Coupled oscillations orchestrate selective information transmission in visual cortex.

Performing visually guided behavior involves flexible routing of sensory information towards associative areas. We hypothesize that in visual cortical areas, this routing is shaped by a gating influence of the local neuronal population on the activity of the same population's single neurons. We analyzed beta frequencies (representing local population activity), high-gamma frequencies (representative of the activity of local clusters of neurons), and the firing of single neurons in the medial temporal (MT) area of behaving rhesus monkeys. Our results show an influence of beta activity on single neurons, predictive of behavioral performance. Similarly, the temporal dependence of high-gamma on beta predicts behavioral performance. These demonstrate a unidirectional influence of network-level neural dynamics on single-neuron activity, preferentially routing relevant information. This demonstration of a local top-down influence unveils a previously unexplored perspective onto a core feature of cortical information processing: the selective transmission of sensory information to downstream areas based on behavioral relevance.

Delta-alpha/beta coupling as a signature of visual working memory in the prefrontal cortex.

Executive functions, particularly visual working memory, depend on the prefrontal cortex (PFC). Phase-amplitude coupling (PAC) has been proposed as a measure of synchronized brain oscillations. To study the neural correlates of working memory in cross-frequency interactions, local field potential (LFP) recordings were made in the PFC of two macaque monkeys. PAC analysis revealed that the delta band (1-5 Hz) phase modulated the alpha-beta band (8-33 Hz) amplitude throughout task epochs, in both the pre- and post-training stages. The elevation of δ-αß PAC in the fixation period during post-training was a signature of task learning. Interestingly, the δ-αß PAC was not enhanced in error trials compared to correct trials, and the subject's performance was strictly dependent on the orchestration of the delta phase. Furthermore, contrary to the dorsoventral functional specialization of PFC, spatial and shape stimuli induced the same pattern of PAC in PFC subdivisions.

Working memory expedites the processing of visual signals within the extrastriate cortex.

Working memory is the ability to maintain information in the absence of sensory input. In this study, we investigated how working memory benefits processing in visual areas. Using a measure of phase consistency to detect the arrival time of visual signals to the middle temporal (MT) area, we assessed the impact of working memory on the speed of sensory processing. We recorded from MT neurons in two monkeys during a spatial working memory task with visual probes. When the memorized location closely matches the receptive field center of the recording site, visual input arrives sooner, but if the memorized location does not match the receptive field center then the arrival of visual information is delayed. Thus, working memory expedites the arrival of visual input in MT. These results reveal that even in the absence of firing rate changes, working memory can still benefit the processing of information within sensory areas.

Prediction of behavioral performance by alpha-band phase synchronization in working memory.

Working memory (WM) is a cognitive system with limited capacity that can temporarily store and process information. The purpose of this study was to investigate functional connectivity based on phase synchronization during WM and its relationship with the behavioral response. In this regard, we recorded EEG/Eye tracking data of seventeen healthy subjects while performing a memory-guided saccade (MGS) task with two different positions (near eccentricity and far eccentricity). We computed saccade error as memory performance and measured functional connectivity using Phase Locking Value (PLV) in the alpha frequency band (8-12 Hz). The results showed that PLV is negatively correlated with saccade error. Our finding indicated that during the maintenance period, PLV between the frontal and visual area in trials with low saccade error increased significantly compared to trials with high saccade error. Furthermore, we observed a significant difference between PLV for near and far conditions in the delay period. The results suggest that PLV in memory maintenance, in addition to predicting saccade error as behavioral performance, can be related to the coding of spatial information in WM.

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal aging in the brain is associated with progressive slowdown and disruptions in various abilities such as motor ability, cognitive impairment, decreasing information processing speed, attention, and memory. With the aggravation of global aging, more research focuses on brain changes in the elderly adult. The graph theory, in combination with functional magnetic resonance imaging (fMRI), makes it possible to evaluate the brain network functional connectivity patterns in different conditions with brain modeling. We have evaluated the brain network communication model changes in three different age groups (including 8 to 15 years, 25 to 35 years, and 45 to 75 years) in lifespan pilot data from the human connectome project (HCP). Initially, Pearson correlation-based connectivity networks were calculated and thresholded. Then, network characteristics were compared between the three age groups by calculating the global and local graph measures. In the resting state brain network, we observed decreasing global efficiency and increasing transitivity with age. Also, brain regions, including the amygdala, putamen, hippocampus, precuneus, inferior temporal gyrus, anterior cingulate gyrus, and middle temporal gyrus, were selected as the most affected brain areas with age through statistical tests and machine learning methods. Using feature selection methods, including Fisher score and Kruskal-Wallis, we were able to classify three age groups using SVM, KNN, and decision-tree classifier. The best classification accuracy is in the combination of Fisher score and decision tree classifier obtained, which was 82.2%. Thus, by examining the measures of functional connectivity using graph theory, we will be able to explore normal age-related changes in the human brain, which can be used as a tool to monitor health with age.

EEG-based identification system using deep neural networks with frequency features.

