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Graph theory allows us to gain better insight concerning changes in brain functional architecture associated with cognitive impairments in the early stages of multiple sclerosis (MS). In the present study, we employed a machine-learning system based on graph measures from functional networks constructed by cognitive task-related functional magnetic resonance imaging (fMRI) data. We used a predefined atlas to define the brain regions and Pearson’s correlation to describe the connectivity strength between the regions. Then, several graph metrics were extracted for each subject. After that, the most efficient subsets of features were selected through the Wilcoxon rank-sum test, and the linear support vector machine (SVM) classifier was employed to distinguish between MS and healthy subjects. The node degree, subgraph centrality, K-coreness, and PageRank centralities measured in the left fusiform, hippocampus, and parahippocampal gyri regions demonstrated an accuracy of 85% through the combination of all local measures. Two optimal global measures, modularity and small-worldness index, and individual betweenness centrality feature improved the identification of MS patients with a sensitivity of 81.25%. Our results indicated the potential of the proposed system to identify cognitive changes in early MS for diagnostic purposes.

Introduction: Natural rewards are essential for survival. However, drug-seeking behaviors can be maladaptive and endanger survival. The present study was conducted to enhance our understanding of how animals respond to food and morphine as natural and drug rewards, respectively, in a conditioned place preference (CPP) paradigm. Methods: We designed a protocol to induce food CPP and compare it as a natural reward with morphine CPP in rats. The protocol for reward induction in both groups (foods and morphine) consisted of three phases: pre-test, conditioning, and post-test. In morphine groups, we injected morphine as a reward (5 mg/kg, SC). To induce natural reward, we used two different protocols. In the first one, the rats were deprived of food for 24 h. In the other method, the rats were restricted to food for 14 days. During the conditioning period, the animals received daily chow, biscuits, or popcorn as a reward inducer. Results: Results revealed that CPP was not induced in food-deprived rats. A combination of food restriction (as a facilitator) and a biscuit or popcorn-induced reward using CPP. In contrast, food deprivation did not facilitate food CPP in response to regular food. Interestingly the CPP score of the group which received biscuits during a 7-day conditioning period was more than that of the morphine group. Conclusion: In conclusion, food restriction could be a better protocol than food deprivation to facilitate food reward.

Neural synchronization has been engaged in several brain mechanisms. Previous studies have shown that the interaction between the time of spiking activity and phase of local field potentials (LFPs) plays a key role in many cognitive functions. However, the potential role of this spike-LFP phase coupling (SPC) in neural coding is not fully understood. Here, we sought to investigate the role of this SPC for encoding the sensory properties of visual stimuli. To this end, we measured SPC strength in the preferred and anti-preferred motion directions of stimulus presented inside the receptive field of middle temporal (MT) neurons. We found a selective response in terms of SPC strength for different directions of motion. Remarkably, this selective function is inverted with respect to the spiking activity. In other words, the least SPC occurs for the preferred direction (based on the spike rate), and vice versa; the strongest SPC is induced in the anti-preferred direction. Altogether, these findings imply that the neural system may use spike-LFP phase coupling in the primate visual cortex to encode sensory information.

Many visual attention models have been presented to obtain the saliency of a scene, i.e., the visually significant parts of a scene. However, some mechanisms are still not taken into account in these models, and the models do not fit the human data accurately. These mechanisms include which visual features are informative enough to be incorporated into the model, how the conspicuity of different features and scales of an image may integrate to obtain the saliency map of the image, and how the structure of an image affects the strategy of our attention system. We integrate such mechanisms in the presented model more efficiently compared to previous models. First, besides low-level features commonly employed in state-of-the-art models, we also apply medium-level features as the combination of orientations and colors based on the visual system behavior. Second, we use a variable number of center-surround difference maps instead of the fixed number used in the other models, suggesting that human visual attention operates differently for diverse images with different structures. Third, we integrate the information of different scales and different features based on their weighted sum, defining the weights according to each component's contribution, and presenting both the local and global saliency of the image. To test the model's performance in fitting human data, we compared it to other models using the CAT2000 dataset and the Area Under Curve (AUC) metric. Our results show that the model has high performance compared to the other models (AUC = 0.79 and sAUC = 0.58) and suggest that the proposed mechanisms can be applied to the existing models to improve them.

Despite the overlapping neural circuits underlying natural and drug rewards, several studies have suggested different behavioral and neurochemical mechanisms in response to drug vs. natural rewards. The strong link between hippocampal theta oscillations (4–12 Hz) and reward-associated learning and memory has raised the hypothesis that this rhythm in hippocampal CA1 might be differently modulated by drug- and natural-conditioned place preference (CPP). Time–frequency analysis of recorded local field potentials (LFPs) from the CA1 of freely moving male rats previously exposed to a natural (in this case, food), drug (in this case, morphine), or saline (control) reward cue in the CPP paradigm showed that the hippocampal CA1 theta activity represents a different pattern for entrance to the rewarded compared to unrewarded compartment during the post-test session of morphine- and natural-CPP. Comparing LFP activity in the CA1 between the saline and morphine/natural groups showed that the maximum theta power occurred before entering the unrewarded compartment and after the entrance to the rewarded compartment in morphine and natural groups, respectively. In conclusion, our findings suggest that drug and natural rewards could differently affect the theta dynamic in the hippocampal CA1 region during reward-associated learning and contextual cueing in the CPP paradigm.

Achieving an efficient and reliable method is essential to interpret a user’s brain wave and deliver an accurate response in biomedical signal processing. However, EEG patterns exhibit high variability across time and uncertainty due to noise and it is a significant problem to be addressed in mental task as motor imagery. Therefore, fuzzy components may help to enable a higher tolerance to noisy conditions. With the advent of Deep Learning and its considerable contributions to Artificial intelligence and data analysis, numerous efforts have been made to evaluate and analyze brain signals. In this study, to make use of neural activity phenomena, the feature extraction preprocessing is applied based on Multi-scale filter bank CSP. In the following, the hybrid series architecture named EEG-CLFCNet is proposed which extract the frequency and spatial features by Compact-CNN and the temporal features by the LSTM network. However, the classification results are evaluated by merging the fully connected network and fuzzy neural block. Here, the proposed method is further validated by the BCI competition IV-2a dataset and compare with two hyperparameter tuning methods, Coordinate-descent and Bayesian optimization algorithm. The proposed architecture that used fuzzy neural block and Bayesian optimization as tuning approach, results in better classification accuracy compared with the state-of-the-art literatures. As results shown, the remarkable performance of the proposed model, EEG-CLFCNet, and the general integration of fuzzy units to other classifiers would pave the way for enhanced MI-based BCI systems.

