Individual differences in cognition, affect, and performance: behavioral, neuroimaging, and molecular genetic approaches

Neurophysiology of ACL Injury

The neurophysiology of ACL injury extends beyond the mechanical rupture of the ligament to encompass profound alterations in the central and peripheral nervous systems, impacting sensorimotor integration and neuromuscular control. The ACL, densely populated with mechanoreceptors, plays a critical role in joint proprioception, dynamically regulating knee stability through complex neural circuits that connect to the spinal cord and brain. When disrupted by injury, these neural pathways contribute to delayed muscular activation, altered motor planning, and compromised joint stability. Such neuromechanical deficits increase the likelihood of reinjury and highlight the need for comprehensive neuroplastic rehabilitation. Neuroplastic therapy, employing tools like external focus strategies, stroboscopic glasses, smartboards, and virtual reality, aims to restore and enhance neural connectivity, sensory integration, and motor coordination. These advanced tools target distinct phases of motor learning, promoting automaticity and resilience in movement patterns. By integrating visual-cognitive, proprioceptive, and reflexive controls, this therapeutic approach not only accelerates recovery but also optimizes performance and reduces the risk of re-injury, representing a paradigm shift in ACL rehabilitation.

The effect of interstimulus interval on sustained attention

The ability of nervous systems to filter out irrelevant and repetitive stimuli may prevent animals from becoming 'saturated' with excess information. However, animals must be particular about which stimuli to attend to and which to ignore, as mistakes may be costly. Using a comparative approach, we explored the effect of interstimulus interval (ISI) between repeated presentations of visual stimuli presented on a screen to test the decrease in responses (response decrement) of both Trite planiceps jumping spiders and untrained Columba livia pigeons, animals with comparable visual ability despite having structurally different visual systems and brain size. We used ISIs of 2.5 s, 5 s, 10 s, predicting that decreases in ISI would lead to progressively less responses to the stimuli. Following from previous work on T. planiceps, we also manipulated pigeon hunger level, finding that hungry birds were initially more responsive than sated pigeons, but the rate of decrease in responses to the stimulus did not differ between the two groups. While a clear response decrement was seen in both species across all conditions, shorter ISIs resulted in more dramatic response decrements, aligning with previous work and with the resource depletion theory posited in the human-based literature.

Individual differences in visual search: A systematic review of the link between visual search performance and traits or abilities

Visual search (VS) comprises a class of tasks that we typically perform several times during a day and requires intentionally scanning (with or without moving the eyes) the environment for a specific target (be it an object or a feature) among distractor stimuli. Experimental research in lab-based or real-world settings has offered insight into its underlying neurocognitive mechanisms from a nomothetic point of view. A lesser-known but rapidly growing body of quasi-experimental and correlational research has explored the link between individual differences and VS performance. This combines different research traditions and covers a wide range of individual differences in studies deploying a vast array of VS tasks. As such, it is a challenge to determine whether any associations highlighted in single studies are robust when considering the wider literature. However, clarifying such relationships systematically and comprehensively would help build more accurate models of VS, and it would highlight promising directions for future research. This systematic review provides an up to date and comprehensive synthesis of the existing literature investigating associations between common indices of performance in VS tasks and measures of individual differences mapped onto four categories of cognitive abilities (short-term working memory, fluid reasoning, visual processing and processing speed) and seven categories of traits (Big Five traits, trait anxiety and autistic traits). Consistent associations for both traits (in particular, conscientiousness, autistic traits and trait anxiety - the latter limited to emotional stimuli) and cognitive abilities (particularly visual processing) were identified. Overall, however, informativeness of future studies would benefit from checking and reporting the reliability of all measurement tools, applying multiplicity correction, using complementary techniques, study preregistration and testing why, rather than only if, a robust relation between certain individual differences and VS performance exists.

Mental workload assessment by monitoring brain, heart, and eye with six biomedical modalities during six cognitive tasks

Introduction

The efficiency and safety of complex high precision human-machine systems such as in aerospace and robotic surgery are closely related to the cognitive readiness, ability to manage workload, and situational awareness of their operators. Accurate assessment of mental workload could help in preventing operator error and allow for pertinent intervention by predicting performance declines that can arise from either work overload or under stimulation. Neuroergonomic approaches based on measures of human body and brain activity collectively can provide sensitive and reliable assessment of human mental workload in complex training and work environments.

Methods

In this study, we developed a new six-cognitive-domain task protocol, coupling it with six biomedical monitoring modalities to concurrently capture performance and cognitive workload correlates across a longitudinal multi-day investigation. Utilizing two distinct modalities for each aspect of cardiac activity (ECG and PPG), ocular activity (EOG and eye-tracking), and brain activity (EEG and fNIRS), 23 participants engaged in four sessions over 4 weeks, performing tasks associated with working memory, vigilance, risk assessment, shifting attention, situation awareness, and inhibitory control.

Results

The results revealed varying levels of sensitivity to workload within each modality. While certain measures exhibited consistency across tasks, neuroimaging modalities, in particular, unveiled meaningful differences between task conditions and cognitive domains.

Discussion

This is the first comprehensive comparison of these six brain-body measures across multiple days and cognitive domains. The findings underscore the potential of wearable brain and body sensing methods for evaluating mental workload. Such comprehensive neuroergonomic assessment can inform development of next generation neuroadaptive interfaces and training approaches for more efficient human-machine interaction and operator skill acquisition.

Self-Regulation Phenomenon Emerged During Prolonged Fatigue Driving: An EEG Connectivity Study

Driving fatigue is a common experience for most drivers and can reduce human cognition and judgment abilities. Previous studies have exhibited a phenomenon of the non-monotonically varying indicators (both behavioral and neurophysiological) for driving fatigue evaluation but paid little attention to this phenomenon. Herein, we propose a hypothesis that the non-monotonically varying phenomenon is caused by the self-regulation of brain activity, which is defined as the fatigue self-regulation (FSR) phenomenon. In this study, a 90-min simulated driving task was performed on 26 healthy university students. EEG data and reaction time (RT) were synchronously recorded during the whole task. To identify the FSR phenomenon, a data-driven criterion was proposed based on clustering analysis of individual behavioral data and the FSR group was determined as having non-monotonic increase trend of RT and the drops of RT during prolonged driving were more than two levels among the total five levels. The subjects were then divided into two groups: the FSR group and the non-FSR group. Quantitative comparative analysis showed significant differences in behavioral performance, functional connectivity, network characteristics, and classification performance between the FSR and non-FSR groups. Specifically, the behavioral performance exhibited apparent non-monotonic development trend: increasing-decreasing-increasing. Moreover, network characteristics presented similar self-regulated development trends. Our study provides a new insight for revealing the complex neural mechanisms of driving fatigue, which may promote the development of practical techniques for automatic detection method and mitigation strategy.

