Execution Of Cognitive Tasks Research Articles

Alpha Neurofeedback Training in Elite Soccer Players Trained in Groups.

Neurofeedback training is applied in the world of sports as a means to improve athletes' performance. Training sessions are usually organized on an individual basis, one at a time. Here we investigated if the training could also be organized in groups. Forty-one national-level football (soccer) players (26 females, 15 males) carried out training sessions simultaneously in groups of up to 13, using a wearable device with Bluetooth connection, during their regular training hours at the club. It was possible to obtain good EEG measurements using this setup, albeit with a somewhat higher data loss than usual in standard laboratory sessions. The brain's alpha activity was trained using music-based neurofeedback in a crossover design. A training session consisted of alternating periods of neurofeedback and execution of cognitive tasks. EEG alpha (8-12Hz) activity was higher in the neurofeedback periods compared to the cognitive task periods, and the reverse was true for beta (13-30Hz) activity. The training program resulted in an increase of 34% in alpha activity associated with the training, and improved the athletes' performance on task switching and mental rotation tasks. In addition, self-reported sleep duration, as well as scores on the Being in Shape questionnaire (Feeling of Control and Flow) also improved. This study shows that neurofeedback training is feasible in groups of athletes, which can stimulate its application in team sports.

Aberrant associations between neuronal resting-state fluctuations and working memory-induced activity in major depressive disorder.

Previous investigations have revealed performance deficits and altered neural processes during working-memory (WM) tasks in major depressive disorder (MDD). While most of these studies used task-based functional magnetic resonance imaging (fMRI), there is an increasing interest in resting-state fMRI to characterize aberrant network dynamics involved in this and other MDD-associated symptoms. It has been proposed that activity during the resting-state represents characteristics of brain-wide functional organization, which could be highly relevant for the efficient execution of cognitive tasks. However, the dynamics linking resting-state properties and task-evoked activity remain poorly understood. Therefore, the present study investigated the association between spontaneous activity as indicated by the amplitude of low frequency fluctuations (ALFF) at rest and activity during an emotional n-back task. 60 patients diagnosed with an acute MDD episode, and 52 healthy controls underwent the fMRI scanning procedure. Within both groups, positive correlations between spontaneous activity at rest and task-activation were found in core regions of the central-executive network (CEN), whereas spontaneous activity correlated negatively with task-deactivation in regions of the default mode network (DMN). Compared to healthy controls, patients showed a decreased rest-task correlation in the left prefrontal cortex (CEN) and an increased negative correlation in the precuneus/posterior cingulate cortex (DMN). Interestingly, no significant group-differences within those regions were found solely at rest or during the task. The results underpin the potential value and importance of resting-state markers for the understanding of dysfunctional network dynamics and neural substrates of cognitive processing.

Semi-Autonomous Behaviour Tree-Based Framework for Sorting Electric Vehicle Batteries Components

The process of recycling electric vehicle (EV) batteries currently represents a significant challenge to the waste management automation industry. One example of it is the necessity of removing and sorting dismantled components from EV battery pack. This paper proposes a novel framework to semi-automate the process of removing and sorting different objects from an EV battery pack using a mobile manipulator. The work exploits the Behaviour Trees model for cognitive task execution and monitoring, which links different robot capabilities such as navigation, object tracking and motion planning in a modular fashion. The framework was tested in simulation, in both static and dynamic environments, and it was evaluated based on task time and the number of objects that the robot successfully placed in the respective containers. Results suggested that the robot’s success rate in accomplishing the task of sorting the battery components was 95% and 82% in static and dynamic environments, respectively.

A robust and reliable online P300-based BCI system using Emotiv EPOC + headset

Brain–computer interface (BCI) system aims to enable interaction with people and therefore the environment without muscular activation, using changes in brain signals due to the execution of cognitive tasks. The target of the presented work is to investigate the power of Emotiv EPOC + headset to detect and record the P300 wave. Moreover, the effect of preprocessing the acquired signal was studied. Five participants were asked to attend different sessions to an equivalent 6x6 matrix while the rows and columns were randomly flashed at a rate of 200 ms. The acquired EEG data were sent wirelessly to OpenViBE software, which is employed to run the P300 speller. Two classification methods were tried: Linear discriminate analysis (LDA) and support vector machine (SVM). The capability of the headset to detect the P300 signals is proven by the results. Additionally, results show that participants reached accuracy up to 90 and 70% after only two training sessions for Linear discriminate analysis (LDA) and support vector machine (SVM) classifiers, respectively. The significance of this work is to demonstrate that such a portable and affordable headset might be useful to design and implement a robust and reliable online P300-based BCI system.