Since EEG signals encode an individual’s intent of executing an action, scientists have extensively focused on this topic. Motor Imagery (MI) signals have been used by researchers to assistance disabled persons, for autonomous driving and even control devices such as wheelchairs. Therefore, accurate decoding of these signals is essential to develop a Brain–Computer interface (BCI) systems. Due to dynamic nature, low signal-to-noise ratio and complexity of EEG signals, EEG decoding is not simple task. Extracting temporal and spatial features from EEG is accessible via Convolution neural network (CNN). However, enhanced CNN models are required to learn the dynamic correlations existing in MI signals. It is found that good features are extracted via CNN in both deep and shallow models, which indicate that various levels related features can be mined. In this case, spatial patterns from multi-scaled data in different frequency bands are learnt at first and then the temporal and frequency band information from projected signals is extracted. Here, to make use of neural activity phenomena, the feature extraction process employed is based on Multi-scale FBCSP (MSFBCSP). In CNN, the envelope of each spatially filtered signal is extracted in time dimension by performing Hilbert transform. However, to access common morphologies, the convolutional operation across time is performed first and then another convolution layer across channels in the frequency band is used to represent the carried information in a more compact form. Moreover, Bayesian approach is used for mapping hyperparameters to a probability of score on the objective function. The prominent feature of the proposed network is the high capacity of preserving and utilizing the information encoded in frequency bands. Our proposed method significantly improves the efficiency of current classification method in specific dataset of the physionet. According to empirical evaluations, strong robustness and high decoding classification are two distinctive characteristics of our proposed work.

How does the human brain prioritize different visual representations in working memory (WM)? Here, we define the oscillatory mechanisms supporting selection of “where” and “when” features from visual WM storage and investigate the role of pFC in feature selection. Fourteen individuals with lateral pFC damage and 20 healthy controls performed a visuospatial WM task while EEG was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. A retro-cue presented mid-delay prompted which of the two shapes had been in either the top/bottom spatial position or first/second temporal position. We found that cross-frequency coupling between parieto-occipital alpha (α; 8–12 Hz) oscillations and topographically distributed gamma (γ; 30–50 Hz) activity tracked selection of the distinct cued feature in controls. This signature of feature selection was disrupted in patients with pFC lesions, despite intact α–γ coupling independent of feature selection. These findings reveal a pFC-dependent parieto-occipital α–γ mechanism for the rapid selection of visual WM representations.

Research in cognitive neuroscience has renewed the idea that brain oscillations are a core organization implicated in fundamental brain functions. Growing evidence reveals that the characteristic features of these oscillations, including power, phase and frequency, are highly non-stationary, fluctuating alongside alternations in sensation, cognition and behavior. However, there is little consensus on the functional implications of the instantaneous frequency variation in cortical excitability and concomitant behavior. Here, we capitalized on intracortical electrophysiology in the macaque monkey’s visual area MT performing a visuospatial discrimination task with visual cues. We observed that the instantaneous frequency of the theta–alpha oscillations (4–13 Hz) is modulated among specific neurons whose RFs overlap with the cued stimulus location. Interestingly, we found that such frequency modulation is causally correlated with MT excitability at both scales of individual and ensemble of neurons. Moreover, studying the functional relevance of frequency variations indicated that the average theta–alpha frequencies foreshadow the monkey’s reaction time. Our results also revealed that the neural synchronization strength alters with the average frequency shift in theta–alpha oscillations, suggesting frequency modulation is critical for mutually adjusting MTs’ rhythms. Overall, our findings propose that theta–alpha frequency variations modulate MT’s excitability, regulate mutual neurons’ rhythmicity and indicate variability in behavior.

How does the human brain integrate spatial and temporal information into unified mnemonic representations? Building on classic theories of feature binding, we first define the oscillatory signatures of integrating ‘where’ and ‘when’ information in working memory (WM) and then investigate the role of prefrontal cortex (PFC) in spatiotemporal integration. Fourteen individuals with lateral PFC damage and 20 healthy controls completed a visuospatial WM task while electroencephalography (EEG) was recorded. On each trial, two shapes were presented sequentially in a top/bottom spatial orientation. We defined EEG signatures of spatiotemporal integration by comparing the maintenance of two possible where-when configurations: the first shape presented on top and the reverse. Frontal delta-theta (δθ; 2–7 Hz) activity, frontal-posterior δθ functional connectivity, lateral posterior event-related potentials, and mesial posterior alpha phase-to-gamma amplitude coupling dissociated the two configurations in controls. WM performance and frontal and mesial posterior signatures of spatiotemporal integration were diminished in PFC lesion patients, whereas lateral posterior signatures were intact. These findings reveal both PFC-dependent and independent substrates of spatiotemporal integration and link optimal performance to PFC.

Storing information from incoming stimuli in working memory (WM) is essential for decision-making. The prefrontal cortex (PFC) plays a key role to support this process. Previous studies have characterized different neuronal populations in the PFC for working memory judgements based on whether an originally presented stimulus matches a subsequently presented one (matching-rule decision-making). However, much remains to be understood about this mechanism at the population level of PFC neurons. Here, we hypothesized differences in processing of feature vs. spatial WM within the PFC during a matching-rule decision-making task. To test this hypothesis, the modulation of neural activity within the PFC during two types of decision-making tasks (spatial WM and feature WM) in comparison to a passive fixation task was determined. We discovered that neural population-level activity within the PFC is different for the match vs. non-match condition exclusively in the case of the feature-specific decision-making task. For this task, the non-match condition exhibited a greater firing rate and lower trial-to-trial variability in spike count compared to the feature-match condition. Furthermore, the feature-match condition exhibited lower variability compared to the spatial-match condition. This was accompanied by a faster behavioral response time for the feature-match compared to the spatial-match WM task. We attribute this lower across-trial spiking variability and behavioral response time to a higher task-relevant attentional level in the feature WM compared to the spatial WM task. The findings support our hypothesis for task-specific differences in the processing of feature vs. spatial WM within the PFC. This also confirms the general conclusion that PFC neurons play an important role during the process of matching-rule governed decision-making.

