The effect of hair type and texture on electroencephalography and event-related potential data quality.

Research utilizing event-related potential (ERP) methods is generally biased with regard to sample representativeness. Among the myriad of factors that contribute to sample bias are researchers' assumptions about the extent to which racial differences in hair texture, volume, and style impact electrode placement, and subsequently, study eligibility. The current study examines these impacts using data collected from n = 213 individuals ages 17-19 years, and offers guidance on collection of ERP data across the full spectrum of hair types. Individual differences were quantified for hair texture using a visual scale, and for hair volume by measuring the amount of gel used in cap preparation. Electroencephalography data quality was assessed with multiple metrics at the preprocessing, post-processing, and variable generation stages. Results indicate that hair volume is associated with small, but systematic differences in signal quality and signal amplitude. Such differences are highly problematic as they could be misattributed to cognitive differences among groups. However, inclusion of gel volume as a covariate to account for individual differences in hair volume significantly reduced, and in most cases eliminated, group differences. We discuss strategies for overcoming real and perceived technical barriers for researchers seeking to achieve greater inclusivity and representativeness in ERP research.

Psychophysiology of Monotonous Driving, Fatigue and Sleepiness in Train and Non-Professional Drivers: Driver Safety Implications.

Fatigue and sleepiness are complex bodily states associated with monotony as well as physical and cognitive impairment, accidents, injury, and illness. Moreover, these states are often characteristic of professional driving. However, most existing work has focused on motor vehicle drivers, and research examining train drivers remains limited. As such, the present study psychophysiologically examined monotonous driving, fatigue, and sleepiness in a group of passenger train drivers and a group of non-professional drivers. Sixty-three train drivers and thirty non-professional drivers participated in the present study, which captured 32-lead electroencephalogram (EEG) data during a monotonous driving task. Fatigue and sleepiness were self-evaluated using the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Karolinksa Sleepiness Scale, and the Checklist of Individual Strength. Unexpectedly, fatigue and sleepiness scores did not significantly differ between the groups; however, train drivers generally scored lower than non-professional drivers, which may be indicative of individual and/or industry attempts to reduce fatigue. Across both groups, fatigue and sleepiness scores were negatively correlated with theta, alpha, and beta EEG variables clustered towards the fronto-central and temporal regions. Broadly, these associations may reflect a monotony-associated blunting of neural activity that is associated with a self-reported fatigue state.

Decision-making in uncertain contexts: The role of autonomic markers in resolving indecision.

Although impulsivity is associated with an increased willingness to make risky decisions, uncertainty intolerance may also contribute to maladaptive decision-making behavior, where individuals neglect to pursue potential rewards even when probabilities for success are in their favor. Several theories have sought to explain the neural systems that guide decision-making in this context, with evidence supporting a role for increased sympathetic activation. However, it remains unclear whether the sympathetic system is associated with greater apprehension in response to uncertain outcomes, or whether it serves to guide behavioral decisions in the context of this uncertainty. Furthermore, although postulated as a within-person process, most research has examined the association between decision behavior and sympathetic activation at the between-person level. We hypothesize that in the context of uncertainty between-person differences in skin conductance will be associated with longer deliberation times; whereas within-person trial-level increases in skin conductance will be associated with a tendency to reject uncertain options. Data were collected from n = 56 children aged 7-11 years, using a computerized card game in which children chose to accept or reject cards of varying point value at varying levels of probability. Skin conductance level (SCL) was recorded throughout the task. No significant between-person associations emerged. However, within-person analyses indicated that momentary deliberation time moderated the association between momentary skin-conductance and decision outcome. This moderation was such that for trials during which the individual deliberated longer (i.e., was more indecisive), a concurrent increase in skin conductance was associated with a significantly higher likelihood of rejecting the card. The within-person nature of these results suggests that skin conductance may help in resolving indecision in the context of uncertainty.

The relationship between neurocognitive performance and HRV parameters in nurses and non-healthcare participants.