Photoplethysmographic Waveform Analysis for Autonomic Reactivity Assessment in Depression.

In the present study, a photoplethysmographic(PPG) waveform analysis for assessing differences in autonomic reactivity to mental stress between patients with Major Depressive Disorder(MDD) and healthy control(HC) subjects is presented. PPG recordings of 40MDD and 40HC subjects were acquired at basal conditions, during the execution of cognitive tasks, and at the post-task relaxation period. PPG pulses are decomposed into three waves (a main wave and two reflected waves) using a pulse decomposition analysis. Pulse waveform characteristics such as the time delay between the position of the main wave and reflected waves, the percentage of amplitude loss in the reflected waves, and the heart rate(HR) are calculated among others. The intra-subject difference of a feature value between two conditions is used as an index of autonomic reactivity. Statistically significant individual differences from stress to recovery were found for HR and the percentage of amplitude loss in the second reflected wave( A13) in both HC and MDD group. However, autonomic reactivity indices related to A13 reached higher values in HC than in MDD subjects(Cohen's[Formula: see text]), implying that the stress response in depressed patients is reduced. A statistically significant( ) negative correlation( r=-0.5) between depression severity scores and A13 was found. A decreased autonomic reactivity is associated with higher degree of depression. Stress response quantification by dynamic changes in PPG waveform morphology can be an aid for the diagnosis and monitoring of depression.

A Functional-Near-Infrared-Spectroscopy-Based Evaluation of Cognitive Lagging From Prefrontal Hemodynamics

Functional near infrared spectroscopy (fNIRS) devices capture the variability in oxygenated and deoxygenated blood flow in the cortical layers of the brain during execution of cognitive tasks. The present letter employs fNIRS device to study the cognitive lagging in working memory (WM) performance based on visuo-spatial forward span number search. The unsupervised learning-based clustering yields three distinct clusters of hemodynamic loads during the task performance. From these three clusters, we estimate cognitive lagging by computing the performance score per unit time and assigned three cognitive load classes: low, moderate, and high. Moving toward the classification approach of these three cognitive load classes, ensemble learning classifier produces higher classification accuracy, which reaches a maximum of 91.66%. The trend of shifting cognitive load from low toward high with performance score is observed from estimated Pearson's cross-correlation using the medoid points of cognitive load clusters and associated performance scores. The visualization of dynamic changes in cognitive load (low, moderate, and high) in temporal span of WM performance is obtained from the voxel plot approach, which advocates that regional deactivation of orbitofrontal cortex and augmented hemodynamic load in the dorsolateral prefrontal cortex has a possible relation with this cognitive lagging. In the view of experimental outcomes, the fNIRS-sensor-based measuring of cognitive load could be a future assessment tool for cognitive failure in higher task demand.

Effects on Postural Kinematics of Performing a Cognitive Task During Upright Standing.

The execution of cognitive tasks is known to alter postural sway during standing, but the underlying mechanisms are still debated. This study investigated how performing a mental task modified balance control during standing. We required 15 healthy adult males to maintain an upright stance under conditions of simply relaxing and maintaining normal quiet standing (control condition) or while performing a secondary cognitive task (mental arithmetic). Under each condition, we measured the participants' center of pressure and used kinematic measurements for a quantitative evaluation of postural control modulation. We calculated the standard deviation of the joint angles (ankle, knee, and hip) and the estimated joint stiffness to measure joint mobility changes in postural control. To estimate the kinematic pattern of covariation among these joints, we used uncontrolled manifold analysis, an assessment of the strength of multijoint coordination. Compared to normal standing, executing the cognitive task while standing led to reduced movements of the ankle and hip joints. There were no significant differences in ankle stiffness or uncontrolled manifold ratios between the conditions. Our results suggest that when performing a secondary cognitive task during standing, neither changes in the modification of stiffness nor the strength of multijoint coordination (both of which preserve the center of mass position) explains changes in postural sway.