In this paper, a novel fully-automated state-based decoding method has been proposed for continuous decoding problems in brain-machine interface (BMI) systems, called Gaussian mixture of model (GMM)-assisted PLS (GMMPLS). In contrast to other state-based and hierarchical decoders, the proposed method does not demand any prior information about the desired output structure. Instead, GMMPLS uses the GMM algorithm to divide the desired output into a specific number of states (clusters). Next, a logistic regression model is trained to predict the probability membership of each time sample for each state. Finally, using the concept of the partial least square (PLS) algorithm, GMMPLS constructs a model for decoding the desired output using the input data and the achieved membership probabilities. The performance of the GMMPLS has been evaluated and compared to PLS, the nonlinear quadratic PLS (QPLS), and the bayesian PLS (BPLS) methods through a simulated dataset and two different real-world BMI datasets. The achieved results demonstrated that the GMMPLS significantly outperformed PLS, QPLS, and BPLS overall datasets.

Background Brain Computer Interfaces (BCIs) translate the activity of the nervous system to a control signal which is interpretable for an external device. Using continuous motor BCIs, the user will be able to control a robotic arm or a disabled limb continuously. In addition to decoding the target position, accurate decoding of force amplitude is essential for designing BCI systems capable of performing fine movements like grasping. In this study, we proposed a stack Long Short-Term Memory (LSTM) neural network which was able to accurately predict the force amplitude applied by three freely moving rats using their Local Field Potential (LFP) signal. Results The performance of the network was compared with the Partial Least Square (PLS) method. The average coefficient of correlation (r) for three rats were 0.67 in PLS and 0.73 in LSTM based network and the coefficient of determination ($$R^{2}$$ R 2 ) were 0.45 and 0.54 for PLS and LSTM based network, respectively. The network was able to accurately decode the force values without explicitly using time lags in the input features. Additionally, the proposed method was able to predict zero-force values very accurately due to benefiting from an output nonlinearity. Conclusion The proposed stack LSTM structure was able to predict applied force from the LFP signal accurately. In addition to higher accuracy, these results were achieved without explicitly using time lags in input features which can lead to more accurate and faster BCI systems.

Brain-Computer Interface (BCI) based on Local Field Potential (LFP) has recently been developed to restore communication or behavioral functions. LFP provides comprehensive information, due to its stability, robustness, and reach frequency content within the cognitive process. It has been demonstrated that spatial attention can be decoded from brain activity in the visual cortical areas. However, whether motion direction can be decoded from the LFP signal in the primate visual cortex remains uninvestigated, as well as how decoding performance may be influenced by spatial attention. In this paper, these issues were examined by recording LFP from the middle temporal area (MT) of macaque, employing machine learning algorithms. The animal was trained to report a brief direction change in a target stimulus which moved in various directions during a visual attention task. It was found that the LFP-gamma power was able to provide significant information to reliably decode motion direction, compared with other frequency bands, on a single-trial basis. Moreover, the results show that spatial attention leads to enhancements in motion direction discrimination performance. The highest decoding performance was achieved in the high-gamma frequencies (60–120Hz) when targets were presented inside the receptive field in opposite directions. Using a feature selection approach, performance was improved by optimally selecting features where the highest level of participation was observed in the gamma-band. Generally, the results suggest that in the MT area, LFP signals exhibit appreciable information about visual features like motion direction, which could thus be utilized as a control signal for cognitive BCI systems.

Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and require frequent calibration and high computational complexity. Furthermore, most applications of closed-loop stimulation act on peripheral nerves or muscles, resulting in rapid muscle fatigue. Here we show that a local field potential-based BCI can control spinal stimulation and improve forelimb function in rats with cervical SCI. We decoded forelimb movement via multi-channel local field potentials in the sensorimotor cortex using a canonical correlation analysis algorithm. We then used this decoded signal to trigger epidural spinal stimulation and restore forelimb movement. Finally, we implemented this closed-loop algorithm in a miniaturized onboard computing platform. This Brain-Computer-Spinal Interface (BCSI) utilized recording and stimulation approaches already used in separate human applications. Our goal was to demonstrate a potential neuroprosthetic intervention to improve function after upper extremity paralysis.

A method that has recently been mentioned as information encoding brain is cross-frequency coupling (CFC). It is generally assumed that CFC can play a crucial role in perception, memory, and attention. In this study, two new indices for evaluating frequency–amplitude coupling (FAC) through generalized linear model (GLM) and linear regression method were introduced and investigated along with other CFC indices. Electroencephalogram (EEG) signals were recorded during covert visual attention tasks to find out the CFC index capability so as to distinguish different states in the mentioned tasks. To this end, machine learning algorithms were used and four various types of CFC, phase–amplitude coupling (PAC), phase–phase coupling (PPC), amplitude–amplitude coupling (AAC), and frequency–amplitude coupling (FAC) in recorded signals were considered as inputs for classifiers. The results demonstrated that the proposed method used for evaluating FAC through linear regression can provide more information about the different states in two covert attention tasks using quadratic discriminant analysis (QDA) by classification performance of 94.21% and 90.54% in color and direction tasks, respectively. Also, FAC that used a GLM model and PAC had a higher performance compared with PPC and AAC in color task (90.74 and 92.24% against 83.21 and 86.22). We can conclude that CFC can encompass useful information about semantic category of stimuli in covert attention tasks and can be used as an acceptable alternative for the time–frequency features of brain signals.

Introduction: Stress and fear caused by computer games have been shown to have various effects on the cognitive system. This work was aimed to investigate the effects of short-time horror computer games on cognitive indicators. Methods: A total of twenty female subjects were recruited and divided into experimental and control groups. All required tests were performed before and after the intervention (playing or watching horror game) on the control and experimental groups. The saliva samples were collected before and after the intervention to measure levels of cortisol and alpha-amylase. Also, blood was taken before and during the game from each subject to evaluate plasma levels of oxytocin and brain-derived neurotrophic factor. The Brain waveforms were acquired by Emotive brain signal recording device before and after the intervention. Data analysis was conducted using R and MATLAB software. Results: The cortisol and alpha-amylase levels were shown to significantly increase after the horror game playing. Also, the levels of oxytocin were significantly higher after the experimentation. The levels of brain-derived neurotrophic factor were displayed to reduce after the experimentation. The results of the brainwave analysis revealed that the average stress index was significantly higher, while the average attention index was lower after playing the game. No significant difference in the study variables was observed in the control group. Conclusion: Horror computer games may have adverse effects on the activity of the stress system in the central nervous system. Fear-induced stress was shown to relatively undermine some cognitive elements.

