Cognitive Brain States Research Articles

Unsupervised Joint Domain Adaptation for Decoding Brain Cognitive States From tfMRI Images.

Recent advances in large model and neuroscience have enabled exploration of the mechanism of brain activity by using neuroimaging data. Brain decoding is one of the most promising researches to further understand the human cognitive function. However, current methods excessively depends on high-quality labeled data, which brings enormous expense of collection and annotation of neural images by experts. Besides, the performance of cross-individual decoding suffers from inconsistency in data distribution caused by individual variation and different collection equipments. To address mentioned above issues, a Join Domain Adapative Decoding (JDAD) framework is proposed for unsupervised decoding specific brain cognitive state related to behavioral task. Based on the volumetric feature extraction from task-based functional Magnetic Resonance Imaging (tfMRI) data, a novel objective loss function is designed by the combination of joint distribution regularizer, which aims to restrict the distance of both the conditional and marginal probability distribution of labeled and unlabeled samples. Experimental results on the public Human Connectome Project (HCP) S1200 dataset show that JDAD achieves superior performance than other prevalent methods, especially for fine-grained task with 11.5%-21.6% improvements of decoding accuracy. The learned 3D features are visualized by Grad-CAM to build a combination with brain functional regions, which provides a novel path to learn the function of brain cortex regions related to specific cognitive task in group level.

Alpha/beta oscillations reveal cognitive and affective brain states associated with role taking in a dyadic cooperative game.

Social cooperation often requires taking different roles in order to reach a shared goal. By defining individual tasks, these roles dictate processing demands of the collaborators. The main aim of the present study was to examine the hypothesis that induced alpha and lower beta oscillations provide insights into affective and cognitive brain states during social cooperation. Toward this end, an experimental game was used in which participants had to navigate a Pacman figure through a maze by sending and receiving information about the correct moving direction. Supporting our hypotheses, individual roles taken by the collaborators during gameplay were associated with significant changes in alpha and lower beta power. Furthermore, effects were similar when participants played the Pacman Game with human or computer partners. Findings are discussed from the perspective of the information-via-desynchronization hypothesis proposing that alpha and lower beta power decreases reflect states of enhanced cortical information representation. Overall, experimental games are a useful tool for extending basic research on brain oscillations to the domain of naturalistic social interaction as emphasized by the second-person neuroscience perspective.

High Variability Periods in the EEG Distinguish Cognitive Brain States.

To describe a novel measure of EEG signal variability that distinguishes cognitive brain states. We describe a novel characterization of amplitude variability in the EEG signal termed "High Variability Periods" or "HVPs", defined as segments when the standard deviation of a moving window is continuously higher than the quartile cutoff. We characterize the parameter space of the metric in terms of window size, overlap, and threshold to suggest ideal parameter choice and compare its performance as a discriminator of brain state to alternate single channel measures of variability such as entropy, complexity, harmonic regression fit, and spectral measures. We show that the average HVP duration provides a substantially distinct view of the signal relative to alternate metrics of variability and, when used in combination with these metrics, significantly enhances the ability to predict whether an individual has their eyes open or closed and is performing a working memory and Raven's pattern completion task. In addition, HVPs disappear under anesthesia and do not reappear in early periods of recovery. HVP metrics enhance the discrimination of various brain states and are fast to estimate. HVP metrics can provide an additional view of signal variability that has potential clinical application in the rapid discrimination of brain states.

Inferring Cognitive State of Pilot’s Brain Under Different Maneuvers During Flight

This work designs an adversarial Bayesian deep network to solve the cognitive detection of pilot fatigue. Batch normalization and data enhancement are adopted in the posterior inference of the proposed model parameters to effectively improve the generalization of neural networks. The generator is used to enhance the brain power map generated from three cognitive indicators and improve the accuracy of fatigue state recognition. This work also adds adversarial noise in the vicinity of each brain electrode to form an adversarial image, which further reveals the correlation between the cognitive state of brain and the location of brain regions. Compared with other deep models and parameter optimization methods, our model achieves better detection accuracy.

Individual functional parcellation revealed compensation of dynamic limbic network organization in healthy ageing.

Using group-level functional parcellations and constant-length sliding window analysis, dynamic functional connectivity studies have revealed network-specific impairment and compensation in healthy ageing. However, functional parcellation and dynamic time windows vary across individuals; individual-level ageing-related brain dynamics are uncertain. Here, we performed individual parcellation and individual-length sliding window clustering to characterize ageing-related dynamic network changes. Healthy participants (n= 637, 18-88 years) from the Cambridge Centre for Ageing and Neuroscience dataset were included. An individual seven-network parcellation, varied from group-level parcellation, was mapped for each participant. For each network, strong and weak cognitive brain states were revealed by individual-length sliding window clustering and canonical correlation analysis. The results showed negative linear correlations between age and change ratios of sizes in the default mode, frontoparietal, and salience networks and a positive linear correlation between age and change ratios of size in the limbic network (LN). With increasing age, the occurrence and dwell time of strong states showed inverted U-shaped patterns or a linear decreasing pattern in most networks but showed a linear increasing pattern in the LN. Overall, this study reveals a compensative increase in emotional networks (i.e., the LN) and a decline in cognitive and primary sensory networks in healthy ageing. These findings may provide insights into network-specific and individual-level targeting during neuromodulation in ageing and ageing-related diseases.