Improving system security can be achieved through people identification. Among various methods, electroencephalography-based (EEG-based) identification is a dependable way to prevent identity theft and impersonation. Due to the distractions present in the identification environment, such as lack of focus, mental engagement, small body movements, blinking, and other noises, it is essential to analyze data that reflects these conditions. The present research aims to advance practical EEG-based identification by studying data with mental preoccupation and developing a suitable algorithm. In this article, data from a study conducted on a group of 109 individuals has been analyzed. The data is categorized into two groups: focused data and waiting data. The article describes preprocessing the data and extracting three types of features, including Statistical, Frequency, and Wavelet. Then, a deep neural network (DNN) is used to classify the data. The DNN utilizes a multilayer, fully-connected neural network, with the number of layers and neurons varying based on the data type. Optimization and regularization methods are employed to improve the accuracy of the results. The DNN achieved an average accuracy of 99.19% for frequency features over all subjects in the focused data category, while the waiting data category showed an accuracy of 97.81%.

Perceived Stress and Cfognition Function Quantification in a Scary Video Game: An Electroencephalogram Features and Biochemical Measures.

Introduction: Video games affect the stress system and cognitive abilities in different ways. Here, we evaluated electrophysiological and biochemical indicators of stress and assessed their effects on cognition and behavioral indexes after playing a scary video game. Methods: Thirty volunteers were recruited into two groups as control and experimental. The saliva and blood samples were collected before and after intervention (watching/playing the scary game for control and experimental groups respectively). To measure cortisol and salivary alpha-amylase (sAA) levels, oxytocin (OT), and brain-derived neurotrophic factor (BDNF) plasma levels, dedicated ELISA kits were used. Electroencephalography recording was done before and after interventions for electroencephalogram (EEG)-based emotion and stress recognition. Then, the feature extraction (for mental stress, arousal, and valence) was done. Matrix laboratory (MATLAB) software, version 7.0.1 was used for processing EEG-acquired data. The repeated measures were applied to determine the intragroup significance level of difference. Results: Scary gameplay increases mental stress (P<0.001) and arousal (P<0.001) features and decreases the valence (P<0.001) one. The salivary cortisol and alpha-amylase levels were significantly higher after the gameplay (P<0.001 for both). OT and BDNF plasma levels decreased after playing the scary game (P<0.05 for both). Conclusion: We conclude that perceived stress considerably elevates among players of scary video games, which adversely affects the emotional and cognitive capabilities, possibly via the strength of synaptic connections, and dendritic thorn construction of the brain neurons among players. Highlights: The mental stress level increases in players of scary video games.The salivary cortisol and alpha-amylase levels are significantly higher after the scary gameplay.Plasma levels of oxytocin and brain-derived neurotrophic factor decrease after the scary gameplay.The arousal and valence features increase in players of scary video game.Cognitive capabilities are adversely affected by the scary gameplay. Plain Language Summary: Nowadays, video games have become an important part of human life at different ages. Therefore, assessing their effects (improving and/or damaging) on cognition and behavior is important for understanding how they affect the nervous system. The results of such studies can be used to design a variety of games in the future in a way that minimizes the harmful side effects of video games on human cognitive functions and maximizes their beneficial effects.

Early MS Identification Using Non-linear Functional Connectivity and Graph-theoretic Measures of Cognitive Task-fMRI Data.

Introduction: Functional neuroimaging has developed a fundamental ground for understanding the physical basis of the brain. Recent studies have extracted invaluable information from the underlying substrate of the brain. However, cognitive deficiency has insufficiently been assessed by researchers in multiple sclerosis (MS). Therefore, extracting the brain network differences among relapsing-remitting MS (RRMS) patients and healthy controls as biomarkers of cognitive task functional magnetic resonance imaging (fMRI) data and evaluating such biomarkers using machine learning were the aims of this study. Methods: In order to activate cognitive functions of the brain, blood-oxygen-level-dependent (BOLD) data were collected throughout the application of a cognitive task. Accordingly, a nonlinear-based brain network was established using kernel mutual information based on the automated anatomical labeling atlas (AAL). Subsequently, a statistical test was carried out to determine the variation in brain network measures between the two groups on binary adjacency matrices. We also found the prominent graph features by merging the Wilcoxon rank-sum test with the Fisher score as a hybrid feature selection method. Results: The results of the classification performance measures showed that the construction of a brain network using a new nonlinear connectivity measure in task-fMRI performs better than the linear connectivity measures in terms of classification. The Wilcoxon rank-sum test also demonstrated a superior result for clinical applications. Conclusion: We believe that non-linear connectivity measures, like KMI, outperform linear connectivity measures, like correlation coefficient in finding the biomarkers of MS disease according to classification performance metrics. Highlights: The performance of some brain regions (the hippocampus, parahippocampus, cuneus, pallidum, and two segments of the cerebellum) is different between healthy and MS people.Non-linear connectivity measures, such as Kernel mutual information, perform better than linear connectivity measures, such as correlation coefficient, in finding the biomarkers of MS disease. Plain Language Summary: Multiple sclerosis (MS) can disrupt the function of the central nervous system. The function of brain network is impaired in these patients. In this study, we evaluated the change in brain network based on a non-linear connectivity measure using cognitive task-based fMRI data between MS patients and healthy controls. We used Kernel mutual information (KMI) and designed a graph network based on the results of connectivity analysis. The the paced auditory serial addition test was used to activate cognitive functions of the brain. The classification was employed for the results using different decision tree -based technique and support vector machine. KMI can be considered a valid measure of connectivity over linear measures, like the correlation coefficient. KMI does not have the drawbacks of mutual information technique. However, further studies should be implemented on brain data of MS patients to draw more definite conclusions.