Attention selectively routes the most behaviorally relevant information from the stream of sensory inputs through the hierarchy of cortical areas. Previous studies have shown that visual attention depends on the phase of oscillatory brain activities. These studies mainly focused on the stimulus presentation period, rather than the pre-stimulus period. Here, we hypothesize that selective attention controls the phase of oscillatory neural activities to efficiently process relevant information. We document an attentional modulation of pre-stimulus inter-trial phase coherence (a measure of deviation between instantaneous phases of trials) of low frequency local field potentials (LFP) in visual area MT of macaque monkeys. Our data reveal that phase coherence increases following a spatial cue deploying attention towards the receptive field of the recorded neural population. We further show that the attentional enhancement of phase coherence is positively correlated with the modulation of the stimulus-induced firing rate, and importantly, a higher phase coherence is associated with a faster behavioral response. These results suggest a functional utilization of intrinsic neural oscillatory activities for an enhanced processing of upcoming stimuli.

Continuous decoding is a crucial step in many types of brain-computer interfaces (BCIs). Linear regression techniques have been widely used to determine a linear relation between the input and desired output. A serious issue in this technique is the over-fitting phenomenon. Partial least square (PLS) is a well-known and popular method which tries to overcome this problem. PLS calculates a set of latent variables which are maximally correlated to the output and determines a linear relation between a low-rank estimation of the input and output data. However, this method has shown its potential to overfit the training data in many cases. In this paper, a regularized version of PLS (RPLS) is proposed which tries to determine a linear relation between the latent vector of the input and desired output using the regularized least square instead of the ordinary one. This approach is able to control the effect of non-efficient and non-generalized latent vectors in prediction. We have shown that the proposed method outperforms Ridge regression (RR), PLS, and PLS with regularized weights (PLSRW) in estimating the output in two different real BCI datasets, Neurotycho public electrocorticogram (ECoG) dataset for decoding trajectory of hand movements in monkeys and our own local field potential (LFP) dataset for decoding applied force performed by rats. Furthermore, the results indicate that RPLS is more robust against the increase in the number of latent vectors compared to PLS and PLSRW. Next, we evaluated the resistance of our proposed method against the presence of different noise levels in a BCI application and compared it to other techniques using a semi-simulated dataset. This approach revealed that RPLS offered a higher performance compared with other techniques in all levels of noise. Finally, to illustrate the usability of RPLS in other type of data, we presented the application of this method in predicting relative active substance content of pharmaceutical tablets using near-infrared (NIR) transmittance spectroscopy data. This application showed a superior performance of the proposed method compared to other decoding methods.

Introduction: Nowadays, computer games play an important role on the cognitive and behavioral health of the community. The purpose of this study is to investigate the short-term effects of Flow Free® on the neurologic characteristics of the players of these games. Materials and Methods: A total of 40 healthy male students aged 20 years and above were randomly divided into the control and experimental groups. All tests were performed before and after the game in experimental group. The tests were performed only once in the control group without participating in the game following watching the game. Saliva samples were collected to measure cortisol and alpha amylase levels. Cognitive tests and electro-encephalography were performed. Data were analyzed using Wilcoxon signed-rank test. Results: There was no significant difference between the groups in demographic characteristics and pre-intervention measures (the same as the experimental group participants without playing the game). The post-tests showed no significant change in the control group. In the experimental group, the Paced Auditory Serial Addition Test (PASAT) results indicated a significant increase in mental health (P ≤ 0.037) and sustained attention (P ≤ 0.046); the changes in the concentration of alpha amylase (P ≤ 0.009) and salivary cortisol (P ≤ 0.01) after the game showed a significant increase compared to the pre-test. Additionally, an increase in the mean surface of the pattern of stress index (P ≤ 0.039) and attention index (P ≤ 0.048) were recorded. The post-test measures in the experimental group were also significantly different from those of the control group. Conclusion: Flow Free®, as a stimulant of the central nervous system (CNS), led to the increased activity of the stress path and changes in brain signals, hence strengthening the cognitive element of attention in the players after the game.

In order to discriminate different spikes in an extracellular recording, a multitude of successful spike sorting algorithms has been proposed up to now. However, new implantable neuroprosthetics containing a spike sorting block necessitate the use of a real-time and a preferably unsupervised method. The aim of this article is to propose a new unsupervised spike sorting algorithm which could work in real-time. As opposed to most traditional frameworks that consist of separate noise cancelation and feature extraction steps, here a sequential algorithm is proposed which makes use of noise statistics and uses data samples as features. For each detected spike, the difference between the detected spike and all the previously detected spike templates are calculated. If the output is a signal similar to noise, this indicates that the new spike is fired from a previously observed neuron. Two varieties of the general method are illustrated and a set of clustering indices which determine an optimal clustering is used to set the parameters. Clustering indices surpassed 0.90 (out of 1) for synthetic data with modest noise level. Experiments with our recorded signals showed satisfactory results in clustering and template identification. Spike sorting is an active field. A deficiency in conventional spike sorting algorithms is that most of them are either supervised or offline. Here, we present an online unsupervised algorithm which could be developed as a solution for current neuroprosthetics. Since the present method clustered real spikes data appropriately without a need for training data, the methodology could be adapted to be used in implantable devices.

The functional use of brain-machine interfaces (BMIs) in everyday tasks requires the accurate decoding of both movement and force information. In real-word tasks such as reach-to-grasp movements, a prosthetic hand should be switched between reaching and grasping modes, depending on the detection of the user intents in the decoder part of the BMI. Therefore, it is important to detect the rest or active states of different actions in the decoder to produce the corresponding continuous command output during the estimated state. In this study, we demonstrated that the resting and force-generating time-segments in a key pressing task could be accurately detected from local field potentials (LFPs) in rat's primary motor cortex. Common spatial pattern (CSP) algorithm was applied on different spectral LFP sub-bands to maximize the difference between the two classes of force and rest. We also showed that combining a discrete state decoder with linear or non-linear continuous force variable decoders could lead to a higher force decoding performance compared with the case we use a continuous variable decoder only. Moreover, the results suggest that gamma LFP signals (50-100 Hz) could be used successfully for decoding the discrete rest/force states as well as continuous values of the force variable. The results of this study can offer substantial benefits for the implementation of a self-paced, force-related command generator in BMI experiments without the need for manual external signals to select the state of the decoder.

