collected for segments of consistent driving environments, including urban driving, urban curves, interstate, interstate curves, dark rural, and rural straightaways. Dependent measures included measures of lateral and longitudinal vehicle control.Results: There was a significant relationship between impaired driving performance as indicated by increased standard deviation of lane position and EEG power in slow theta band (3-5 Hz) in parietal and occipital areas.Conclusions: These results, combined with our prior reported results, suggest that EEG and electrocardiogram (ECG) acquired concurrent with neuropsychological tests hold potential to provide a highly sensitive, specific, and dose-dependent profile of cannabis intoxication and level of impairment.

Transcranial Impedance Changes during Sleep: A Rheoencephalography Study.

OBJECTIVE: To demonstrate the utility of rheoencephalography (REG) for measuring cerebral blood flow and fluid dynamics during different stages of sleep. METHODS: Anteroposterior cranial electrical impedance was measured with concurrent polysomnography in a group of healthy subjects during sleep. Transcranial electrical impedance was characterized by measuring the peak-to-trough and envelope of the filtered pulsative REG signal as well as its frequency. The sensitivity of the REG amplitude to changes in cerebral blood flow (CBF) was confirmed by the analysis of the signal during breathing maneuvers with known effects on CBF. The mean amplitude and variability of the REG characteristic parameters were averaged across all participants and were compared between different stages of sleep. RESULTS: Average transcranial impedance was significantly lower during non-REM stages N1 and N2, compared to other sleep stages, suggesting a decrease in CBF volume. Stage N3 showed the slowest frequency indicating a slow heart rate during this stage. N3 also had the lowest variability in frequency and peak-to-trough amplitude. CONCLUSION: Measurement of transcranial electrical conductivity may be a viable non-invasive method for monitoring any potential changes in intracranial fluid homeostasis. Clinical Impact: In the absence of other convenient non-invasive methods, using REG to track intracranial fluid dynamics during sleep can facilitate an improved understanding of pathogenesis in Alzheimer's disease.

Head Position During Sleep: Potential Implications for Patients with Neurodegenerative Disease.

BACKGROUND: The characterization of sleep in those with neurodegenerative disease (NDD) is essential in understanding the potential neurobiological mechanisms that underlie the connection between sleep disruption and NDD manifestations and progression. OBJECTIVE: Explore the inter-relationships between NDD and age, sex, diagnosis of obstructive sleep apnea, snoring, and duration of sleep time with the head in the supine and non-supine positions. METHODS: A case-control design was used to evaluate differences in sleep position obtained from multi-night, in-home Sleep Profiler recordings in 45 patients with diagnosed NDD (24 with mild cognitive impairment, 15 with Alzheimer's disease, and 6 with Lewy Body, Parkinson's, or other dementias) and 120 age-sex matched controls with normal cognition (NC). RESULTS: The frequency of supine sleep >2 h/night was significantly greater in the NDD than in the NC group (p < 0.001, odds ratio = 3.7), and remained significant after controlling for age, sex, snoring, and obstructive sleep apnea diagnosis (p = 0.01). There were no group differences in nocturnal mobility i.e., number of head position changes/h. CONCLUSION: This study demonstrates the utility of in-home measurements of sleep in defining the association of supine sleep position with neurodegenerative disorders. Our findings warrant further investigation, particularly in light of the recent evidence suggesting that sleep may an active role in the brain's ability to clear CNS neurotoxins and metabolites.

Comparison of EMG power during sleep from the submental and frontalis muscles.

OBJECTIVE: Submentalis electromyography (sEMG) and frontalis electromyography (fEMG) muscle activities have been used to assist in the staging of sleep and detection of disruptions in sleep. This study was designed to assess the concordance between sEMG and fEMG power, by and across sleep stages. METHODS: Forty-three records with simultaneous acquisition of differential signals from the submental and frontalis muscles were evaluated. Sleep stages were assigned using the poly-somnography signals based on majority agreement of five technicians. The sEMG and fEMG signals were identically filtered and aligned prior to cross-correlation analysis. RESULTS: A strong concordance between sEMG and fEMG power was observed, with 95% of the records exhibiting at least moderate agreement. During rapid eye movement (REM) sleep, sEMG power was significantly less than fEMG power, but exhibited four times greater across-subject variability. fEMG power during wake and non-REM (NREM) sleep was greater than sEMG power, but with 50% less variability. Differences in wake and N1 mean power and between the other sleep stages were more distinct in the fEMG recordings. Relative changes in sEMG and fEMG power across wake, NREM, and REM stages were essentially identical with median by-subject cross correlations of 0.98 and interquartile ranges of 0.97 and 0.99, respectively. CONCLUSION: The fEMG and sEMG power values were similar during wakefulness and sleep; however, the frontalis exhibits substantially less between-subject variability. This study established face validity for the use of fEMG in the detection of wake and stages of sleep, and for future applications toward assessment of quantitative REM sleep muscle activity in REM sleep behavior disorder.