EEG-FCV: An EEG-Based Functional Connectivity Visualization Framework for Cognitive State Evaluation

Electroencephalogram (EEG)-based tools for brain functional connectivity (FC) analysis and visualization play an important role in evaluating brain cognitive function. However, existing similar FC analysis tools are not only visualized in 2 dimensions (2D) but also are highly prone to cause visual clutter and unable to dynamically reflect brain connectivity changes over time. Therefore, we design and implement an EEG-based FC visualization framework in this study, named EEG-FCV, for brain cognitive state evaluation. EEG-FCV is composed of three parts: the Data Processing module, Connectivity Analysis module, and Visualization module. Specially, FC is visualized in 3 dimensions (3D) by introducing three existing metrics: Pearson Correlation Coefficient (PCC), Coherence, and PLV. Furthermore, a novel metric named Comprehensive is proposed to solve the problem of visual clutter. EEG-FCV can also visualize dynamically brain FC changes over time. Experimental results on two available datasets show that EEG-FCV has not only results consistent with existing related studies on brain FC but also can reflect dynamically brain FC changes over time. We believe EEG-FCV could prompt further progress in brain cognitive function evaluation.

Machine learning evaluates changes in functional connectivity under a prolonged cognitive load.

One must be aware of the black-box problem by applying machine learning models to analyze high-dimensional neuroimaging data. It is due to a lack of understanding of the internal algorithms or the input features upon which most models make decisions despite outstanding performance in classification, pattern recognition, and prediction. Here, we approach the fundamentally high-dimensional problem of classifying cognitive brain states based on functional connectivity by selecting and interpreting the most relevant input features. Specifically, we consider the alterations in the cortical synchrony under a prolonged cognitive load. Our study highlights the advances of this machine learning method in building a robust classification model and percept-related prestimulus connectivity changes over the conventional trial-averaged statistical analysis.

Complex Network Analysis of Experimental EEG Signals for Decoding Brain Cognitive State

Depicting the relationship between brain cognitive state and task difficulty level constitutes a challenging problem of significant importance. In order to probe it, we design an experiment to gather EEG data from mental arithmetic task under different difficulty levels. We construct brain complex networks using a complex network method and information entropy theory. We then employ weighted clustering coefficient to characterize the networks generated from different brain cognitive states. The results show that with the increase in task difficulty level, the mean weighted clustering coefficients show a decrease. This is due to the lack of coordination of brain activity and the low efficiency of the network organization caused by the increase in task difficulty. In addition, we calculate the permutation entropy from the signals of each channel EEG signals to support the findings from our network analysis. These findings render our method particularly useful for depicting the relationship between brain cognitive state and difficulty level.

The expression of established cognitive brain states stabilizes with working memory development.

We present results from a longitudinal study conducted over 10 years in a sample of 126 8-33 year olds demonstrating that adolescent development of working memory is supported by decreased variability in the amplitude of expression of whole brain states of task-related activity. fMRI analyses reveal that putative gain signals affecting maintenance and retrieval aspects of working memory processing stabilize during adolescence, while those affecting sensorimotor processes do not. We show that trial-to-trial variability in the reaction time and accuracy of eye-movements during a memory guided saccade task are related to fluctuations in the amplitude of expression of task-related brain states, or brain state variability, and also provide evidence that individual developmental trajectories of reaction time variability are related to individual trajectories of brain state variability. These observations demonstrate that the stabilization of widespread gain signals affecting already available cognitive processes underlies the maturation of cognition during adolescence.

Analysis of Brain Cognitive State for Arithmetic Task and Motor Task Using Electroencephalography Signal

Analysis of Brain Cognitive State for Arithmetic Task and Motor Task Using Electroencephalography Signal

Neuropeptide alterations in the tree shrew hypothalamus during volatile anesthesia

Neuropeptides are critical signaling molecules, involved in the regulation of diverse physiological processes including energy metabolism, pain perception and brain cognitive state. Prolonged general anesthesia has an impact on many of these processes, but the regulation of peptides by general anesthetics is poorly understood. In this study, we present an in-depth characterization of the hypothalamic neuropeptides of the tree shrew during volatile isoflurane/nitrous oxide anesthesia administered accompanying a neurosurgical procedure. Using a predicted-peptide database and hybrid spectral analysis, we first identified 85 peptides from the tree shrew hypothalamus. Differential analysis was then performed between control and experimental group animals. The levels of 12 hypothalamic peptides were up-regulated following prolonged general anesthesia. Our study revealed for the first time that several neuropeptides, including alpha-neoendorphin and somatostatin-14, were altered during general anesthesia. Our study broadens the scope for the involvement of neuropeptides in volatile anesthesia regulation, opening the possibility for investigating the associated regulatory mechanisms.