Objective. Schizotypy, a potential phenotype for schizophrenia, is a personality trait that depicts psychosis-like signs in the normal range of psychosis continuum. Family communication may affect the social functioning of people with schizotypy. Greater family stress, such as irritability, criticism and less praise, is perceived at a higher level of schizotypy. This study aims to determine the differences between people with high and low levels of schizotypy using electroencephalography (EEG) during criticism, praise and neutral comments. EEGs were recorded from 29 participants in the general community who varied from low schizotypy to high schizotypy (HS) during a novel emotional auditory oddball task. Approach. We consider the difference in event-related potential parameters, namely the amplitude and latency of P300 subcomponents (P3a and P3b), between pairs of target words (standard, positive, negative and neutral). A model based on tensor factorization is then proposed to detect these components from the EEG using the CANDECOMP/PARAFAC decomposition technique. Finally, we employ the mutual information estimation method to select influential features for classification. Main results. The highest classification accuracy, sensitivity, and specificity of 93.1%, 94.73%, and 90% are obtained via leave-one-out cross validation. Significance. This is the first attempt to investigate the identification of individuals with psychometrically-defined HS from brain responses that are specifically associated with perceiving family stress and schizotypy. By measuring these brain responses to social stress, we achieve the goal of improving the accuracy in detection of early episodes of psychosis.

Steady-state visually evoked potentials (SSVEP) are one of the most important paradigms in the BCI Domain. Among the best methods for detecting frequency in the SSVEP-based BCI is the Canonical Correlation Analysis (CCA), which calculates canonical correlation between two sets of multidimensional variables, the electroencephalogram (EEG) and reference signals. Despite its efficiency and widespread application, CCA algorithm has some limitations. One major limitation of CCA is to only consider the spatial domain information of the signal.However, regarding frequency of signal as another critical feature of the signals, combining both spatial and frequency domain information can significantly improve the performance of frequency recognition. Although several previous studies about CCA algorithm, could improve its performance, they have not addressed CCA algorithm's limitation. To address this concern, in the current study, we presented Spatio-Spectral CCA (SS-CCA) algorithm, which is inspired from Common Spatio-Spectral Patterns (CSSP) algorithm. In the SS-CCA algorithm, we added a time delay to the EEG signal, in order to simultaneously optimize spatial and frequency information and obtain the canonical variables. Accordingly, for correlation coefficient's calculations, more information from EEG signal is utilized.Finally, SS-CCA algorithm which is used as the base model of Filter Bank CCA (FBCCA), and Filter Bank SS-CCA algorithms, can help increase the frequency recognition performance. In order to evaluate the proposed method, 35-subject benchmark dataset were used. Proposed algorithm yielded mean accuracy 98.33 across all subjects.Our classification accuracy and Information Transfer Rate (ITR) results showed that the performance of the above-mentioned method improves in comparison to the CCA.In conclusion, using the proposed SS-CCA algorithm instead of the CCA, in all our experiments the CCA-based methods were improved.

Background Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs. Methods In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM). Results According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively. Conclusion The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

Interaction of oscillatory rhythms at different frequencies is considered to provide a neuronal mechanism for information processing and transmission. These interactions have been suggested to have a vital role in cognitive functions such as working memory and decision-making. Here, we investigated the medial prefrontal cortex (mPFC), which is known to have a critical role in successful execution of spatial working memory tasks. We recorded local field potential oscillations from mPFC while rats performed a delayed-non-match-to-place (DNMTP) task. In the DNMTP task, the rat needed to decide actively about the pathway based on the information remembered in the first phase of each trial. Our analysis revealed a dynamic phase-amplitude coupling (PAC) between theta and high frequency oscillations (HFOs). This dynamic coupling emerged near the turning point and diminished afterward. Further, theta activity during the delay period, which is thought of as the maintenance phase, in the absence of the coupling, can predict task completion time. We previously reported diminished rat performance in the DNMTP task in response to electromagnetic radiation. Here, we report an increase in the theta rhythm during delay activity besides diminishing the coupling after electromagnetic radiation. These findings suggest that the different roles of the mPFC in working memory could be supported by separate mechanisms: Theta activity during the delay period for information maintenance and theta-HFOs phase-amplitude coupling relating to the decision-making procedure.

Hierarchical model and X (HMAX), which is a feedforward network, has displayed profoundly satisfying performance for object recognition tasks in comparison with other state-of-the-art machine vision algorithms. Nevertheless, the standard HMAX model has two major drawbacks. The first one is the computational cost of the S2 layer. The second one is random patch selection of HMAX model, which leads to low performance as meaningless and redundant patches are extracted. In this paper, a faster and more accurate HMAX model in combination with scale-invariant feature transform algorithm is proposed to improve mentioned weaknesses. Our proposed model consists of two levels of improvement. The first level is increasing the speed of matching in S2 layer by comparing the extracted patches with only a few informative patches rather than the whole image. The second one is related to the performance improvement by extracting the discriminative and distinctive patches in the training stage. The obtained results prove that the proposed model performs classification tasks faster than both the standard HMAX model and the binary-based HMAX model (B-HMAX). Meanwhile, the performance for the proposed model stays almost as high as that of the B-HMAX model.

One of the most important topics in neuroscience is the issue of brain electrical stimulation and its widespread use. Based on this issue, rat robot, a rat navigation system was introduced in 2002, which has utilized brain electric stimulations as a guide and a reward for driving rats. Recently systems have been designed which are automatically navigated by a computer. One of the obstacles in the way of these systems is to select the stimulation frequency of the somatosensory cortex for the rotation action. In this paper, the stimulation parameters of the somatosensory cortex for rotation in the T-shaped maze were examined for the first time with applying only one pulse train. Then, the optimized parameters have been utilized in a complex maze. The results show that the performance is directly related to the pulse width and it has an inverse relationship with the pulse intervals. With optimal parameters, correctly controlling the animal in 90% of the trials in the T-maze, were managed, and in the complex maze, about 70% of the stimuli with optimized parameters were with only applying one pulse train. The results show that the stimulation parameters for navigation with only one pulse train are well optimized, and the results of this paper can be a trigger for an automatic navigation and reduce the computational costs and automatic system errors.