Event-related potentials during sustained attention and memory tasks: Utility as biomarkers for mild cognitive impairment.

INTRODUCTION: The objective of the study is to validate attention and memory tasks that elicit event-related potentials (ERPs) for utility as sensitive biomarkers for early dementia. METHODS: A 3-choice vigilance task designed to evaluate sustained attention and standard image recognition memory task designed to evaluate attention, encoding, and image recognition memory were administered with concurrent electroencephalography acquisition to elicit ERPs in mild cognitive impairment (MCI) and healthy cohorts. ERPs were averaged, and mean or maximum amplitude of components was measured and compared between and within cohorts. RESULTS: There was significant suppression of the amplitude of the late positive potential in the MCI cohort compared with the healthy controls during 3-choice vigilance task, predominantly over occipital and right temporal-parietal region, and standard image recognition memory task over all regions. During standard image recognition memory task, diminished performance showed strong correlation with electroencephalography measurements. The old/new effects observed in the healthy controls cohort correlated with performance and were lost in MCI. DISCUSSION: ERPs obtained during cognitive tasks may provide a powerful tool for assessing MCI and have strong potential as sensitive and robust biomarkers for tracking disease progression and evaluating response to investigative therapeutics.

EEG-Based Neurocognitive Metrics May Predict Simulated and On-Road Driving Performance in Older Drivers.

The number of older drivers is steadily increasing, and advancing age is associated with a high rate of automobile crashes and fatalities. This can be attributed to a combination of factors including decline in sensory, motor, and cognitive functions due to natural aging or neurodegenerative diseases such as HIV-Associated Neurocognitive Disorder (HAND). Current clinical assessment methods only modestly predict impaired driving. Thus, there is a need for inexpensive and scalable tools to predict on-road driving performance. In this study EEG was acquired from 39 HIV+ patients and 63 healthy participants (HP) during: 3-Choice-Vigilance Task (3CVT), a 30-min driving simulator session, and a 12-mile on-road driving evaluation. Based on driving performance, a designation of Good/Poor (simulator) and Safe/Unsafe (on-road drive) was assigned to each participant. Event-related potentials (ERPs) obtained during 3CVT showed increased amplitude of the P200 component was associated with bad driving performance both during the on-road and simulated drive. This P200 effect was consistent across the HP and HIV+ groups, particularly over the left frontal-central region. Decreased amplitude of the late positive potential (LPP) during 3CVT, particularly over the left frontal regions, was associated with bad driving performance in the simulator. These EEG ERP metrics were shown to be associated with driving performance across participants independent of HIV status. During the on-road evaluation, Unsafe drivers exhibited higher EEG alpha power compared to Safe drivers. The results of this study are 2-fold. First, they demonstrate that high-quality EEG can be inexpensively and easily acquired during simulated and on-road driving assessments. Secondly, EEG metrics acquired during a sustained attention task (3CVT) are associated with driving performance, and these metrics could potentially be used to assess whether an individual has the cognitive skills necessary for safe driving.

Comparison of Medical and Consumer Wireless EEG Systems for Use in Clinical Trials.

Objectives: To compare quantitative EEG signal and test-retest reliability of medical grade and consumer EEG systems. Methods: Resting state EEG was acquired by two medical grade (B-Alert, Enobio) and two consumer (Muse, Mindwave) EEG systems in five healthy subjects during two study visits. EEG patterns, power spectral densities (PSDs) and test/retest reliability in eyes closed and eyes open conditions were compared across the four systems, focusing on Fp1, the only common electrode. Fp1 PSDs were obtained using Welch's modified periodogram method and averaged for the five subjects for each visit. The test/retest results were calculated as a ratio of Visit 1/Visit 2 Fp1 channel PSD at each 1 s epoch. Results: B-Alert, Enobio, and Mindwave Fp1 power spectra were similar. Muse showed a broadband increase in power spectra and the highest relative variation across test-retest acquisitions. Consumer systems were more prone to artifact due to eye blinks and muscle movement in the frontal region. Conclusions: EEG data can be successfully collected from all four systems tested. Although there was slightly more time required for application, medical systems offer clear advantages in data quality, reliability, and depth of analysis over the consumer systems. Significance: This evaluation provides evidence for informed selection of EEG systemsappropriate for clinical trials.