Nurses represent the largest sector of the healthcare workforce, and it is established that they are faced with ongoing physical and mental demands that leave many continuously stressed. In turn, this chronic stress may affect cardiac autonomic activity, which can be non-invasively evaluated using heart rate variability (HRV). The association between neurocognitive parameters during acute stress situations and HRV has not been previously explored in nurses compared to non-nurses and such, our study aimed to assess these differences. Neurocognitive data were obtained using the Mini-Mental State Examination and Cognistat psychometric questionnaires. ECG-derived HRV parameters were acquired during the Trier Social Stress Test. Between-group differences were found in domain-specific cognitive performance for the similarities (p = .03), and judgment (p = .002) domains and in the following HRV parameters: SDNNbaseline, (p = .004), LFpreparation (p = .002), SDNNpreparation (p = .002), HFpreparation (p = .02), and TPpreparation (p = .003). Negative correlations were found between HF power and domain-specific cognitive performance in nurses. In contrast, both negative and positive correlations were found between HRV and domain-specific cognitive performance in the non-nurse group. The current findings highlight the prospective use of autonomic HRV markers in relation to cognitive performance while building a relationship between autonomic dysfunction and cognition.

Developmental changes in external and internal performance monitoring across middle childhood: An ERP study.

Performance monitoring is critical for learning and behavioral adaption and is supported by both externally and internally sourced information. Cross-sectional studies indicate an increase in internal error processing across childhood, suggesting a potential developmental transition from reliance on external information to reliance on internally developed models. However, little research has examined the association between these constructs longitudinally. Data from 339 children assessed annually from kindergarten to 2nd grade were examined to determine the developmental trajectory of ERP indices of performance monitoring, and whether the association between these indices changes across time. EEG data were recorded during an incentivized Go/No-Go task and ERP component amplitudes were extracted as peak measures at Fz. Despite small increases in magnitude, no significant changes were observed in any of the ERPs. Multi-level regression analyses indicated that in kindergarten a more negative feedback-related negativity (FRN) was associated with a more negative error-related negativity (ERN) and a more negative error positivity (Pe). Further, the association between the FRN and Pe changed over time, such that in 2nd grade the FRN and Pe decoupled from one another and were no longer associated. These results suggest that the development of performance monitoring through middle childhood may be a phasic process. More specifically, matured external feedback monitoring processes may first facilitate the development of conscious error recognition, and then the development of internal error monitoring processes. Once internal models of error monitoring are well-established, children may then reduce their utilization of external feedback.

Association between EEG asymmetry and the error-related negativity across middle childhood.

Resting frontal EEG alpha asymmetry has been extensively examined as a marker of motivational disposition. Recent research has examined how this trait-level marker of motivation influences an individual's internal error monitoring (indexed by the error-related negativity; ERN), with mixed findings as to whether more negative ERNs are associated with greater left or right alpha power. Data from 339 children who completed an incentivized Go/No-Go task annually from Kindergarten through 2nd grade were examined for an association between ERN amplitude and EEG asymmetry, and for whether the association was developmentally stable. Results indicate an association between left-dominant activation and a more negative amplitude in Kindergarten, with an inversion of this association emerging by 2nd grade, such that a more negative ERNs were associated with right-dominant activation. We suggest that the association between EEG asymmetry and ERN amplitude is likely modulated by task condition (e.g., incentivization) and experience over time (e.g., habituation).

Classifying Multi-Level Stress Responses From Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder.

OBJECTIVE: Mental stress is a major problem in our society and has become an area of interest for many psychiatric researchers. One primary research focus area is the identification of bio-markers that not only identify stress but also predict the conditions (or tasks) that cause stress. Electroencephalograms (EEGs) have been used for a long time to study and identify bio-markers. While these bio-markers have successfully predicted stress in EEG studies for binary conditions, their performance is suboptimal for multiple conditions of stress. METHODS: To overcome this challenge, we propose using latent based representations of the bio-markers, which have been shown to significantly improve EEG performance compared to traditional bio-markers alone. We evaluated three commonly used EEG based bio-markers for stress, the brain load index (BLI), the spectral power values of EEG frequency bands (alpha, beta and theta), and the relative gamma (RG), with their respective latent representations using four commonly used classifiers. RESULTS: The results show that spectral power value based bio-markers had a high performance with an accuracy of 83%, while the respective latent representations had an accuracy of 91%.

Electrophysiological Brain-Cardiac Coupling in Train Drivers during Monotonous Driving.

Electrophysiological research has previously investigated monotony and the cardiac health of drivers independently; however, few studies have explored the association between the two. As such the present study aimed to examine the impact of monotonous train driving (indicated by electroencephalogram (EEG) activity) on an individual's cardiac health as measured by heart rate variability (HRV). Sixty-three train drivers participated in the present study, and were required to complete a monotonous train driver simulator task. During this task, a 32 lead EEG and a three-lead electrocardiogram were recorded from each participant. In the present analysis, the low (LF) and high frequency (HF) HRV parameters were associated with delta (p < 0.05), beta (p = 0.03) and gamma (p < 0.001) frequency EEG variables. Further, total HRV was associated with gamma activity, while sympathovagal balance (i.e., LF:HF ratio) was best associated fronto-temporal delta activity (p = 0.02). HRV and EEG parameters appear to be coupled, with the parameters of the delta and gamma EEG frequency bands potentially being the most important to this coupling. These relationships provide insight into the impact of a monotonous task on the cardiac health of train drivers, and may also be indicative of strategies employed to combat fatigue or engage with the driving task.