Neural decoding allows us to study the brain functions by investigating the relationship between a stimulus and the corresponding response. Recently, the local field potential (LFP) has been targeted as a hallmark of brain activity for neural decoding. Despite several decoding methods, there is still a lack of a comprehensive framework to decode cognitive functions in an integrated structure. Here, we addressed this issue by developing a dictionary-based method to represent the LFP signals via a bag-of-words (BOW) approach. First, we defined a general dictionary consisting of various Gabor wavelets as the words which enabled us to represent LFPs in word domain. For each trial, the LFP signal was convolved with the dictionary words. The integral of the absolute value and the mean phase of the complex output were considered as histogram weights. In the next step, using cross-validation leave-one-out method, the trials were split into the training and test sets. The weights of each individual word were swapped across trials within a certain category of the training set while the sequential order was maintained. Finally, the test trial was classified using label voting in the k-nearest training trials. We conducted the proposed method on two independent LFP data sets, recorded from the rat primary auditory cortex (A1) and monkey middle temporal area in order to evaluate its efficiency. In addition to the chance level, the proposed method was compared with a standard BOW approach that has been extended recently for biomedical signals classification. Results show a high efficiency (~ 15% improvement in decoding accuracy) of the proposed method. Together, the aforementioned method provides a comprehensive framework for single-trial decoding from short-length electrophysiological signals.

Male and female brains have different structures, which can make genders produce various eye- movement patterns. This study presents the results of an eye tracking experiment in which we analyzed the eye movements of 25 male and 20 female participants during passive indoor picture viewing. We examined eye-movement parameters, namely fixation duration, scan path length, number of saccades, spatial density, saccade amplitude, and the ratio of total fixation duration to total saccade duration so as to investigate gender differences in eye-movement patterns while indoor picture viewing. We found significant differences in eye-movement patterns between genders. The results of eye-movement analysis also indicated that females showed more explorative gaze behavior, indicated by larger saccade amplitudes, and by longer scan paths. Furthermore, owing to shorter ratio of fixation durations to saccade duration in females as compared to male, we speculate that females inspect the images faster than males. In addition, we classified the genders into two subgroups-males and females-based on their eye-movement parameters by using a support vector machine classifier achieving an accuracy of 70%. We have come to the result males and females - with same culture - see the environment differently. Our findings have profound implications for researches employing gaze-based models.

Significance Dynamical coordination of the neural activity between individual neurons is known to have a key role in the efficient transfer of sensory information to associative areas. Here, we report a role of interneuronal synchrony within the high-gamma (180 to 220 Hz) frequency range of activity in macaque area MT (a visual area in the dorsal visual pathway) in determining behavioral performance. This is, however, in contrast to previous reports for the ventral visual pathway (such as area V4), where only gamma range (40 to 70 Hz) was observed to play a role. We propose that such a difference between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas., Efficient transfer of sensory information to higher (motor or associative) areas in primate visual cortical areas is crucial for transforming sensory input into behavioral actions. Dynamically increasing the level of coordination between single neurons has been suggested as an important contributor to this efficiency. We propose that differences between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas, ensuring a proper routing of the information flow. Here we determined the level of coordination between neurons in area MT in macaque visual cortex in a visual attention task via the strength of synchronization between the neurons’ spike timing relative to the phase of oscillatory activities in local field potentials. In contrast to reports on the ventral visual pathway, we observed the synchrony of spikes only in the range of high gamma (180 to 220 Hz), rather than gamma (40 to 70 Hz) (as reported previously) to predict the animal’s reaction speed. This supports a mechanistic role of the phase of high-gamma oscillatory activity in dynamically modulating the efficiency of neuronal information transfer. In addition, for inputs to higher cortical areas converging from the dorsal and ventral pathway, the distinct frequency bands of these inputs can be leveraged to preserve the identity of the input source. In this way source-specific oscillatory activity in primate cortex can serve to establish and maintain “functionally labeled lines” for dynamically adjusting cortical information transfer and multiplexing converging sensory signals.

Background and objective Computer Aided Diagnosis (CAD) techniques have widely been used in research to detect the neurological abnormalities and improve the consistency of diagnosis and treatment in medicine. In this study, a new CAD system based on EEG signals was developed. The motivation for the development of the CAD system was to diagnose multiple sclerosis (MS) disease during covert visual attention tasks. It is worth noting that research of this kind on the efficacy of attention tasks is limited in scope for MS patients; therefore, it is vital to develop a feature of EEG to characterize the patient's state with high sensitivity and specificity. Methods We evaluated the use of phase–amplitude coupling (PAC) of EEG signals to diagnose MS. It is assumed that the role of PAC for information encoding during visual attention in MS is greatly unknown; therefore, we made an attempt to investigate it via CAD systems. The EEG signals were recorded from healthy and MS patients while performing new visual attention tasks. Machine learning algorithms were also used to identify the EEG signals as to whether the disease existed or not. The challenge regarding the dimensionality of the extracted features was addressed through selecting the relevant and efficient features using T-test and Bhattacharyya distance criteria, and the validity of the system was assessed through leave-one-subject-out cross-validation method. Results Our findings indicated that online sequential extreme learning machine (OS-ELM) classifier with T-test feature selection method yielded peak accuracy, sensitivity and specificity in both color and direction tasks. These values were 91%, 83% and 96% for color task, and 90%, 82% and 96% for the direction task. Conclusions Based on the results, it can be concluded that this procedure can be used for the automatic diagnosis of early MS, and can also facilitate the treatment assessment in patients.

Brain micro-electrical stimulation and its applications are among the most important issues in the field of brain science and neurophysiology. Deep brain stimulation techniques have been used in different theraputic or alternative medicine applications including chronic pain control, tremor control, Parkinson’s disease control and depression control. Recently, brain electrical stimulation has been used for tele-control and navigation of small animals such as rodents and birds. Electrical stimulation of the medial forebrain bundle (MFB) area has been reported to induce a pleasure sensation in rat which can be used as a virtual reward for rat navigation. In all cases of electrical stimulation, the temporal adaptation may deteriorate the instantaneous effects of the stimulation. Here, we study the adaptation effects of the MFB electrical stimulation in rats. The animals are taught to press a key in an operant conditioning chamber to self-stimulate the MFB region and receive a virtual reward for each key press. Based on the number of key presses, and statistical analyses the effects of adaptation on MFB stimulation is evaluated. The stimulation frequency were changed from 100 to 400 Hz, the amplitude were changed from 50 to 170 µA and the pulse-width were changed from 180 to 2000 µs. In the frequency of 250 Hz the adaptation effect were observed. The amplitude did not show a significant effect on MFB adaptation. For all values of pulse-widths, the adaptation occurred over two consecutive days, meaning that the number of key presses on the second day was less than the first day.