Individualized Prediction of Task Performance Decline Using Pre-Task Resting-State Functional Connectivity

As a common complaint in contemporary society, mental fatigue is a key element in the deterioration of the daily activities known as time-on-task (TOT) effect, making the prediction of fatigue-related performance decline exceedingly important. However, conventional group-level brain-behavioral correlation analysis has the limitation of generalizability to unseen individuals and fatigue prediction at individual-level is challenging due to the significant differences between individuals both in task performance efficiency and brain activities. Here, we introduced a cross-validated data-driven analysis framework to explore, for the first time, the feasibility of utilizing pre-task idiosyncratic resting-state functional connectivity (FC) on the prediction of fatigue-related task performance degradation at individual level. Specifically, two behavioral metrics, namely ∆RT (between the most vigilant and fatigued states) and TOTslope over the course of the 15-min sustained attention task, were estimated among three sessions from 37 healthy subjects to represent fatigue-related individual behavioral impairment. Then, a connectome-based prediction model was employed on pre-task resting-state FC features, identifying the network-related differences that contributed to the prediction of performance deterioration. As expected, prominent populational TOT-related performance declines were revealed across three sessions accompanied with substantial inter-individual differences. More importantly, we achieved significantly high accuracies for individualized prediction of both TOT-related behavioral impairment metrics using pre-task neuroimaging features. Despite the distinct patterns between both behavioral metrics, the identified top FC features contributing to the individualized predictions were mainly resided within/between frontal, temporal and parietal areas. Overall, our results of individualized prediction framework extended conventional correlation/classification analysis and may represent a promising avenue for the development of applicable techniques that allow precaution of the TOT-related performance declines in real-world scenarios.

Temporal-spatial convolutional residual network for decoding attempted movement related EEG signals of subjects with spinal cord injury

Brain Computer Interface (BCI) offers a promising approach to restoring hand functionality for people with cervical spinal cord injury (SCI). A reliable classification of brain activities based on appropriate flexibility in feature extraction could enhance BCI systems performance. In the present study, based on convolutional layers with temporal-spatial, Separable and Depthwise structures, we develop Temporal-Spatial Convolutional Residual Network)TSCR-Net(and Temporal-Spatial Convolutional Iterative Residual Network)TSCIR-Net(structures to classify electroencephalogram (EEG) signals. Using EEG signals in five different hand movement classes of SCI people, we compare the effectiveness of TSCIR-Net and TSCR-Net models with some competitive methods. We use the bayesian hyperparameter optimization algorithm to tune the hyperparameters of compact convolutional neural networks. In order to show the high generalizability of the proposed models, we compare the results of the models in different frequency ranges. Our proposed models decoded distinctive characteristics of different movement efforts and obtained higher classification accuracy than previous deep neural networks. Our findings indicate that TSCIR-Net and TSCR-Net models fulfills a better classification accuracy of 71.11%, and 64.55% for EEG_All and 57.74%, and 67.87% for EEG_Low frequency data sets than the compared methods in the literature.

Reduced slow-wave activity and autonomic dysfunction during sleep precede cognitive deficits in Alzheimer's disease transgenic mice

Occurrence of amyloid-β (Aβ) aggregation in brain begins before the clinical onset of Alzheimer's disease (AD), as preclinical AD. Studies have reported that sleep problems and autonomic dysfunction associate closely with AD. However, whether they, especially the interaction between sleep and autonomic function, play critical roles in preclinical AD are unclear. Therefore, we investigated how sleep patterns and autonomic regulation at different sleep-wake stages changed and whether they were related to cognitive performance in pathogenesis of AD mice. Polysomnographic recordings in freely-moving APP/PS1 and wild-type (WT) littermates were collected to study sleep patterns and autonomic function at 4 (early disease stage) and 8 months of age (advanced disease stage), cognitive tasks including novel object recognition and Morris water maze were performed, and Aβ levels in brain were measured. APP/PS1 mice at early stage of AD pathology with Aβ aggregation but without significant differences in cognitive performance had frequent sleep-wake transitions, lower sleep-related delta power percentage, lower overall autonomic activity, and lower parasympathetic activity mainly during sleep compared with WT mice. The same phenomenon was observed in advanced-stage APP/PS1 mice with significant cognitive deficits. In mice at both disease stages, sleep-related delta power percentage correlated positively with memory performance. At early stage, memory performance correlated positively with sympathetic activity during wakefulness; at advanced stage, memory performance correlated positively with parasympathetic activity during both wakefulness and sleep. In conclusion, sleep quality and distinction between wake- and sleep-related autonomic function may be biomarkers for early AD detection.

Changes in Perceived Mental Load and Motor Performance during Practice-to-Learn and Practice-to-Maintain in Basketball

BACKGROUND

Attentional resource allocation during sports practice is associated with the players' perceived mental load. However, few ecological studies address this problem by considering the players' characteristics (e.g., practice experience, skill and cognition). Therefore, this study aimed to analyse the dose-response effect of two different types of practice, each with different learning objectives, on mental load and motor performance by using a linear mixed model analysis.

METHOD

Forty-four university students (age 20.36 ± 3.13 years) participated in this study. Two sessions were conducted, one based on a standard rules 1 × 1 basketball situation ("practice to maintain") and one with motor, temporal and spatial restrictions in 1 × 1 tasks ("practice to learn").

RESULTS

"Practice to learn" produced a higher perceived mental load (NASA-TLX scale) and a worse performance than "practice to maintain", but was moderated by experience and inhibition (p = 0.001). The same happens in the most demanding restriction (i.e., temporal, p < 0.0001).

CONCLUSION

The results showed that increasing the difficulty of 1 × 1 situations through restrictions harmed the player's performance and increased their perceived mental load. These effects were moderated by previous basketball experience and the player's inhibition capacity, so the difficulty adjustment should be based on the athletes themselves.

Repeated testing does not confound cognitive performance in the Western Australian magpie (Cracticus tibicen dorsalis)

A robust understanding of cognitive variation at the individual level is essential to understand selection for and against cognitive traits. Studies of animal cognition often assume that within-individual performance is highly consistent. When repeated tests of individuals have been conducted, the effects of test order (the overall sequence in which different tests are conducted) and test number (the ordinal number indicating when a specific test falls within a sequence)-in particular the potential for individual performance to improve with repeated testing-have received limited attention. In our study, we investigated test order and test number effects on individual performance in three inhibitory control tests in Western Australian magpies (Cracticus tibicen dorsalis). We presented adult magpies with three novel inhibitory control tasks (detour-reaching apparatuses) in random order to test whether experience of cognitive testing and the order in which the apparatuses were presented were predictors of cognitive performance. We found that neither test number nor test order had an effect on cognitive performance of individual magpies when presenting different variants of inhibitory control tasks. This suggests that repeated testing of the same cognitive trait, using causally identical but visually distinct cognitive tasks, does not confound cognitive performance. We recommend that repeated testing effects of cognitive performance in other species be studied to broadly determine the validity of repeated testing in animal cognition studies.