Discrimination of Verbal/Visuospatial Memory Retrieval Processes by Measuring Prefrontal Lobe Blood Volume With Functional Near-Infrared Spectrometry

In cognitive task execution, retrieval processes transfer memories from long-term storage to working memory, comprising three short-term memories—phonological loop, visuospatial sketchpad, and episodic buffer—controlled by a short-term-memory central executive. The phonological loop and visuospatial sketchpad are prominent in verbal and visuospatial information processing, respectively. Individuals differ in how they retrieve the same information. Discrimination between verbal or visuospatial memory retrieval by monitoring central executive activation is useful. Functional near-infrared spectroscopy (fNIRS) was used to assess blood volume and prefrontal lobe brain activation. Using a novel approach, blood volume during memory retrieval was obtained using fNIRS. To eliminate other factors not related to the central executive activation, a base period was observed. Dynamic range was calculated for both memory retrieval and base periods, and differences obtained at ten positions within the prefrontal lobe were used as features to discriminate between verbal and visuospatial retrieval. For discrimination, k-nearest neighbor (kNN) and support vector machine (SVM) with different kernels were applied. Our method was tested on participants in verbal/visuospatial memory retrieval experiments. As the result, accuracy, positive predictive values, and negative predictive values of kNN and SVM were 1, which indicated that the proposed method successfully discriminated between verbal/visuospatial memory retrieval through prefrontal lobe blood volume observation.

Granger causality analysis in combination with directed network measures for classification of MS patients and healthy controls using task-related fMRI

Several studies have already assessed brain network variations in multiple sclerosis (MS) patients and healthy controls (HCs). The underlying neural system's functioning is apparently too complicated, however. Therefore, the neural time series' analysis through new methods is the aim of any recent research. Functional magnetic resonance imaging (fMRI) is a prominent modality for investigating the human brain's neural substrate, especially when cognitive impairment occurs. The present study was an attempt to investigate the brain network's differences between MS patients and HCs using graph-theoretic measures constructed by an effective connectivity measure through statistical tests. The results of the significant measures were then evaluated through machine learning methods. To this end, we gathered blood-oxygen level dependent (BOLD) fMRI data of the participants during the execution of paced auditory serial addition test (PASAT). Granger causality analysis (GCA) was then employed between brain regions' time series on each subject in order to construct a brain network. Afterward, the Wilcoxon rank-sum test was implemented to find the alteration of brain networks between the mentioned groups. According to the results, Global flow coefficient was significantly different between HCs and patients. Moreover, MS disease impacted several areas of the brain including Hippocampus, Para Hippocampal, Thalamus, Cuneus, Superior temporal gyrus, Heschl, Caudate, Medial Frontal Superior Gyrus, Fusiform, Pallidum, and several parts of Cerebellum in centrality measures and local flow coefficient. Most of the obtained regions were related to the cognitive impacts of the disease. We also found the best subset of graph features by means of Fisher score, and classified them to evaluate the features strength for the discrimination of MS patients from HCs via several machine learning methods. Having used the combination of Wilcoxon rank-sum test and Fisher score, we were able to classify MS patients from HCs using linear support vector machine (SVM) with an accuracy of 95%. With regard to the few existing studies on brain network of MS patients, especially during a cognitive task execution, our findings showed that the selected graph measures by Wilcoxon rank-sum test and Fisher score from the GCA-based brain networks resulted in a promising classification accuracy.

Cognitive security: A comprehensive study of cognitive science in cybersecurity

Nowadays, IoT, cloud computing, mobile and social networks are generating a transformation in social processes. Nevertheless, this technological change rise to new threats and security attacks that produce new and complex cybersecurity scenarios with large volumes of data and different attack vectors that can exceeded the cognitive skills of security analysts. In this context, cognitive sciences can enhance the cognitive processes, which can help to security analysts to establish actions in less time and more efficiently within cybersecurity operations. This works presents a cognitive security model that integrates technological solutions such as Big Data, Machine Learning, and Support Decision Systems with the cognitive processes of security analysts used to generate knowledge, understanding and execution of security response actions. The model considers alternatives to establish the automation process in the execution of cognitive tasks defined in the cyber operations processes and includes the analyst as the central axis in the processes of validation and decision making through the use of MAPE-K, OODA and Human in the Loop.