Electroencephalographic prediction of global and domain specific cognitive performance of clinically active Australian Nurses.

OBJECTIVE: To investigate the relationship between EEG activity and the global and domain specific cognitive performance of healthy nurses, and determine the predictive capabilities of these relationships. APPROACH: Sixty-four nurses were recruited for the present study, and data from 61 were utilised in the present analysis. Global and domain specific cognitive performance of each participant was assessed psychometrically using the Mini-mental state exam and the Cognistat, and a 32-lead monopolar EEG was recorded during a resting baseline phase and an active phase in which participants completed the Stroop test. MAIN RESULTS: Global cognitive performance was successfully predicted (81%-85% of variance) by a combination of fast wave activity variables in the alpha, beta and theta frequency bands. Interestingly, predicting domain specific performance had varying degrees of success (42%-99% of the variance predicted) and relied on combinations of both slow and fast wave activity, with delta and gamma activity predicting attention performance; delta, theta, and gamma activity predicting memory performance; and delta and beta variables predicting judgement performance. SIGNIFICANCE: Global and domain specific cognitive performance of Australian nurses may be predicted with varying degrees of success by a unique combination of EEG variables. These proposed models image transitory cognitive declines and as such may prove useful in the prediction of early cognitive impairment, and may enable better diagnosis, and management of cognitive impairment.

Impact of acute stress on cortical electrical activity and cardiac autonomic coupling.

Assessment of heart rate variability (reflective of the cardiac autonomic nervous system) has shown some predictive power for stress. Further, the predictive power of the distinct patterns of cortical brain activity and - cardiac autonomic interactions are yet to be explored in the context of acute stress, as assessed by an electrocardiogram and electroencephalogram. The present study identified distinct patterns of neural-cardiac autonomic coupling during both resting and acute stress states. In particular, during the stress task, frontal delta waves activity was positively associated with low-frequency heart rate variability and negatively associated with high-frequency heart rate variability. Low high-frequency power is associated with stress and anxiety and reduced vagal control. A positive association between resting high-frequency heart rate variability and frontocentral gamma activity was found, with a direct inverse relationship of low-frequency heart rate variability and gamma wave coupling at rest. During the stress task, low-frequency heart rate variability was positively associated with frontal delta activity. That is, the parasympathetic nervous system is reduced during a stress task, whereas frontal delta wave activity is increased. Our findings suggest an association between cardiac parasympathetic nervous system activity and frontocentral gamma and delta activity at rest and during acute stress. This suggests that parasympathetic activity is decreased during acute stress, and this is coupled with neuronal cortical prefrontal activity. The distinct patterns of neural-cardiac coupling identified in this study provide a unique insight into the dynamic associations between brain and heart function during both resting and acute stress states.

A systematic review of the current evidence regarding interventions for anxiety, PTSD, sleepiness and fatigue in the law enforcement workplace.

Law enforcement is inherently stressful, and police officers are particularly vulnerable to mental and physical disorders. As such, researchers are currently assessing intervention strategies that may combat or manage these psychological, physical and mental issues. To review most recent information regarding anxiety, PTSD, and sleepiness and fatigue and identify the interventions and treatments proposed to overcome work related stressors and associated mental illnesses inflicting law enforcement officers. The EMBASE, OVID MEDLINE and PsycINFO databases were canvassed for articles investigating anxiety, post-traumatic stress disorder, sleepiness, and fatigue. Initial article selections were made based on title, whilst final inclusion was informed by a full critical appraisal with respect to the primary and secondary effects. The systematic search returned 363 records, of which 183 were unique. Following screening, 43 records were included in the final review. The included literature assessed the efficacy of several interventions, and provided a number of recommendations regarding interventions, and policy. Moreover, literature indicates that police officers benefit from interventions targeting work-related stress and potential psychological disorders, if these interventions are continuous. Furthermore, larger controlled studies are required to further elucidate the benefits of psychosocial intervention in law enforcement.

Prevalence and Risk Factors of Depression, Anxiety, and Stress in a Cohort of Australian Nurses.