Interindividual Variability in Mental Fatigue-Related Impairments in Endurance Performance: A Systematic Review and Multiple Meta-regression

BACKGROUND

The negative effect of mental fatigue (MF) on physical performance has recently been questioned. One reason behind this could lie in the interindividual differences in MF-susceptibility and the individual features influencing them. However, the range of individual differences in mental fatigue-susceptibility is not known, and there is no clear consensus on which individual features could be responsible for these differences.

OBJECTIVE

To give an overview of interindividual differences in the effects of MF on whole-body endurance performance, and individual features influencing this effect.

METHODS

The review was registered on the PROSPERO database (CRD42022293242). PubMed, Web of Science, SPORTDiscus and PsycINFO were searched until the 16th of June 2022 for studies detailing the effect of MF on dynamic maximal whole-body endurance performance. Studies needed to include healthy participants, describe at least one individual feature in participant characteristics, and apply at least one manipulation check. The Cochrane crossover risk of bias tool was used to assess risk of bias. The meta-analysis and regression were conducted in R.

RESULTS

Twenty-eight studies were included, with 23 added to the meta-analysis. Overall risk of bias of the included studies was high, with only three presenting an unclear or low rating. The meta-analysis shows the effect of MF on endurance performance was on average slightly negative (g = - 0.32, [95% CI - 0.46; - 0.18], p < 0.001). The multiple meta-regression showed no significant influences of the included features (i.e. age, sex, body mass index and physical fitness level) on MF-susceptibility.

CONCLUSIONS

The present review confirmed the negative impact of MF on endurance performance. However, no individual features influencing MF-susceptibility were identified. This can partially be explained by the multiple methodological limitations such as underreporting of participant characteristics, lack of standardization across studies, and the restricted inclusion of potentially relevant variables. Future research should include a rigorous description of multiple different individual features (e.g., performance level, diet, etc.) to further elucidate MF mechanisms.

Occupational Burnout Is Linked with Inefficient Executive Functioning, Elevated Average Heart Rate, and Decreased Physical Activity in Daily Life - Initial Evidence from Teaching Professionals

Burnout is becoming a global pandemic jeopardizing brain health, with a huge impact on quality of life, available workforce, and the economy. Knowledge of the impact of burnout on cognition, physiology, and physical activity (PA) in daily life allows for an improved understanding of the health consequences and everyday ramifications of burnout. Twenty-eight volunteers participated in a three-day recording of daily physiology and PA, including heart rate (HR) and daily steps, with a wearable device. They filled in questionnaires screening for burnout (BBI-15), depression (BDI), and executive functions (EFs) in daily life (BRIEF-A). The subjects with burnout had more challenges in EFs, higher average HRs and lower numbers of steps in daily life than those without it. The BBI-15 scores correlated positively with the BDI scores and BRIEF-A indices and negatively with the awake HR variability (HRV) and daily steps. The metacognition index correlated negatively with the HRV. In conclusion, burnout is linked with compromised EFs along with alterations in cardiac physiology and PA in daily life. Such alterations may be easily detected with wearable devices, opening possibilities for novel biomarkers of burnout and other neuropsychiatric disorders. We suggest that physical activity and heart and brain health are intimately intertwined and that burnout interacts with each of them bidirectionally.

Psychiatrists' Cognitive and Affective Biases and the Practice of Psychopharmacology: Why Do Psychiatrists Differ From One Another in How They View and Prescribe Certain Medication Classes?

Cognitive and affective biases impact clinical decision-making in general medicine. This article explores how such biases might specifically affect psychiatrists' attitudes and prescribing patterns regarding two medication classes (stimulants and benzodiazepines) and addresses related issues. To supplement personal observations, selective PubMed narrative literature searches were conducted using relevant title/abstract terms, followed by snowballing for additional pertinent titles. Acknowledging that there are many more types of biases, we describe and use clinical vignettes to illustrate 17 cognitive and affective biases that might influence clinicians' psychopharmacological practices. Factors possibly underlying these biases include temperamental differences and both preprofessional and professional socialization. Mitigating strategies can reduce the potentially detrimental impacts that biases may impose on clinical care. How extensively these biases appear, how they differ among psychiatrists and across classes of medication, and how they might be most effectively addressed to minimize harms deserve further systematic study.

Modulating break types induces divergent low band EEG processes during post-break improvement: A power spectral analysis

Conventional wisdom suggests mid-task rest as a potential approach to relieve the time-on-task (TOT) effect while accumulating evidence indicated that acute exercise might also effectively restore mental fatigue. However, few studies have explored the neural mechanism underlying these different break types, and the results were scattered. This study provided one of the first looks at how different types of fatigue-recovery break exerted influence on the cognitive processes by evaluating the corresponding behavioral improvement and neural response (EEG power spectral) in a sustained attention task. Specifically, 19 participants performed three sessions of psychomotor vigilance tasks (PVT), with one session including a continuous 30-min PVT while the other two sessions additionally inserted a 15-min mid-task cycling and rest break, respectively. For behavioral performance, both types of break could restore objective vigilance transiently, while subjective feeling was only maintained after mid-task rest. Moreover, divergent patterns of EEG change were observed during post-break improvement. In detail, relative theta decreased and delta increased immediately after mid-task exercise, while decreased delta was found near the end of the rest-inserted task. Meanwhile, theta and delta could serve as neurological indicators to predict the reaction time change for exercise and rest intervention, respectively. In sum, our findings provided novel evidence to demonstrate divergent neural patterns following the mid-task exercise and rest intervention to counter TOT effects, which might lead to new insights into the nascent field of neuroergonomics for mental fatigue restoration.

Multifactorial approaches to study bilingualism in the aging population: Past, present, future

A better understanding and more reliable classification of bilinguals has been progressively achieved through the fine-tuning methodology and simultaneously optimizing the measurement tools. However, the current understanding is far from generalization to a larger population varying in different measures of bilingualism-L2 Age of acquisition (L2 AOA), L2 usage and exposure, and L2 proficiency. More recent studies have highlighted the importance of modeling bilingualism as a continuous variable. An in-depth look at the role of bilingualism, comparing groups, may be considered a reductionist approach, i.e., grouping based on one measure of bilingualism (e.g., L2 AOA) may not account for variability in other measures of bilingualism (L2 exposure, L2 use or L2 proficiency, amongst others) within and between groups. Similarly, a multifactorial dimension is associated with cognitive performance, where not all domains of cognition and subcomponents are equally influenced by bilingualism. In addition, socio-cultural and demographical factors may add another dimension to the impact of bilingualism on cognitive performance, especially in older adults. Nevertheless, not many studies have controlled or used the multiple socio-cultural and demographical factors as a covariate to understand the role of different aspects of bilingualism that may influence cognitive performance differently. Such an approach would fail to generalize the research findings to a larger group of bilinguals. In the present review paper, we illustrate that considering a multifactorial approach to different dimensions of bilingual study may lead to a better understanding of the role of bilingualism on cognitive performance. With the evolution of various fine-tuned methodological approaches, there is a greater need to study variability in bilingual profiles that can help generalize the result universally.

