EEG and ERP biosignatures of mild cognitive impairment for longitudinal monitoring of early cognitive decline in Alzheimer's disease.

Cognitive decline in Alzheimer's disease is associated with electroencephalographic (EEG) biosignatures even at early stages of mild cognitive impairment (MCI). The aim of this work is to provide a unified measure of cognitive decline by aggregating biosignatures from multiple EEG modalities and to evaluate repeatability of the composite measure at an individual level. These modalities included resting state EEG (eyes-closed) and two event-related potential (ERP) tasks on visual memory and attention. We compared individuals with MCI (n = 38) to age-matched healthy controls HC (n = 44). In resting state EEG, the MCI group exhibited higher power in Theta (3-7Hz) and lower power in Beta (13-20Hz) frequency bands. In both ERP tasks, the MCI group exhibited reduced ERP late positive potential (LPP), delayed ERP early component latency, slower reaction time, and decreased response accuracy. Cluster-based permutation analysis revealed significant clusters of difference between the MCI and HC groups in the frequency-channel and time-channel spaces. Cluster-based measures and performance measures (12 biosignatures in total) were selected as predictors of MCI. We trained a support vector machine (SVM) classifier achieving AUC = 0.89, accuracy = 77% in cross-validation using all data. Split-data validation resulted in (AUC = 0.87, accuracy = 76%) and (AUC = 0.75, accuracy = 70%) on testing data at baseline and follow-up visits, respectively. Classification scores at baseline and follow-up visits were correlated (r = 0.72, p<0.001, ICC = 0.84), supporting test-retest reliability of EEG biosignature. These results support the utility of EEG/ERP for prognostic testing, repeated assessments, and tracking potential treatment outcomes in the limited duration of clinical trials.

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.

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.

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.

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.

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.

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.

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.