Nurses remain at the forefront of patient care. However, their heavy workload as a career can leave them overworked and stressed. The demanding nature of the occupation exposes nurses to a higher risk of developing negative mental states such as depression, anxiety, and stress. Hence, the current study aimed to assess the prevalence and risk factors of these mental states in a representative sample of Australian nurses. The Depression Anxiety Stress Scale was administered to 102 nurses. Information about demographic and work characteristics were obtained using lifestyle and in-house designed questionnaires. Prevalence rates of depression, anxiety, and stress were found to be 32.4%, 41.2%, and 41.2% respectively. Binominal logistic regressions for depression and stress were significant (p = 0.007, p = 0.009). Job dissatisfaction significantly predicted a higher risk of nurses developing symptoms of depression and stress respectively (p = 0.009, p = 0.011). Poor mental health among nurses may not only be detrimental to the individual but may also hinder professional performance and in turn, the quality of patient care provided. Further research in the area is required to identify support strategies and interventions that may improve the health and wellbeing of nursing professionals and hence the quality of care delivered.

Electroencephalography as a predictor of self-report fatigue/sleepiness during monotonous driving in train drivers.

OBJECTIVE: In this study, electroencephalography activity recorded during monotonous driving was investigated to examine the predictive capability of monopolar EEG analysis for fatigue/sleepiness in a cohort of train drivers. APPROACH: Sixty-three train drivers participated in the study, where 32- lead monopolar EEG data was recorded during a monotonous driving task. Participant sleepiness was assessed using the Pittsburgh sleep quality index (PSQI), the Epworth sleepiness scale (ESS), the Karolinksa sleepiness scale (KSS) and the checklist of individual strength 20 (CIS20). MAIN RESULTS: Self-reported fatigue/sleepiness scores of the train driver cohort were primarily associated with EEG delta, theta, and alpha variables; however, some beta and gamma associations were also implicated. Furthermore, general linear models informed by these EEG variables were able to predict self-reported scores with varying degrees of success, representing between 48% and 54% of variance in fatigue scores. SIGNIFICANCE: Self-reported fatigue/sleepiness scores of train drivers were predicted with varying degrees of success (dependent upon the self-reported fatigue/sleepiness measure) by alterations to monopolar delta, theta, and alpha band activity variables, indicating EEG as a potential indicator for fatigue/sleepiness in train drivers.

Heart Rate Variability as a Biomarker for Predicting Stroke, Post-stroke Complications and Functionality.

BACKGROUND: Heart rate variability (HRV) is a non-invasive measure of the function of the autonomic nervous system, and its dynamic nature may provide a means through which stroke and its associated complications may be predicted, monitored, and managed. OBJECTIVE: The objective of this review is to identify and provide a critique on the most recent uses of HRV in stroke diagnosis/management and highlight areas that warrant further research. METHODS: The MEDLINE, CINAHL, and OVID MEDLINE databases were canvassed using a systematic search strategy, for articles investigating the use of HRV in stroke diagnosis and management. Initial paper selections were based on title alone, and final paper inclusion was informed by a full-text critical appraisal. RESULTS: The systematic search returned 98 records, of which 51 were unique. Following screening, 22 records were included in the final systematic review. The included papers provided some information regarding predicting incident stroke, which largely seems to be best predicted by time- and frequency-domain HRV parameters. Furthermore, post-stroke complications and functionality are similarly predicted by time- and frequency-domain parameters, as well as non-linear parameters in some instances. CONCLUSIONS: Current research provides good evidence that HRV parameters may have utility as a biomarker for stroke and for post-stroke complications and/or functionality. Future research would benefit from the integration of non-linear, and novel parameters, the hybridisation of HRV parameters, and the expansion of the utilisation of predictive regression and hazard modelling.

Recent advances in molecular biomarkers for diabetes mellitus: a systematic review.

CONTEXT: Diabetes is a growing global metabolic epidemic. Current research is focussing on exploring how the biological processes and clinical outcomes of diabetes are related and developing novel biomarkers to measure these relationships, as this can subsequently improve diagnostic, therapeutic and management capacity. OBJECTIVE: The objective of this study is to identify the most recent advances in molecular biomarkers of diabetes and directions that warrant further research. METHODS: Using a systematic search strategy, the MEDLINE, CINAHL and OVID MEDLINE databases were canvassed for articles that investigated molecular biomarkers for diabetes. Initial selections were made based on article title, whilst final inclusion was informed by a critical appraisal of the full text of each article. RESULTS: The systematic search returned 246 records, of which 113 were unique. Following screening, 29 records were included in the final review. Three main research strategies (the development of novel technologies, broad biomarker panels, and targeted approaches) identified a number of potential biomarkers for diabetes including miR-126, C-reactive protein, 2-aminoadipic acid and betatrophin. CONCLUSION: The most promising research avenue identified is the detection and quantification of micro RNA. Further, the utilisation of functionalised electrodes as a means to detect biomarker compounds also warrants attention.