The effect of mental schema evolution on mental workload measurement: an EEG study with simulated quadrotor UAV operation

OBJECTIVE

Mental workload is the result of the interactions between the demands of an operation task and the skills, behavior and perception of the performer. Working under a high mental workload can significantly affect an operator's ability to choose optimal decisions. However, the effect of mental schema, which reflects the level of expertise of an operator, on mental workload remains unclear. Here, we propose a theoretical framework for describing how the evolution of mental schema affects mental workload from the perspective of cognitive processing.

APPROACH

we recruited 51 students to participate in a 10-day simulated UAV flight training. The EEG PSD metrics were used to investigate the changes in neural responses caused by variations in the mental workload at different stages of mental schema evolution.

MAIN RESULTS

It was found that mental schema evolution influenced the direction and change trends of the frontal theta PSD, parietal alpha PSD, and central beta PSD. Initially, before the mental schema was formed, only the frontal theta PSD increased with increasing task difficulty; when the mental schema was initially being developed, the frontal theta PSD and the parietal alpha PSD decreased with increasing task difficulty, while the central beta PSD increased with increasing task difficulty. Finally, as the mental schema gradually matured, the trend of the three indicators did not change with increasing task difficulty. However, differences in the frontal PSD became more pronounced across task difficulty levels, while differences in the parietal PSD narrowed.

SIGNIFICANCE

Our results describe the relationship between the EEG power spectrum and the mental workload of UAV operators as the mental schema evolved. This suggests that EEG indicators can not only provide more accurate measurements of mental workload but also provide insights into the development of an operator's skill level.

Individual differences in skill acquisition and transfer assessed by dual task training performance and brain activity

Assessment of expertise development during training program primarily consists of evaluating interactions between task characteristics, performance, and mental load. Such a traditional assessment framework may lack consideration of individual characteristics when evaluating training on complex tasks, such as driving and piloting, where operators are typically required to execute multiple tasks simultaneously. Studies have already identified individual characteristics arising from intrinsic, context, strategy, personality, and preference as common predictors of performance and mental load. Therefore, this study aims to investigate the effect of individual difference in skill acquisition and transfer using an ecologically valid dual task, behavioral, and brain activity measures. Specifically, we implemented a search and surveillance task (scanning and identifying targets) using a high-fidelity training simulator for the unmanned aircraft sensor operator, acquired behavioral measures (scan, not scan, over scan, and adaptive target find scores) using simulator-based analysis module, and measured brain activity changes (oxyhemoglobin and deoxyhemoglobin) from the prefrontal cortex (PFC) using a portable functional near-infrared spectroscopy (fNIRS) sensor array. The experimental protocol recruited 13 novice participants and had them undergo three easy and two hard sessions to investigate skill acquisition and transfer, respectively. Our results from skill acquisition sessions indicated that performance on both tasks did not change when individual differences were not accounted for. However inclusion of individual differences indicated that some individuals improved only their scan performance (Attention-focused group), while others improved only their target find performance (Accuracy-focused group). Brain activity changes during skill acquisition sessions showed that mental load decreased in the right anterior medial PFC (RAMPFC) in both groups regardless of individual differences. However, mental load increased in the left anterior medial PFC (LAMPFC) of Attention-focused group and decreased in the Accuracy-focused group only when individual differences were included. Transfer results showed no changes in performance regardless of grouping based on individual differences; however, mental load increased in RAMPFC of Attention-focused group and left dorsolateral PFC (LDLPFC) of Accuracy-focused group. Efficiency and involvement results suggest that the Attention-focused group prioritized the scan task, while the Accuracy-focused group prioritized the target find task. In conclusion, training on multitasks results in individual differences. These differences may potentially be due to individual preference. Future studies should incorporate individual differences while assessing skill acquisition and transfer during multitask training.

Inter-Individual Differences in Cognitive Tasks: Focusing on the Shaping of Decision-Making Strategies

In this paper, we review recent (published and novel) data showing inter-individual variation in decision-making strategies established by mice in a gambling task (MGT for Mouse Gambling Task). It may look intriguing, at first, that congenic animals develop divergent behaviors. However, using large groups of mice, we show that individualities emerge in the MGT, with about 30% of healthy mice displaying risk-averse choices while about 20-25% of mice make risk-prone choices. These strategies are accompanied by different brain network mobilization and individual levels of regional -prefrontal and striatal- monoamines. We further illustrate three ecological ways that influence drastically cognitive strategies in healthy adult mice: sleep deprivation, sucrose or artificial sweetener exposure, and regular exposure to stimulating environments. Questioning how to unmask individual strategies, what are their neural/neurochemical bases and whether we can shape or reshape them with different environmental manipulations is of great value, first to understand how the brain may build flexible decisions, and second to study behavioral plasticity, in healthy adult, as well as in developing brains. The latter may open new avenues for the identification of vulnerability traits to adverse events, before the emergence of mental pathologies.

Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work?

Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.

Dynamic Reorganization of Functional Connectivity During Post-break Task Reengagement

Because of the undesired fatigue-related consequences, accumulating efforts have been made to find an effective intervention to alleviate the suboptimal cognitive function caused by mental fatigue. Nonetheless, limitations of intervention and evaluation methods may hinder the revealing of underlying neural mechanisms of fatigue recovery. Through the newly-developed dynamic functional connectivity (FC) analysis framework, this study aims to investigate the effects of two types of mid-task interventions (i.e., rest-break and moderate-intensity exercise-break) on the dynamic reorganization of FC during the execution of psychomotor vigilance test (PVT). Using a sliding window approach, temporal brain networks within each frequency band (i.e., δ, θ, α, & β) were estimated before and immediate after the intervention, and towards the end of the task to investigate the immediate and delayed effects respectively during post-break task reengagement. Behaviourally, similar beneficial effects of exercise- and rest-break on performance were observed, manifested by the immediate improvements after both interventions and a long-lasting influence towards the end of tasks. Moreover, temporal brain networks assessment showed significant immediate decreases of fluctuability, which followed by an increase of fluctuability towards the end of intervention tasks. Furthermore, the temporal nodal measure revealed the channels with significant differences across tasks were mainly resided in the fronto-parietal areas that exhibited interesting frequency-dependent distribution. The observations of immediate and delayed dynamic FC reorganizations extend previous fatigue-related intervention and static FC studies, and provide new insight into the dynamic characteristics of FC during post-break task reengagement.