Autonomic impairment in Alzheimer’s disease is revealed by complexity analysis of functional thermal imaging signals during cognitive tasks

Objective: Alzheimer’s disease (AD) is characterized by progressive memory failures and visuospatial impairment. Moreover, AD can be accompanied by autonomic system alterations, which, among other impacts, affect thermoregulatory activity. We here investigate differences in autonomic activity between AD patients and healthy controls (HC), employing a complexity analysis of functional infrared imaging (fIRI) data acquired at rest and during the execution of clinical cognitive and mnemonic tests. Approach: fIRI allows for contactless monitoring of autonomic activity and its thermoregulatory expression without interfering with the psychophysiological state of the subject, preserving free interaction with the doctor. The signal complexity analysis, based on the sample entropy, was compared to a standard frequency-based analysis of autonomic-related signals. Main results: AD patients exhibited lower complexity of fIRI signals during the tests, which could be indicative of a stronger sympathetic activity with respect to HC. No significant effects were found at rest. No differences were found on employing frequency-based analysis. Significance: This study confirms that AD patients may exhibit peculiar autonomic responses associated with the execution of cognitive tasks that can be measured through fIRI. Moreover, these responses could be highlighted by a nonlinear metric of signal predictability such as the sample entropy establishing autonomic impairment of AD patients.

A Model Device for Real-Time Monitoring of Cognitive Activity in Humans (the “Cognovisor”)

A new method for determining the nature of thoughts in real time and reflecting them in maps of the cognitive space in humans was developed. The method is based on two approaches developed in our previous studies: a) determination of the nature of thoughts in real time from EEG rhythms using a simple artificial neural network as the classifier and b) construction of maps of the cognitive space in humans based on introducing metrics into the space of rhythmic EEG patterns established on execution of cognitive tasks of different types. We plan to use the technology developed here as tool for studying thinking.

STUDIES ON SCHIZOPHRENIA AND DEPRESSIVE DISEASES BASED ON FUNCTIONAL NEAR-INFRARED SPECTROSCOPY

Many investigations have been carried out on functional near infrared spectroscopy (fNIRS) applications in depression and schizophrenia patients that are most mysterious and costliest mental disorders in current society. fNIRS is a new optical method which assesses brain cortex hemodynamic and nervous activities non-invasively and it has been used in medicine as a study tool. Most of the researches of this approach have assessed the homodynamic response of frontal and temporal regions by means of various cognitive tasks. In this research, first, the cognitive task execution techniques have been explained concisely, and then some findings of fNIRS-based researches about depression and schizophrenia have been summarized and assessed. In fNIRS studies that have used various devices with different number of channels, the brain cortex functionality in schizophrenia and depressive patients has been investigated. The results demonstrate that a decrease in prefrontal regions activities can be observed in schizophrenia and depressive patients. Also more detailed studies illustrate ventrolateral, prefrontal and frontopolar region disorders. In severe depressive patients, a decrease in activities of prefrontal and temporal regions has been detected. Therefore, by paying attention to the deficiencies in these regions’ functions, it is possible to treat these diseases.

Dual Frequency Head Maps: A New Method for Indexing Mental Workload Continuously during Execution of Cognitive Tasks.

One goal of advanced information and communication technology is to simplify work. However, there is growing consensus regarding the negative consequences of inappropriate workload on employee's health and the safety of persons. In order to develop a method for continuous mental workload monitoring, we implemented a task battery consisting of cognitive tasks with diverse levels of complexity and difficulty. We conducted experiments and registered the electroencephalogram (EEG), performance data, and the NASA-TLX questionnaire from 54 people. Analysis of the EEG spectra demonstrates an increase of the frontal theta band power and a decrease of the parietal alpha band power, both under increasing task difficulty level. Based on these findings we implemented a new method for monitoring mental workload, the so-called Dual Frequency Head Maps (DFHM) that are classified by support vectors machines (SVMs) in three different workload levels. The results are in accordance with the expected difficulty levels arising from the requirements of the tasks on the executive functions. Furthermore, this article includes an empirical validation of the new method on a secondary subset with new subjects and one additional new task without any adjustment of the classifiers. Hence, the main advantage of the proposed method compared with the existing solutions is that it provides an automatic, continuous classification of the mental workload state without any need for retraining the classifier—neither for new subjects nor for new tasks. The continuous workload monitoring can help ensure good working conditions, maintain a good level of performance, and simultaneously preserve a good state of health.

