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.

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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.

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.