The impact of anxiety on the cognitive function of informal Parkinson's disease caregiver: Evidence from task-based and resting-state fNIRS

Informal Parkinson's disease (PD) caregivers are considered to experience high levels of caregiver burden, negatively affecting the health of caregivers. However, few studies explored the relationship between anxiety in caregiver burden and cognitive function in informal PD caregivers. Although, no study has even investigated the neural mechanisms underlying this connection. This study aimed to conduct comprehensive cognitive and clinical assessments and evaluate brain activity from task-based state and resting-state using functional near-infrared spectroscopy (fNIRS). A total of ten informal PD caregivers and 15 matched, healthy, non-caregivers were recruited. Comprehensive cognitive and clinical assessments were conducted to evaluate five cognitive domains and mental states. Neural activity induced by verbal fluency task (VFT) and brain connectivity during resting state were monitored, and their correlations with the neuropsychological and clinical tests were explored. Our results showed that compared to non-caregiver, an informal PD caregiver exhibited no difference in most cognitive domains of function but performed better in attentional function, along with higher levels of anxiety. Decreased activation over prefrontal regions during VFT and hypo-connectivity within the frontoparietal network (FPN) and between default mode network (DMN) and FPN in the resting state were confirmed in this study as a result of the negative effects of anxiety on the brain. Furthermore, Spearman's correlation found that neural activity in FPN during task-based state and resting state was negatively correlated with the severity of anxiety. These findings indicate that despite normal or even better cognitive function, informal PD caregivers have impaired brain function, and this deficit in neural activity was related to anxiety.

Perceptual-Motor Efficiency and Concussion History Are Prospectively Associated With Injury Occurrences Among High School and Collegiate American Football Players

Background:

After a sport-related concussion (SRC), the risk for lower extremity injury is approximately 2 times greater, and the risk for another SRC may be as much as 3 to 5 times greater.

Purpose:

To assess the predictive validity of screening methods for identification of individual athletes who possess an elevated risk of SRC.

Study Design:

Case-control study; Level of evidence, 3.

Methods:

Metrics derived from a smartphone flanker test software application and self-ratings of both musculoskeletal function and overall wellness were acquired from American high school and college football players before study participation. Occurrences of core or lower extremity injury (CLEI) and SRC were documented for all practice sessions and games for 1 season. Receiver operating characteristic and logistic regression analyses were used to identify variables that provided the greatest predictive accuracy for CLEI or SRC occurrence.

Results:

Overall, there were 87 high school and 74 American college football players included in this study. At least 1 CLEI was sustained by 45% (39/87) of high school players and 55% (41/74) of college players. Predictors of CLEI included the flanker test conflict effect ≥69 milliseconds (odds ratio [OR], 2.12; 90% CI, 1.24-3.62) and a self-reported lifetime history of SRC (OR, 1.70; 90% CI, 0.90-3.23). Of players with neither risk factor, only 38% (29/77) sustained CLEI compared with 61% (51/84) of players with 1 or both of the risk factors (OR, 2.56; 90% CI, 1.50-4.36). SRC was sustained by 7 high school players and 3 college players. Predictors of SRC included the Overall Wellness Index score ≤78 (OR, 9.83; 90% CI, 3.17-30.50), number of postconcussion symptoms ≥4 (OR, 8.35; 90% CI, 2.71-25.72), the Sport Fitness Index score ≤78 (OR, 5.16; 90% CI, 1.70-15.65), history of SRC (OR, 4.03; 90% CI, 1.35-12.03), and the flanker test inverse efficiency ratio ≥1.7 (OR, 3.19; 90% CI, 1.08-9.47).

Conclusion:

Survey responses and smartphone flanker test metrics predicted greater injury incidence among individual football players classified as high-risk compared with that for players with a low-risk profile.

Neuroergonomy: An Engagement between Work Psychology and Neurosciences

This article aims to identify the importance of markers at the neurophysiological, physiological, and behavioral levels of mental states linked to the performance of workers as an influence in the field of neuroscience and psychology at work. The author discusses the emergence of neuroergonomy as an area of neuroscience principles and cognitive psychology related to human factors for fundamentals of analysis in brain dynamics, mainly through the use of noninvasive tools, such as the use of electroencephalography for physiological aspects of mental exhaustion of the worker. Neuroergonomics is seen as fundamental for the investigation of complex relationships of organizations, especially when interacting with technology. Future research should involve more complex team-building scenarios and enrich the different neuroergonomic solutions.

Memory-Related Frontal Brainwaves Predict Transition to Mild Cognitive Impairment in Healthy Older Individuals Five Years Before Diagnosis

Background:

Early prognosis of high-risk older adults for amnestic mild cognitive impairment (aMCI), using noninvasive and sensitive neuromarkers, is key for early prevention of Alzheimer’s disease. We have developed individualized measures in electrophysiological brain signals during working memory that distinguish patients with aMCI from age-matched cognitively intact older individuals.

Objective:

Here we test longitudinally the prognosis of the baseline neuromarkers for aMCI risk. We hypothesized that the older individuals diagnosed with incident aMCI already have aMCI-like brain signatures years before diagnosis.

Methods:

Electroencephalogram (EEG) and memory performance were recorded during a working memory task at baseline. The individualized baseline neuromarkers, annual cognitive status, and longitudinal changes in memory recall scores up to 10 years were analyzed.

Results:

Seven of the 19 cognitively normal older adults were diagnosed with incident aMCI for a median 5.2 years later. The seven converters’ frontal brainwaves were statistically identical to those patients with diagnosed aMCI (n = 14) at baseline. Importantly, the converters’ baseline memory-related brainwaves (reduced mean frontal responses to memory targets) were significantly different from those who remained normal. Furthermore, differentiation pattern of left frontal memory-related responses (targets versus nontargets) was associated with an increased risk hazard of aMCI (HR = 1.47, 95% CI 1.03, 2.08).

Conclusion:

The memory-related neuromarkers detect MCI-like brain signatures about five years before diagnosis. The individualized frontal neuromarkers index increased MCI risk at baseline. These noninvasive neuromarkers during our Bluegrass memory task have great potential to be used repeatedly for individualized prognosis of MCI risk and progression before clinical diagnosis.

Toward practical driving fatigue detection using three frontal EEG channels: a proof-of-concept study

Objective. Although various driving fatigue detection strategies have been introduced, the limited practicability is still an obstacle for the real application of these technologies. This study is based on the newly proposed non-hair-bearing (NHB) method to achieve practical driving fatigue detection with fewer channels from NHB areas and more efficient electroencephalogram (EEG) features.Approach. EEG data were recorded from 20 healthy subjects (15 males, age = 22.2 ± 3.2 years) in a 90 min simulated driving task using a remote wireless cap. Behaviorally, subjects demonstrated a salient fatigue effect, as reflected by a monotonic increase in reaction time. Using a sliding-window approach, we determined the vigilant and fatigued states at individual level to reduce the inter-subject differences in behavioral impairment and brain activity. Multiple EEG features, including power-spectrum density (PSD), functional connectivity (FC), and entropy, were estimated in a pairwise manner, which were set as input for fatigue classification.Main results. Intriguingly, this data-driven approach showed that the best classification performance was achieved using three EEG channel pairs located in the NHB area. The mixed features of the frontal NHB area lead to the high within-subject detection rate of driving fatigue (92.7% ± 0.92%) with satisfactory generalizability for fatigue classification across different subjects (77.13% ± 0.85%). Moreover, we found the most prominent contributing features were PSD of different frequency bands within the frontal NHB area and FC within the frontal NHB area and between frontal and parietal areas.Significance. In summary, the current work provided objective evidence to support the effectiveness of the NHB method and further improved the performance, thereby moving a step forward towards practical driving fatigue detection in real-world scenarios.

