Moment to moment variability in functional brain networks during cognitive activity in EEG data.

Functional brain networks (FBNs) are gaining increasing attention in computational neuroscience due to their ability to reveal dynamic interdependencies between brain regions. The dynamics of such networks during cognitive activity between stimulus and response using multi-channel electroencephalogram (EEG), recorded from 16 healthy human participants are explored in this research. Successive EEG segments of 500[Formula: see text]ms duration starting from the onset of cognitive stimulation have been used to analyze and understand the cognitive dynamics. The approach employs a combination of signal processing techniques, nonlinear statistical measures and graph-theoretical analysis. The efficacy of this approach in detecting and tracking cognitive load induced changes in EEG data is clearly demonstrated using graph metrics. It is revealed that most cognitive activity occurs within approximately 500[Formula: see text]ms of the stimulus presentation in addition to temporal variability in the FBNs. It is shown that mutual information (MI), a nonlinear measure, produces good correlations between the EEG channels thus enabling the construction of FBNs which are sensitive to cognitive load induced changes in EEG. Analyses of the dynamics of FBNs and the visualization approach reveal hard to detect subtle changes in cognitive function and hence may lead to a better understanding of cognitive processing in the brain. The techniques exploited have the potential to detect human cognitive dysfunction (impairments).

Evaluation of antibacterial activity of some traditionally used medicinal plants against human pathogenic bacteria.

The worldwide increase of multidrug resistance in both community- and health-care associated bacterial infections has impaired the current antimicrobial therapy, warranting the search for other alternatives. We aimed to find the in vitro antibacterial activity of ethanolic extracts of 16 different traditionally used medicinal plants of Nepal against 13 clinical and 2 reference bacterial species using microbroth dilution method. The evaluated plants species were found to exert a range of in vitro growth inhibitory action against the tested bacterial species, and Cynodon dactylon was found to exhibit moderate inhibitory action against 13 bacterial species including methicillin-resistant Staphylococcus aureus, imipenem-resistant Pseudomonas aeruginosa, multidrug-resistant Salmonella typhi, and S. typhimurium. The minimum inhibitory concentration (MIC) values of tested ethanolic extracts were found from 31 to >25,000 µg/mL. Notably, ethanolic extracts of Cinnamomum camphora, Curculigo orchioides, and Curcuma longa exhibited the highest antibacterial activity against S. pyogenes with a MIC of 49, 49, and 195 µg/mL, respectively; whereas chloroform fraction of Cynodon dactylon exhibited best antibacterial activity against S. aureus with a MIC of 31 µg/mL. Among all, C. dactylon, C. camphora, C. orchioides, and C. longa plant extracts displayed a potential antibacterial activity of MIC < 100 µg/mL.

TVAR modeling of EEG to detect audio distraction during simulated driving.

OBJECTIVE: The objective of our current study was to look for the EEG correlates that can reveal the engaged state of the brain while undertaking cognitive tasks. Specifically, we aimed to identify EEG features that could detect audio distraction during simulated driving. APPROACH: Time varying autoregressive (TVAR) analysis using Kalman smoother was carried out on short time epochs of EEG data collected from participants as they undertook two simulated driving tasks. TVAR coefficients were then used to construct all pole model enabling the identification of EEG features that could differentiate normal driving from audio distracted driving. MAIN RESULTS: Pole analysis of the TVAR model led to the visualization of event related synchronization/desynchronization (ERS/ERD) patterns in the form of pole displacements in pole plots of the temporal EEG channels in the z plane enabling the differentiation of the two driving conditions. ERS in the EEG data has been demonstrated during audio distraction as an associated phenomenon. SIGNIFICANCE: Visualizing the ERD/ERS phenomenon in terms of pole displacement is a novel approach. Although ERS/ERD has previously been demonstrated as reliable when applied to motor related tasks, it is believed to be the first time that it has been applied to investigate human cognitive phenomena such as attention and distraction. Results confirmed that distracted/non-distracted driving states can be identified using this approach supporting its applicability to cognition research.