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.
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.
Background Resting‐state EEG measures such as spectral power in Theta (3‐7 Hz) and Alpha (8‐13 Hz) bands have been previously linked to cognitive decline in Alzheimer’s disease (AD) and other dementias. However, routine use of EEG in clinical settings has not been widely practiced. In this ongoing longitudinal study, we demonstrate how changes in EEG measures after 1‐year are correlated with actual changes in cognitive decline as measured by MMSE score of cognitive assessment. Method Five minutes of eyes‐closed resting‐state EEG were recorded during both an initial and one‐year follow‐up visits from Healthy Controls (n=67), individuals with Mild Cognitive Impairment (n=16), Alzheimer’s Disease (n=4), Dementia with Lewy‐body (n=1), Parkinson’s disease dementia (n=1), and subjects reporting memory problems without a clear diagnosis (n=7). A generalized‐linear‐model (GLM) was used to regress the relationship between predictors (MMSE and age at initial visit as well as longitudinal changes in 9 EEG measures that were selected a priori) and the outcome variable (actual changes in MMSE score after 1‐year). EEG measures included Theta‐Alpha Ratio (TAR) and relative power at 5 Hz (theta5) at temporal channels as well as relative power at 10Hz at POz channel. Result MMSE scores fell in 32% of participants and improved in 8% (Figure 1). A GLM using logarithmic link‐function with 11 predictors and 21 terms (EEG predictors and their interaction with age) was fitted to the data (F=4.24, p=2.44x10‐6). The most significant (p<0.01) predictors were age, theta5, TAR and their interaction with age, all at channel T6. Predicted and actual decline in MMSE were highly correlated (r=0.73, p=10‐5) (Figure 2). A simpler model with 12 terms (no interaction between predictors) resulted in (F=4.34, p=3.98x10‐5). Conclusion Predictive power of EEG in modeling cognitive decline was demonstrated in a cohort of patients with known or possible dementia diagnosis as well as controls. These results support the utility of EEG as a biomarker of cognitive decline. These markers could supplement other neuropathological biomarkers (such as beta‐amyloid and tau), particularly because changes in cognition may not necessarily happen at the same rate as pathological changes.
Background: Resting state electroencephalography (rsEEG) is a neurophysiologic Alzheimer disease (AD) and alpha-synucleinopathy (dementia with Lewy body (DLB) and Parkinson disease (PDD)) biomarker [1]-[5]. Background slowing (increased low frequency power, dominant rhythm (DR) slowing) is frequent in AD and DLB, more so in DLB. We sought to identify distinguishing rsEEG signatures between DLB and AD. Method: Participants included dementia patients [AD (n=26), DLB (n=12), PD (PDD; n=5)] and controls (HC; n=56). 20-channel rsEEG was collected in eyes-closed/eyesopen conditions (5-min each). Power spectral densities and coherences were measured using established methods [6]. Individual rsEEG power (delta, 1-3 Hz; theta, 3-7 Hz;alpha, 8-13 Hz), theta/alpha ratio (TAR), dominant rhythm (DR), and DR frequency domain prominence were measured and averaged across groups, and effect sizes were determined and compared using Hedges g.Result: AD, DLB, and PDD groups exhibited increased low frequency (delta-theta, 1-5 Hz) power and TAR compared to controls (p<4x10 -4 ), with higher effect sizes in the DLB and PDD groups compared to AD. DR frequency was also decreased in all patient groups (DLB>PDD>AD) compared to controls (p=3x10 -9 , p=3x10 -5 , p=4x10 -4 ).AD alpha power and DR frequency domain prominence were reduced compared to controls and PDD/DLB groups (p<0.003). Both AD patients and controls had comparable posterior power distribution predominance of DR, while DLB power of DR was more anteriorly distributed. Compared to controls, delta coherence was higher in AD, but lower in DLB. Delta power was positively correlated with cognitive fluctuation (CAF) in DLB (r(8)=+0.67, p=0.03), while negatively correlated with MMSE in AD (r(24)=-0.64, p=10 -5 ). Conclusion:DLB and AD had both common and distinct rsEEG signatures. Both had DR slowing and background slowing that was more severe in DLB than AD. Distinctive DLB findings included reduced posterior DR spatial dominance and decreased delta coherence. Conversely, distinct AD features were increased delta coherence,
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.