"Hit the Robot on the Head With This Mallet" - Making a Case for Including More Open Questions in HRI Research

Researchers continue to devise creative ways to explore the extent to which people perceive robots as social agents, as opposed to objects. One such approach involves asking participants to inflict 'harm' on a robot. Researchers are interested in the length of time between the experimenter issuing the instruction and the participant complying, and propose that relatively long periods of hesitation might reflect empathy for the robot, and perhaps even attribution of human-like qualities, such as agency and sentience. In a recent experiment, we adapted the so-called 'hesitance to hit' paradigm, in which participants were instructed to hit a humanoid robot on the head with a mallet. After standing up to do so (signaling intent to hit the robot), participants were stopped, and then took part in a semi-structured interview to probe their thoughts and feelings during the period of hesitation. Thematic analysis of the responses indicate that hesitation not only reflects perceived socialness, but also other factors including (but not limited to) concerns about cost, mallet disbelief, processing of the task instruction, and the influence of authority. The open-ended, free responses participants provided also offer rich insights into individual differences with regards to anthropomorphism, perceived power imbalances, and feelings of connection toward the robot. In addition to aiding understanding of this measurement technique and related topics regarding socialness attribution to robots, we argue that greater use of open questions can lead to exciting new research questions and interdisciplinary collaborations in the domain of social robotics.

Canabal M, Gutiérrez-Martín L, Lanza-Gutierrez JM, Portela-García M, López-Ongil C. Fear Recognition for Women Using a Reduced Set of Physiological Signals

Emotion recognition is benefitting from the latest research into physiological monitoring and wireless communications, among other remarkable achievements. These technologies can indeed provide solutions to protect vulnerable people in scenarios such as personal assaults, the abuse of children or the elderly, gender violence or sexual aggression. Cyberphysical systems using smart sensors, artificial intelligence and wearable and inconspicuous devices can serve as bodyguards to detect these risky situations (through fear-related emotion detection) and automatically trigger a protection protocol. As expected, these systems should be trained and customized for each user to ensure the best possible performance, which undoubtedly requires a gender perspective. This paper presents a specialized fear recognition system for women based on a reduced set of physiological signals. The architecture proposed is characterized by the usage of three physiological sensors, lightweight binary classification and the conjunction of linear (temporal and frequency) and non-linear features. Moreover, a binary fear mapping strategy between dimensional and discrete emotional information based on emotional self-report data is implemented to avoid emotional bias. The architecture is evaluated using a public multi-modal physiological dataset with two approaches (subject-dependent and subject-independent models) focusing on the female participants. As a result, the proposal outperforms the state-of-the-art in fear recognition, achieving a recognition rate of up to 96.33% for the subject-dependent model.

Electro-Encephalography and Electro-Oculography in Aeronautics: A Review Over the Last Decade (2010-2020)

Electro-encephalography (EEG) and electro-oculography (EOG) are methods of electrophysiological monitoring that have potentially fruitful applications in neuroscience, clinical exploration, the aeronautical industry, and other sectors. These methods are often the most straightforward way of evaluating brain oscillations and eye movements, as they use standard laboratory or mobile techniques. This review describes the potential of EEG and EOG systems and the application of these methods in aeronautics. For example, EEG and EOG signals can be used to design brain-computer interfaces (BCI) and to interpret brain activity, such as monitoring the mental state of a pilot in determining their workload. The main objectives of this review are to, (i) offer an in-depth review of literature on the basics of EEG and EOG and their application in aeronautics; (ii) to explore the methodology and trends of research in combined EEG-EOG studies over the last decade; and (iii) to provide methodological guidelines for beginners and experts when applying these methods in environments outside the laboratory, with a particular focus on human factors and aeronautics. The study used databases from scientific, clinical, and neural engineering fields. The review first introduces the characteristics and the application of both EEG and EOG in aeronautics, undertaking a large review of relevant literature, from early to more recent studies. We then built a novel taxonomy model that includes 150 combined EEG-EOG papers published in peer-reviewed scientific journals and conferences from January 2010 to March 2020. Several data elements were reviewed for each study (e.g., pre-processing, extracted features and performance metrics), which were then examined to uncover trends in aeronautics and summarize interesting methods from this important body of literature. Finally, the review considers the advantages and limitations of these methods as well as future challenges.

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers

Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

Mid-Task Physical Exercise Keeps Your Mind Vigilant: Evidences From Behavioral Performance and EEG Functional Connectivity

Accumulating efforts have been made to discover effective solutions for fatigue recovery with the ultimate aim of reducing adverse consequences of mental fatigue in real life. The previously-reported behavioral benefits of physical exercise on mental fatigue recovery prompted us to investigate the restorative effect and reveal the underlying neural mechanisms. Specifically, we introduced an empirical method to investigate the beneficial effect of physical exercise on the reorganization of EEG functional connectivity (FC) in a two-session experiment where one session including a successive 30-min psychomotor vigilance task (PVT) (No-intervention session) compared to an insertion of a mid-task 15-min cycling exercise (Intervention session). EEG FC was obtained from 21 participants and quantitatively assessed via graph theoretical analysis and a classification framework. The findings demonstrated the effectiveness of exercise intervention on behavioral performance as shown in improved reaction time and response accuracy. Although we found significantly altered network alterations towards the end of experiment in both sessions, no significant differences between the two sessions and no interaction between session and time were found in EEG network topology. Further interrogation of functional connectivity through classification analysis showed decreased FC in distributed brain areas, which may lead to the significant reduction of network efficiency in both sessions. Moreover, we showed distinct patterns of FC alterations between the two sessions, indicating different information processing strategies adopted in the intervention session. In sum, these results provide some of the first quantitative insights into the complex neural mechanism of exercise intervention for fatigue recovery and lead a new direction for further application research in real-world situations.

Recognition of cognitive load with a stacking network ensemble of denoising autoencoders and abstracted neurophysiological features

The safety of human-machine systems can be indirectly evaluated based on operator's cognitive load levels at each temporal instant. However, relevant features of cognitive states are hidden behind in multiple sources of cortical neural responses. In this study, we developed a novel neural network ensemble, SE-SDAE, based on stacked denoising autoencoders (SDAEs) which identify different levels of cognitive load by electroencephalography (EEG) signals. To improve the generalization capability of the ensemble framework, a stacking-based approach is adopted to fuse the abstracted EEG features from activations of deep-structured hidden layers. In particular, we also combine multiple K-nearest neighbor and naive Bayesian classifiers with SDAEs to generate a heterogeneous classification committee to enhance ensemble's diversity. Finally, we validate the proposed SE-SDAE by comparing its performance with mainstream pattern classifiers for cognitive load evaluation to show its effectiveness.