Stress and its Impact on the Neurocognitive Performance of Australian Nurses.

Nurses function inside a particularly stressful occupation that requires the provision of continuous care to individuals who are often in great need. Stress has been shown to impair performance and specifically shown to impair nursing quality. However, we do not yet know how stress influences the cognitive performance of nurses, and hence, the present study investigated the associations between stress and cognitive performance in nurses using electroencephalography and administered cognitive assessments. Thirty-six nurses (34 women) of mean age 37.77 ± 11.40 years were recruited. Stress was examined using the Lifestyle Appraisal Questionnaire. Broad spectrum electroencephalogram activity at positions Fp1, Fp2, C3 and C4 was recorded for a 5-min baseline and active phase to physiologically assess cognitive performance. Additionally, the Mini-Mental State Exam and Cognistat were also used to measure cognitive performance. Assessed cognitive performance was not associated to stress, however, lifestyle factors, as well as a number of the examined cognitive electroencephalographic variables including changes in theta, alpha activity and gamma reactivity were. Definitively determining how stress affects the cognitive performance of nurses requires additional research; the present study forms a foundation from which future research can further expand the examination of stress exposure in nurses. Copyright © 2016 John Wiley & Sons, Ltd.

Electroencephalogram associations to cognitive performance in clinically active nurses.

Cognitive impairment is traditionally identified via cognitive screening tools that have limited ability in detecting early or transitional stages of impairment. The dynamic nature of physiological variables such as the electroencephalogram (EEG) may provide alternate means for detecting these transitions. However, previous research examining EEG and cognitive performance is largely confined to samples with diagnosed cognitive impairments, and research examining non-impaired, and occupation specific samples, is limited. The present study aimed to investigate the associations between frontal pole and central EEG and cognitive performance in a sample of male and female nurses, and to determine the significance of these associations. Fifty seven nurses participated in the study, in which two lead bipolar EEG was recorded at positions Fp1 (frontal polar), Fp2, C3 (central) and C4 during a baseline and an active phase involving the common neuropsychological Stroop test. Participants' cognitive performance was assessed using the mini-mental state exam (MMSE) and Cognistat screening tools. Significant correlations between EEG beta activity and the outcome of MMSE and Cognistat were revealed, where an increased beta activity was associated to an increased global cognitive performance. Additionally, domain specific cognitive performance was also significantly associated to various EEG variables. The study identified potential EEG biomarkers for global and domain specific cognitive performance, and provides initial groundwork for the development of future EEG based biomarkers for detection of cognitive pathologies.

Association Between Heart Rate Variability Measures and Blood Glucose Levels: Implications for Noninvasive Glucose Monitoring for Diabetes.

BACKGROUND: Diabetes mellitus (DM) is a global metabolic epidemic associated with numerous adverse complications. Invasive finger prick tests or invasive monitors are currently the most common means of monitoring and controlling blood glucose levels (BGLs). Heart rate variability (HRV) is a noninvasive measure of the autonomic nervous system, and its dynamic physiological nature may provide an alternative means of blood glucose monitoring. However, the relationship between BGL and HRV parameters remains relatively unknown. MATERIALS AND METHODS: Thirty-two participants with diabetes (39.97 ± 17.21 years of age) and 31 without diabetes (27.87 ± 10.55 years of age) participated in the current study. Fasting preceded a 10-min three-lead electrocardiogram (ECG), which was followed by a finger prick blood glucose assessment. Following this, a regular meal was consumed, and 30 min after ingestion, a second postprandial 10-min ECG was obtained, and blood glucose assessment was conducted. RESULTS: Low-frequency (LF) power, high-frequency (HF) power, and total power (TP) of HRV were negatively associated with BGL in participants with DM. Additionally, the ratio of LF to HF was positively correlated with BGL. Duration of DM was also associated with multiple HRV parameters, with negative associations to both LF and HF parameters as well as TP. CONCLUSIONS: This study demonstrates links between specific HRV variables and BGL. In the future the dynamic nature of HRV could provide a unique and real-time method for monitoring BGL, for continuous noninvasive prediction and/or management of DM.