Echoes of Affective Stimulation in Brain connectivity Networks.

Affective experience has effects on subjective feelings, physiological indices, entails immediate activity changes in the brain, and even influences brain networks in a protracted manner. However, it is still unclear, how the functional connectivity (FC) interplay between major intrinsic connectivity networks upon affective stimulation depends on affective valence, and whether this is specific for affective experience, i.e., can be distinguished from cognitive task execution. Our study included fMRI scans during and after affective stimulation with sad and neutral movies and a working memory task complemented with measures of cardiovascular activity and mood. Via parcellation of the brain into default mode network (DMN), central executive network (CEN), and dorsal attention network, and application of network-based statistics, we identified subnetworks associated with changing psychological contexts. Specific effects for affective stimulation with negative valence were both reduced heart rate variability and mood, and upregulated FC of inter-CEN-DMN connections while intra-DMN connections were downregulated. Furthermore, results demonstrated a valence-specific dynamic carry-over effect in nodes of the CEN, which temporarily increased their FC strength after affective stimulation with negative valence and exhibited distinct temporal profiles. The reported effects were clearly distinguishable from those of a cognitive task and further elucidate the trajectory of affective experience.

Localized Fluctuant Oscillatory Activity by Working Memory Load: A Simultaneous EEG-fMRI Study.

Working memory (WM) is a resource-limited memory system for temporary storage and processing of brain information during the execution of cognitive tasks. Increased WM load will increase the amount and difficulty of memory information. Several studies have used electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) to explore load-dependent cognition processing according to the time courses of electrophysiological activity or the spatial pattern of blood oxygen metabolic activity. However, the relationships between these two activities and the underlying neural mechanism are still unclear. In this study, using simultaneously collected EEG and fMRI data under an n-back verbal WM task, we modeled the spectral perturbation of EEG oscillation and fMRI activation through joint independent component analysis (JICA). Multi-channel oscillation features were also introduced into the JICA model for further analysis. The results showed that time-locked activity of theta and beta were modulated by memory load in the early stimuli evaluation stage, corresponding to the enhanced activation in the frontal and parietal lobe, which were involved in stimulus discrimination, information encoding and delay-period activity. In the late response selection stage, alpha and gamma activity changes dependent on the load correspond to enhanced activation in the areas of frontal, temporal and parietal lobes, which played important roles in attention, information extraction and memory retention. These findings suggest that the increases in memory load not only affect the intensity and time course of the EEG activities, but also lead to the enhanced activation of brain regions which plays different roles during different time periods of cognitive process of WM.

Strength and Diversity of Inhibitory Signaling Differentiates Primate Anterior Cingulate from Lateral Prefrontal Cortex.

The lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC) of the primate play distinctive roles in the mediation of complex cognitive tasks. Compared with the LPFC, integration of information by the ACC can span longer timescales and requires stronger engagement of inhibitory processes. Here, we reveal the synaptic mechanism likely to underlie these differences using in vitro patch-clamp recordings of synaptic events and multiscale imaging of synaptic markers in rhesus monkeys. Although excitatory synaptic signaling does not differ, the level of synaptic inhibition is much higher in ACC than LPFC layer 3 pyramidal neurons, with a significantly higher frequency (∼6×) and longer duration of inhibitory synaptic currents. The number of inhibitory synapses and the ratio of cholecystokinin to parvalbumin-positive inhibitory inputs are also significantly higher in ACC compared with LPFC neurons. Therefore, inhibition is functionally and structurally more robust and diverse in ACC than in LPFC, resulting in a lower excitatory: inhibitory ratio and a greater dynamic range for signal integration and network oscillation by the ACC. These differences in inhibitory circuitry likely underlie the distinctive network dynamics in ACC and LPC during normal and pathological brain states.SIGNIFICANCE STATEMENT The lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC) play temporally distinct roles during the execution of cognitive tasks (rapid working memory during ongoing tasks and long-term memory to guide future action, respectively). Compared with LPFC-mediated tasks, ACC-mediated tasks can span longer timescales and require stronger engagement of inhibition. This study shows that inhibitory signaling is much more robust and diverse in the ACC than in the LPFC. Therefore, there is a lower excitatory: inhibitory synaptic ratio and a greater dynamic range for signal integration and oscillatory behavior in the ACC. These significant differences in inhibitory synaptic transmission form an important basis for the differential timing of cognitive processing by the LPFC and ACC in normal and pathological brain states.