Partial Least Squares Regression Performs Well in MRI-Based Individualized Estimations

Estimation of individuals' cognitive, behavioral and demographic (CBD) variables based on MRI has attracted much research interest in the past decade, and effective machine learning techniques are of great importance for these estimations. Partial least squares regression (PLSR) is an attractive machine learning technique that can accommodate both single- and multi-label learning in a simple framework, while its potential for MRI-based estimations of CBD variables remains to be explored. In this study, we systemically investigated the performance of PLSR in MRI-based estimations of individuals' CBD variables, especially its performance in simultaneous estimation of multiple CBD variables (multi-label learning). We performed the study on the dataset included in the HCP S1200 release. Resting state functional connections (RSFCs) were used as features, and a total of 10 CBD variables (e.g., age, gender, grip strength, and picture vocabulary) were estimated. The results showed that PLSR performed well in both single- and multi-label learning. In fact, the present estimations were better than those reported in literatures, as indicated by stronger correlations between the estimated and actual CBD variables, as well as high gender classification accuracy (97.8% in this study). Moreover, the RSFCs that contributed to the estimations exhibited strong correlations with the CBD variable estimated, that is, PLSR algorithm automatically selected the RSFCs closely related to one CBD variable to establish predictive models for the variable. Besides, the estimation accuracies based on RSFCs among 100, 200, and 300 regions of interest (ROIs) were higher than those based on RSFCs among 15, 25, and 50 ROIs; the estimation accuracies based on RSFCs evaluated using partial correlation were higher than those based on RSFCs evaluated using full correlation. In addition to the aforementioned virtues, PLSR is efficient in model training and testing, and it is simple and easy to use. Therefore, PLSR can be a favorable choice for future MRI-based estimations of CBD variables.

Effects of Rest-Break on Mental Fatigue Recovery Determined by a Novel Temporal Brain Network Analysis of Dynamic Functional Connectivity

Mental fatigue is growingly considered to be associated with functional brain dysconnectivity. Although conventional wisdom suggests that rest break is an effective countermeasure, the underlying neural mechanisms and how they modulate fatigue-related brain dysconnectivity is largely unknown. Here, we introduce an empirical method to examine the reorganization of dynamic functional connectivity (FC) in a two-session experiment where one session including a mid-task break (Rest) compared to a successive task design in the other session (No-rest). Temporal brain networks were estimated from 20 participants and the spatiotemporal architecture was examined using our newly developed temporal efficiency analysis framework. We showed that taking a mid-task break leads to a restorative effect towards the end of experiment instead of immediate post-rest behaviour benefits. More importantly, we revealed a potential neural basis of our behaviour observation: the reduced spatiotemporal global integrity of temporal brain network in No-rest session was significantly improved with the break opportunity in the last task block of Rest session. Overall, we provided novel evidence to support beneficial effect of rest breaks in both behaviour performance and brain function. Moreover, these findings extended prior static FC studies of mental fatigue and highlight that altered dynamic FC may underlie cognitive fatigue.

The psychological perspective on the adoption of approved genetically modified crops in the presence of acceptability constraint: the contingent role of passion

This study contributes to the literature on genetically modified crops by examining the impact of psychological distance on farmer adoption. The existing body of literature suggests that some farmers have the conservative perception to adopt genetically modified crops because of controversies regarding the side effects and profitability. To understand these social problems, we have relied upon construal-level theory which argues farmer passion about dynamics like here and now, likely or unlikely and the near or far from self are vital for the adoption of approved genetically modified crops. The data for the present study collected from three Asian countries through questionnaire, for China 300, India 350, Philippines 300 valid responses collected. The study founds a positive relation of psychological distance based on passion with farmer trust behavior to adopt genetically modified crops over time. The findings of the present study provide new insights into the development of farmer acceptability and encourage the use of novel crop technologies.Abbreviations: GM, genetically modified; TD, temporal distance; SPD, Spatial distance; SD, Social distance; TB, trust behavior; AGMC, Approved Genetically modified crops; CLT, Construal Level Theory.

Risk and protective effects of serotonin and BDNF genes on stress-related adult psychiatric symptoms

We focused on individual risk by examining childhood adversity and current psychiatric symptoms in a sample of 100 college students genotyped for both the serotonin transporter (5-HTTLPR) and the brain-derived neurotrophic factor (BDNF). Naturally occurring allelic variation in 5-HTTLPR (short/long) and BDNF (valine/methionine) have been strongly implicated in stress-related psychiatric risk, but the combined effects of these alleles on psychological functioning have yet to be fully elucidated. Univariate analysis revealed gene-environment correlations linking heightened psychiatric risk with past childhood adversity for short but not long 5-HTTLPR allelic carriers and for valine (Val) but not methionine (Met) BDNF allelic carriers. Multivariate analyses revealed a significant gene x gene interaction with results showing that risk varied systematically depending on both 5-HTTLPR and BDNF alleles, independent of childhood adversity. Hierarchical regression analyses indicated that approximately 11% of the variance in symptoms of depression could be specifically accounted for by the epistatic interaction of 5-HTTLPR and BDNF val66Met polymorphisms. Allelic group analyses indicated lowest risk, as measured by depression and anxiety, for allelic carriers of 5-HTTLPR-short and BDNF Met, followed by 5-HTTLPR-long and BDNF-Val, 5-HTTLPR-short and BDNF-Val, and 5-HTTLPR-long and BDNF-Met. Results suggest that protective or risk-enhancing effects on stress-related psychiatric functioning may depend on specific allelic combinations of 5-HTTLPR and BDNF.

Picture This: A Review of Research Relating to Narrative Processing by Moving Image Versus Language

Reading fiction for pleasure is robustly correlated with improved cognitive attainment and other benefits. It is also in decline among young people in developed nations, in part because of competition from moving image fiction. We review existing research on the differences between reading or hearing verbal fiction and watching moving image fiction, as well as looking more broadly at research on image or text interactions and visual versus verbal processing. We conclude that verbal narrative generates more diverse responses than moving image narrative. We note that reading and viewing narrative are different tasks, with different cognitive loads. Viewing moving image narrative mostly involves visual processing with some working memory engagement, whereas reading narrative involves verbal processing, visual imagery, and personal memory (Xu et al., 2005). Attempts to compare the two by creating equivalent stimuli and task demands face a number of challenges. We discuss the difficulties of such comparative approaches. We then investigate the possibility of identifying lower level processing mechanisms that might distinguish cognition of the two media and propose internal scene construction and working memory as foci for future research. Although many of the sources we draw on concentrate on English-speaking participants in European or North American settings, we also cover material relating to speakers of Dutch, German, Hebrew, and Japanese in their respective countries, and studies of a remote Turkish mountain community.

Heart Rate Variability and Cognitive Function: A Systematic Review

Background: Autonomic dysfunctions may precede the development of cognitive impairment, but the connection between these dimensions is unclear. This systematic review aims to analyze the relationship between heart rate variability (HRV) and cognitive functions.