Deep Brain Stimulation (DBS) is a medical-practical method and has been applied to solve many medical complications. Animal usage as sensors and actuators, mind-controlled machines, and animal navigation are some of the non-medical DBS applications. One of the brain areas used in ratbot navigation is the Ventral Posteromedial Nucleus (VPM), which creates non-volunteer head rotation. Rat training by water/food restriction can be used to create forward movement. In this study, a combination of VPM stimulation and water/food restriction has been employed to establish a complete navigation system. Five rats responded to VPM stimulations. However, with three of them, rats rotated to the same direction after the stimulations of either VPM side of the brain. Two rats rotated bilaterally, proportionate to the VPM stimulation side. These two rats were trained in a T-shape maze and became ratbots. The results of the 3-session test showed that their navigation performances were 96% and 86%, respectively. These ratbots are suitable for navigational purposes and are ready to complete the missions that are dangerous or impossible for humans., Graphical Abstract

Local Field Potentials (LFPs) recorded from the primary motor cortex (M1) have been shown to be very informative for decoding movement parameters, and these signals can be used to decode forelimb kinematic and kinetic parameters accurately. Although locomotion is one of the most basic and important motor abilities of humans and animals, the potential of LFPs in decoding abstract hindlimb locomotor parameters has not been investigated. This study investigates the feasibility of decoding speed and slope of locomotion, as two important abstract parameters of walking, using the LFP signals.Rats were trained to walk smoothly on a treadmill with different speeds and slopes. The brain signals were recorded using the microwire arrays chronically implanted in the hindlimb area of M1 while rats walked on the treadmill. LFP channels were spatially filtered using optimal common spatial patterns to increase the discriminability of speeds and slopes of locomotion. Logarithmic wavelet band powers were extracted as basic features, and the best features were selected using the statistical dependency criterion before classification.Using 5 s LFP trials, the average classification accuracies of four different speeds and seven different slopes reached 90.8% and 86.82%, respectively. The high-frequency LFP band (250-500 Hz) was the most informative band about these parameters and contributed more than other frequency bands in the final decoder model.Our results show that the LFP signals in M1 accurately decode locomotion speed and slope, which can be considered as abstract walking parameters needed for designing long-term brain-computer interfaces for hindlimb locomotion control.

Artifact removal is a key step toward designing real-world and efficient brain computer interfaces. Here we describe an automatic blind source separation algorithm applicable to real-time signal processing. The algorithm combines the generalized eigenvalue decomposition technique with Laplacian filtering to separate desired and undesired subspaces, exclude artifact sources and recover artifact-free cortical signals. The algorithm outperforms commonly used artifact removal methods in brain computer interfaces as measured by cortical decoding performance.

We effortlessly perceive visual objects as unified entities, despite the preferential encoding of their various visual features in separate cortical areas. A ‘binding’ process is assumed to be required for creating this unified percept, but the underlying neural mechanism and specific brain areas are poorly understood. We investigated ‘feature-binding’ across two feature dimensions, using a novel stimulus configuration, designed to disambiguate whether a given combination of color and motion direction is perceived as bound or unbound. In the “bound” condition, two behaviorally relevant features (color and motion) belong to the same object, while in the “unbound” condition they belong to different objects. We recorded local field potentials from the lateral prefrontal cortex (lPFC) in macaque monkeys that actively monitored the different stimulus configurations. Our data show a neural representation of visual feature binding especially in the 4–12 Hz frequency band and a transmission of binding information between different lPFC neural subpopulations. This information is linked to the animal's reaction time, suggesting a behavioral relevance of the binding information. Together, our results document the involvement of the prefrontal cortex, targeted by the dorsal and ventral visual streams, in binding visual features from different dimensions, in a process that includes a dynamic modulation of low frequency inter-regional communication.

The Brain-Computer interface system provides a communication path among the brain and computer, and recently, it is the subject of increasing attention. One of the most common paradigms of BCI systems is motor imagery. Currently, to classify motor imagery EEG signals, Common Spatial Patterns (CSP) are extensively used. Generally, the recorded motor imagery EEG signals in BCI are noisy, non-stationary, thus significantly reducing the BCI system's performance. It is shown that the CSP algorithm has a good performance in the classification of various types of motor imagery data. However, once the number of trials is low, or the data are noisy, overfitting will probably occur, which precludes extracting an appropriate spatial filter. Another drawback of the CSP is that it only extracts spatial-based filters. Therefore, the current study attempts to decrease the probability of overfitting in the CSP algorithm by presenting an improved method called Ensemble Regularized Common Spatio-Spectral Pattern (Ensemble RCSSP). Compared with other CSP and improved versions of CSP algorithms, our proposed models indicate a better accuracy, robustness, and reliability for motor imagery EEG data. The performance of the proposed Ensemble RCSSP has been tested for BCI Competition IV, Dataset 1, and BCI Competition III, Dataset Iva. Compared with other methods, performance is improved, and on average, the accuracy for all subjects is reached to 82.64% and 86.91% for the first and second datasets, respectively.

Adaptation is an important mechanism that causes a decrease in the neural response both in terms of local field potentials (LFP) and spiking activity. We previously showed this reduction effect in the tuning curve of the primary auditory cortex. Moreover, we revealed that a repeated stimulus reduces the neural response in terms of spike-phase coupling (SPC). In the current study, we examined the effect of adaptation on the SPC tuning curve. To this end, employing the phase-locking value (PLV) method, we estimated the spike-LFP coupling. The data was obtained by a simultaneous recording from four single-electrodes in the primary auditory cortex of 15 rats. We first investigated whether the neural system may use spike-LFP phase coupling in the primary auditory cortex to encode sensory information. Secondly, we investigated the effect of adaptation on this potential SPC tuning. Our data showed that the coupling between spikes’ times and the LFP phase in beta oscillations represents sensory information (different stimulus frequencies), with an inverted bell-shaped tuning curve. Furthermore, we showed that adaptation to a specific frequency modulates SPC tuning curve of the adapter and its neighboring frequencies. These findings could be useful for interpretation of feature representation in terms of SPC and the underlying neural mechanism of adaptation.