Effects of cognitive task execution on stable and unstable surface balance

AbstractCompromised balance with respect to both stable and passively unstable surfaces can potentially affect performance of many occupational, recreational and daily living tasks. Many tasks that require some level of balance maintenance, however, also involve burdens on individuals’ cognitive systems. Consequently the purpose of this project was to investigate the effects of performing cognitive tasks on stable surface and passively unstable surface balance performance. Participants in this study performed balance task on a passible unstable surface, as well as on a stable surface, with and without concurrent quantitative and language based cognitive tasks. Concurrent cognitive tasks did not demonstrate statistically significant effects on stable surface balance. For unstable surface balance, concurrent cognitive tasks had significant effects on trunk and arm segment angular velocities, while significant effects on movement of the passively unstable surface were not observed. The lack of cognitive task...

Influência de tarefas cognitivas sobre o equilíbrio estático de indivíduos saudáveis

Introdução: O controle postural depende da ação integrada dos sistemas sensoriais e motores, e uma maior demanda atencional em situações de dupla-tarefa possivelmente acarreta maior oscilação postural. Objetivo: comparar a influência de tarefas cognitivas sobre o equilíbrio estático de indivíduos saudáveis. Materiais e métodos: estudo transversal realizado com 20 participantes adultos saudáveis. Os participantes foram submetidos a avaliação estabilométrica (olhos abertos e fechados) durante a execução de tarefas cognitivas (Stroop Test e contagem regressiva) e na situação controle (sem tarefa). Os deslocamentos do centro de pressão foram coletados e analisado. Resultados: Houve diferença significante dos efeitos obtidos na execução da tarefa matemática em comparação às demais situações (aumento da área de deslocamento e oscilações do centro de pressão). As demais comparações do tamanho do efeito não demonstraram significância. Conclusão: A execução de tarefas cognitivas influenciou o equilíbrio estático dos participantes. A tarefa matemática demonstrou maior influencia sobre o equilíbrio estático.

Light-sensitive brain pathways and aging.

Notwithstanding its effects on the classical visual system allowing image formation, light acts upon several non-image-forming (NIF) functions including body temperature, hormonal secretions, sleep-wake cycle, alertness, and cognitive performance. Studies have shown that NIF functions are maximally sensitive to blue wavelengths (460–480 nm), in comparison to longer light wavelengths. Higher blue light sensitivity has been reported for melatonin suppression, pupillary constriction, vigilance, and performance improvement but also for modulation of cognitive brain functions. Studies investigating acute stimulating effects of light on brain activity during the execution of cognitive tasks have suggested that brain activations progress from subcortical regions involved in alertness, such as the thalamus, the hypothalamus, and the brainstem, before reaching cortical regions associated with the ongoing task. In the course of aging, lower blue light sensitivity of some NIF functions has been reported. Here, we first describe neural pathways underlying effects of light on NIF functions and we discuss eye and cerebral mechanisms associated with aging which may affect NIF light sensitivity. Thereafter, we report results of investigations on pupillary constriction and cognitive brain sensitivity to light in the course of aging. Whereas the impact of light on cognitive brain responses appears to decrease substantially, pupillary constriction seems to remain more intact over the lifespan. Altogether, these results demonstrate that aging research should take into account the diversity of the pathways underlying the effects of light on specific NIF functions which may explain their differences in light sensitivity.