Methods: The review process was conducted according to the PRISMA-Statement. Restrictions were made, selecting the studies in English and published in peer-review journals, including at least one cognitive measure and presenting the measurement of HRV. Studies that included participants with medical conditions, dementia, psychiatric disorders, strokes, and traumatic brain injury were excluded. Twenty studies were selected, with a total of 19,431 participants. The results were divided into different cognitive domains determined a priori: global cognitive functioning, attention, processing speed, executive functions, memory, language and visuospatial skills.

Results: Both increased sympathetic activity and decreased parasympathetic activity seem to be associated with a worse performance in the cognitive domains considered, in the absence of dementia and severe cardiovascular diseases or other medical and psychiatric diseases.

Conclusion: The results highlight the influence of the autonomic nervous system (ANS) in cognitive functioning. However, the marked interest facing toward a specific domain, i.e., the executive functions, and the relatively small number of the studies on this topic do not allow understanding better this relationship. Despite these limits, HRV could be considered a promising early biomarker of cognitive impairment in populations without dementia or stroke. This index should be evaluated within a preventative perspective to minimize the risk of developing cognitive impairment.

Predicting Cognitive Scores from Resting fMRI Data and Geometric Features of the Brain

Anatomical T1 weighted Magnetic Resonance Imaging (MRI) and functional magnetic resonance imaging collected during resting (rfMRI) are promising markers that offer insight into the structure and function of the human brain. The objective of this work is to explore the use of a deep learning neural network to predict cognitive performance scores for a population of normal controls and subjects with Attention Deficit Hyperactivity Disorder (ADHD). Specifically, we predict verbal and performance IQs and ADHD index from features derived from T1 and rfMRI imaging data. First, we processed the rfMRI and MRI data of subjects using the BrainSuite fMRI Processing (BFP) pipeline to perform anatomical and functional preprocessing. This produces for each subject fMRI and geometric (anatomical) features represented in a standardized grayordinate system. The geometric and functional cortical data corresponding to the two hemispheres were then transformed to 128×128 multichannel images and input to a convolutional component of the neural network. Subcortical data were presented in a standard vector form and inputted to a input layer of the network. The neural network was implemented in Python using the Keras library with a TensorFlow backend. Training was performed on 168 images with 90 images used for testing. We observed a high correlation between predicted and actual values of the indices tested: Performance IQ: 0.47; Verbal IQ: 0.41, ADHD: 0.57. Comparing these values to those from network trained on functional-only and structural-only data, we saw that rfMRI is more informative than MRI, but the two modalities are highly complementary in terms of predicting these indices.

Multiple Intelligences in Teaching and Education: Lessons Learned from Neuroscience

This brief paper summarizes a mixed method review of over 500 neuroscientific reports investigating the proposition that general intelligence (g or IQ) and multiple intelligences (MI) can be integrated based on common and unique neural systems. Extrapolated from this interpretation are five principles that inform teaching and curriculum so that education can be strengths-based and personalized to promote academic achievement. This framework is proposed as a comprehensive model for a system of educational cognitive neuroscience that will serve the fields of neuroscience as well as educators. Five key principles identified are culture matters, every brain is unique—activate strengths, know thyself, embodied cognition/emotional rudder, and make it mean something.

Decoding human mental states by whole-head EEG+fNIRS during category fluency task performance

OBJECTIVE

Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components.

APPROACH

We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data.

MAIN RESULTS

EEG+fNIRS's decoding accuracy was greater than that of its subsystems, partly due to the new type of neurovascular features made available by hybrid data.

SIGNIFICANCE

Availability of an accurate and practical decoding method has potential implications for medical diagnosis, brain-computer interface design, and neuroergonomics.

Interpreting and Utilising Intersubject Variability in Brain Function

We consider between-subject variance in brain function as data rather than noise. We describe variability as a natural output of a noisy plastic system (the brain) where each subject embodies a particular parameterisation of that system. In this context, variability becomes an opportunity to: (i) better characterise typical versus atypical brain functions; (ii) reveal the different cognitive strategies and processing networks that can sustain similar tasks; and (iii) predict recovery capacity after brain damage by taking into account both damaged and spared processing pathways. This has many ramifications for understanding individual learning preferences and explaining the wide differences in human abilities and disabilities. Understanding variability boosts the translational potential of neuroimaging findings, in particular in clinical and educational neuroscience.

Positive Effects of Mindfulness-Based Training on Energy Maintenance and the EEG Correlates of Sustained Attention in a Cohort of Nurses

Mindfulness based training (MBT) is becoming increasingly popular as a means to improve general wellbeing through developing enhanced control over metacognitive processes. In this preliminary study, we tested a cohort of 36 nurses (mean age = 30.3, SD = 8.52; 2 male) who participated in an 8-week MBT intervention to examine the improvements in sustained attention and its energetic costs that may result from MBT. Changes in sustained attention were measured using the psychomotor vigilance task (PVT) and electroencephalography (EEG) was collected both during PVT performance, and during a brief period of meditation. As there was substantial variability in training attendance, this variable was used a covariate in all analyses. Following the MBT program, we observed changes in alpha power across all scalp regions during meditation that were correlated with attendance. Similarly, PVT performance worsened over the 8-week period, but that this decline was mitigated by good attendance on the MBT program. The subjective energy depletion due to PVT performance (measured using self-report on Likert-type scales) was also less in regular attendees. Finally, changes in known EEG markers of attention during PVT performance (P300 and alpha-band event-related desynchronization) paralleled these behavioral shifts. Taken together, our data suggest that sustained attention and its associated costs may be negatively affected over time in the nursing profession, but that regular attendance of MBT may help to attenuate these effects. However, as this study contained no control condition, we cannot rule out that other factors (e.g., motivation, placebo effects) may also account for our findings.

Functional human GRIN2B promoter polymorphism and variation of mental processing speed in older adults

We investigated the role of a single nucleotide polymorphism rs3764030 (G>A) within the human GRIN2B promoter in mental processing speed in healthy, cognitively intact, older adults. In vitro DNA-binding and reporter gene assays of different allele combinations in transfected cells showed that the A allele was a gain-of-function variant associated with increasing GRIN2B mRNA levels. We tested the hypothesis that individuals with A allele will have better memory performance (i.e. faster reaction times) in older age. Twenty-eight older adults (ages 65-86) from a well-characterized longitudinal cohort were recruited and performed a modified delayed match-to-sample task. The rs3764030 polymorphism was genotyped and participants were grouped based on the presence of the A allele into GG and AA/AG. Carriers of the A allele maintained their speed of memory retrieval over age compared to GG carriers (p = 0.026 slope of the regression line between AA and AG versus GG groups). To validate the results, 12 older adults from the same cohort participated in a different version of the short-term memory task. Reaction times were significantly slower with age in older adults with G allele (p < 0.001). These findings support a role for rs3764030 in maintaining faster mental processing speed over aging.