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.

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.

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.

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.

Identification of cognitive load-dependent activation patterns using working memory task-based fMRI at various levels of difficulty.

Working memory, which is regarded as the foundation of cognitive processes, is a system that stores, processes, and manipulates information in short intervals of time that are actually needed for daily functioning. This study aimed to assess the brain activity of healthy controls (HC) while performing the N-back task, which is one of the most popularly used tests for evaluating working memory along with functional magnetic resonance imaging (fMRI). In this regard, we collected fMRI data from right-handed individuals in a 3.0 T scanner during the Persian version of the visual variant N-back task performance with three levels of complexity varied throughout the experiment (1, 2, and 3-back conditions) to increase the cognitive demands. The statistical parametric mapping (SPM12) software was used to analyze fMRI data for the identification of cognitive load-dependent activation patterns based on contrast images obtained from different levels of task difficulty. Our findings showed that as cognitive complexity increased, the number of significant activation clusters and cluster extent increased in several areas distributed in the cerebellum, frontoparietal lobes, insula, SMA, and lenticular nucleus, the majority of which are recognized for their role in working memory. Furthermore, deactivation patterns during 1-, 2-, and 3-back vs. 0-back contrasts revealed significant clusters in brain regions that are mostly described as being part of the default mode network (DMN). Based on previous research, our results supported the recognized involvement of the mentioned cortical and subcortical areas in various types or levels of N-back tasks. This study found that altering activation patterns by increasing task difficulty could aid in evaluating the various stages of cognitive dysfunction in many brain diseases such as multiple sclerosis (MS) and Alzheimer's disease by comparing controls in future studies to apply early appropriate treatment strategies.

Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement.

Area 2 of the primary somatosensory cortex (S1), encodes proprioceptive information of limbs. Several studies investigated the encoding of movement parameters in this area. However, the single-trial decoding of these parameters, which can provide additional knowledge about the amount of information available in sub-regions of this area about instantaneous limb movement, has not been well investigated. We decoded kinematic and kinetic parameters of active and passive hand movement during center-out task using conventional and state-based decoders. Our results show that this area can be used to accurately decode position, velocity, force, moment, and joint angles of hand. Kinematics had higher accuracies compared to kinetics and active trials were decoded more accurately than passive trials. Although the state-based decoder outperformed the conventional decoder in the active task, it was the opposite in the passive task. These results can be used in intracortical micro-stimulation procedures to provide proprioceptive feedback to BCI subjects.

Graph-based analysis of EEG for schizotypy classification applying flicker Ganzfeld stimulation.

Ganzfeld conditions induce alterations in brain function and pseudo-hallucinatory experiences, particularly in people with high positive schizotypy. The current study uses graph-based parameters to investigate and classify brain networks under Ganzfeld conditions as a function of positive schizotypy. Participants from the general population (14 high schizotypy (HS), 29 low schizotypy (LS)) had an electroencephalography assessment during Ganzfeld conditions, with varying visual activation (8 frequencies of random light flicker) and soundscape-induced mood (neutral, serenity, and anxiety). Weighted functional networks were computed in six frequency sub-bands (delta, theta, alpha-low, alpha-high, beta, and gamma) as a function of light-flicker frequency and mood. The brain network was analyzed using graph theory parameters, including clustering coefficient (CC), strength, and global efficiency (GE). It was found that the LS groups had higher CC and strength than the HS groups, especially in bilateral temporal and frontotemporal brain regions. Moreover, some decreases in CC and strength measures were found in LS groups among occipital and parieto-occipital brain regions. LS groups also had significantly higher GE in all Ganzfeld conditions compared to the HS groups. The random under-sampling boosting (RUSBoost) algorithm achieved the best classification performance with an accuracy of 95.34%, specificity of 96.55%, and sensitivity of 92.85% during an anxiety-induction Ganzfeld condition. This is the first exploration of the relationship between brain functional state changes under Ganzfeld conditions in individuals who vary in positive schizotypy. The accuracy of graph-based parameters in classifying brain states as a function of schizotypy is shown, particularly for brain activity during anxiety induction, and should be investigated in psychosis.

Selective Modulation of Hippocampal Theta Oscillations in Response to Morphine versus Natural Reward.

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.

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.

Hybrid fuzzy deep neural network toward temporal-spatial-frequency features learning of motor imagery signals.

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.

Frequency modulation of cortical rhythmicity governs behavioral variability, excitability and synchrony of neurons in the visual cortex.

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.

Tensor factorization approach for ERP-based assessment of schizotypy in a novel auditory oddball task on perceived family stress.

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.

Dynamic theta-modulated high frequency oscillations in rat medial prefrontal cortex during spatial working memory task.

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.

Decoding locomotion speed and slope from local field potentials of rat motor cortex.

BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Classification of Low and High Schizotypy Levels via Evaluation of Brain Connectivity.

Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders.

Sensory representation of visual stimuli in the coupling of low-frequency phase to spike times.

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.