The idea that a flexible behavior relies on synchronous neural activity within intra- and inter-associated cortical areas has been a matter of intense research in human and animal neuroscience. The neurophysiological mechanisms underlying this behavioral correlate of the synchronous activity are still unknown. It has been suggested that the strength of neural synchrony at the level of population is an important neural code to guide an efficient transformation of the sensory input into the behavioral action. In this study, we have examined the non-linear synchronization between neural ensembles in area MT of the macaque visual cortex by employing a non-linear cross-frequency coupling technique, namely bicoherence. We trained a macaque monkey to detect a brief change in the cued stimulus during a visuomotor detection task. The results show that the non-linear phase synchronization in the high-gamma frequency band (100–250 Hz) predicts the animal’s reaction time. The strength of non-linear phase synchronization is selective to the target stimulus location. In addition, the non-linearity characteristics of neural synchronization are selectively modulated when the monkey covertly attends to the stimulus inside the neuron’s receptive field. This additional evidence indicates that non-linear neuronal synchronization may be affected by a cognitive function like spatial attention. Our neural and behavioral observations reflect that two crucial processes may be involved in processing of visuomotor information in area MT: (I) a non-linear cortical process (measured by the bicoherence) and (II) a linear process (measured by the spectral power).

Eye movement studies are subject of interest in human cognition. Cortical activity and cognitive load impress eye movement influentially. Here, we investigated whether fluid intelligence (FI) has any effect on eye movement pattern in a comparative visual search (CVS) task. FI of individuals was measured using the Cattell test, and participants were divided into three groups: low FI, middle FI, and high FI. Eye movements of individuals were then recorded during the CVS task. Eye movement patterns were extracted and compared statistically among the three groups. Our experiment demonstrated that eye movement patterns were significantly different among the three groups. Pearson correlation coefficients between FI and eye movement parameters were also calculated to assess which of the eye movement parameters were most affected by FI. Our findings illustrate that saccade peak velocity had the greatest positive correlation with FI score and the ratio of total fixation duration to total saccade duration had the greatest negative correlation with FI. Next, we extracted 24 features from eye movement patterns and designed: (1) a classifier to categorize individuals and (2) a regression analysis to predict the FI score of individuals. In the best case examined, the classifier categorized subjects with 68.3% accuracy, and the regression predicted FI of individuals with a 0.54 correlation between observed FI and predicted FI. In our investigation, the results have emphasized that imposed loads on low FI individuals is greater than that of high FI individuals in the cognitive load tasks.

This paper proposes an innovative method for automatic detection of pulmonary nodules in Computed Tomography (CT) data and measurement of changes in the number and sizes of the detected nodules during the treatment session. In the presented method, two multi-slice CT images are first taken from the patient’s lung, each captured by a similar capturing device but at two different dates. The CT images are then analyzed and their pulmonary nodules are extracted using a novel framework based on Mathematical Morphology Filtering (MMF), Statistical Region Merging (SRM), and Support Vector Machines (SVM). The MMF step smoothes the image in order to increase its homogeneity as well as removing the noises and artifacts. The SRM algorithm segments each slice of the CT image. After connecting the boundaries of the segments in adjacent slices, three-dimensional objects are produced which are considered as nodule-candidates. These candidates are classified into nodules and non-nodules using a two-class SVM classifier. The extracted nodules in each image are then labeled and their characteristics (i.e. labels, locations, and sizes) are stored. Finally, after registering the image pair using an affine algorithm, the growth rates of the lung nodules are measured.

Implantable image sensors have several biomedical applications due to their miniature size, light weight, and low power consumption achieved through sub-micron standard CMOS (Complementary Metal Oxide Semiconductor) technologies. The main applications are in specific cell labeling, neural activity detection, and biomedical imaging. In this paper the recent research studies on implantable CMOS image sensors for neural activity monitoring of brain are being quantified and reviewed. Based on the results, the suitable implantable image sensors for brain neural monitoring should have high signal to noise ratio of above 60 dB, high dynamic range. of near 88 dB and low power consumption than the safety threshold of 4W/cm2. Moreover, it is found out that the next generation of implantable imaging device trend should reduce the pixel size and power consumption of CMOS image sensors to increase spatial resolution of sample images.

Introduction: Video games are common cultural issues with great influence in all societies. One of the important cognitive effects of video games is on creating stress on video players. The present research objective was to study different types of stress in players based on video game styles. Methods: A total of 80 players, aged 18 to 30 years, played four types of video games;.Runner game, Excitement game, Fear game, and Puzzle game. In the beginning, the players filled in the form of personal information as well as some general and specialized information on the games. Before starting each game, the saliva samples of the players were collected to measure their level of cortisol and α-amylase. At the end of each game, the same samples were collected again. The concentrations of cortisol and α-amylase were measured using a specialized kit and an ELISA device. In addition, the variations of brain waves were recorded by an Emotiv system. Finally, the data were analyzed in SPSS and Matlab system (after and before playing video game). Results: The research findings revealed that the salivary α-amylase concentration increased significantly after playing the Fear game, Runner game, and Excitement game and decreased significantly after playing the Puzzle game. Moreover, the concentration of salivary cortisol increased significantly after playing the Runner game, Excitement game, and Fear game and decreased significantly after playing the Puzzle game. The brain wave analysis also revealed that the level of stress experienced by playing Fear game was higher than the Excitement game. Conclusion: According to the research findings, video games can affect the stress system as well as the cognitive system of humans depending on the game style. In addition, the type and level of stress triggered in the players depend on the game style.

Cognitive dysfunction or physical impairment is the result of structural lesions in the brains of patients with Multiple Sclerosis (MS), which could impress the brain functional connectivity. Cognitive deficits are frequently found in the early phases of MS disease. The changes in brain functional connectivity associated with cognitive tasks can be detected through blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI). In the present study, we evaluated a set of task-related fMRI data in combination with graph theory analysis. The modified Paced Auditory Serial Addition Task (PASAT) was presented to the subjects in an fMRI study in a 3.0 T MRI scanner. Graph theoretical methods allow us to model the brain networks for the identification of functional connectivity patterns in various conditions and to assess the topological properties of brain networks. The adjacency matrices constructed by proportional thresholding of the Pearson correlation-based connectivity networks were studied in patients with relapsing-remitting MS (RRMS) in the early stages and matched healthy controls (HC) through computing the different types of global and regional graph measures. We compared the extracted graph properties to investigate significant cognitive-related alterations in network characteristics between the early MS patients and the controls. We observed a link between functional modularity and clustering with cognition in task-based brain state. We also detected sets of informative brain areas involved in cognitive dysfunction that could distinguish MS patients from the healthy controls in most of local graph measures. It seems that the regions of superior temporo-polar gyrus, right putamen, fusiform gyrus, and some parts of limbic system such as hippocampus, parahippocampal gyri, and amygdala are the brain areas which are affected by cognitive impairment in early phases of MS disease. Our findings demonstrated the potential of applying graph analysis on task-related fMRI data to reflect the cognitive disorders in the early stages of MS.