Decoding brain states using backward edge elimination and graph kernels in fMRI connectivity networks

In the current study, we present a new approach for decoding brain states based on the connectivity graphs extracted from functional magnetic resonance imaging (fMRI) data. fMRI connectivity graphs are constructed in different brain states and fed into an iterative support vector classifier that is enriched by shortest-path kernel. The classifier prunes the graphs of insignificant edges via a backward edge elimination procedure. The iteration in which maximum classification performance occurs is considered as optimum iteration. The edges and nodes that survive in the optimum iteration form discriminant networks between states. We apply “one-versus-one” approach to extend the proposed method into a multi-class classifier. This classifier is used to distinguish between five cognitive brain states from a blocked design fMRI data: (1) fixation, (2) detection of a single stimulus, (3) perceptual matching, (4) attentional cueing, and (5) delayed match-to-sample. The proposed method results in multi-class classification accuracy of 86.32%. Posterior cingulate cortex is identified as a hub in the networks that separate fixation from tasks. Superior parietal lob has the same role to distinguish between different tasks. Connectivity between right retrosplential and superior parietal lobe contributes to discrimination in the fixation-task and task-task cases.

Auditory perception and syntactic cognition: brain activity‐based decoding within and across subjects

The present magnetoencephalography study investigated whether the brain states of early syntactic and auditory-perceptual processes can be decoded from single-trial recordings with a multivariate pattern classification approach. In particular, it was investigated whether the early neural activation patterns in response to rule violations in basic auditory perception and in high cognitive processes (syntax) reflect a functional organization that largely generalizes across individuals or is subject-specific. On this account, subjects were auditorily presented with correct sentences, syntactically incorrect sentences, correct sentences including an interaural time difference change, and sentences containing both violations. For the analysis, brain state decoding was carried out within and across subjects with three pairwise classifications. Neural patterns elicited by each of the violation sentences were separately classified with the patterns elicited by the correct sentences. The results revealed the highest decoding accuracies over temporal cortex areas for all three classification types. Importantly, both the magnitude and the spatial distribution of decoding accuracies for the early neural patterns were very similar for within-subject and across-subject decoding. At the same time, across-subject decoding suggested a hemispheric bias, with the most consistent patterns in the left hemisphere. Thus, the present data show that not only auditory-perceptual processing brain states but also cognitive brain states of syntactic rule processing can be decoded from single-trial brain activations. Moreover, the findings indicate that the neural patterns in response to syntactic cognition and auditory perception reflect a functional organization that is highly consistent across individuals.

Oscillatory brain activity during sensory and cognitive processing in elderly

Event Abstract Back to Event Oscillatory brain activity during sensory and cognitive processing in elderly Christina Schmiedt-Fehr1*, Joscha T. Schmiedt1 and C. Basar-Eroglu1 1 Institute of Psychology and Cognition Research, University of Bremen, Germany Understanding the physiological and behavioural changes associated with the aging brain has taken on increasing importance as a growing proportion of society is living beyond age 60. Changes in ongoing EEG are suggested to reflect the underlying functionality and health of the central nervous system. Alterations in the event-related potentials (ERP) and oscillatory activity are used to study the functionality of sensory and cognition related processing systems. So far, we focused on age-related changes in alpha oscillatory networks to study sensory processing. The findings indicate an age-related change in scalp distribution, specifically, an increase in frontal alpha activity for visual and auditory stimulation. These changes may be related to higher cognitive processes and global modality-independent changes in the functional involvement of the alpha system for frontal regulation of cognitive brain states. Observations of a shift the ERP component P3 to anterior locations as individuals get older, support our assumption. Functional significance of these findings could be that older subjects show increased difficulty to create templates of the targets in working memory, or to inhibit orienting to stimuli that should no longer be experienced as new. To investigate the latter hypotheses, we applied a visual oddball task and analysed related delta (0.5-3 Hz), theta (4-7 Hz), alpha (8-13 Hz), and gamma (28-48 Hz) oscillatory activity, across early versus late blocks of trials in healthy young (n=20, 25±4 years) and older individuals (n=20, 61±5 years), during the processing of target and standard stimuli. We used checker boards as stimuli. Target detection was indicated by button press; targets were presented on 25% of the trials. Maximum amplitude values derived from FFT filtered and subsequently averaged data provided the database for the computation of amplitude and distribution differences between age-groups and during early and late blocks of trials. Analysis of variance was employed in data analyses. Age comparisons revealed that, also during cognitive processing there appears to be a shift of oscillatory activity to anterior locations in older individuals. Whereby, this was not observed for alpha, but for frontal delta responses, only during standard stimulus processing at the beginning of the experiment. Differences in alpha responses were also found; during target processing older compared to younger individuals showed a frontal alpha response with earlier onset, but shorter duration. During both, standards and targets, younger compared to older participants showed larger amplitudes and prolonged alpha responses at posterior locations. Latency differences were found for delta responses; during targets and standards older individuals showed a latency delay of the delta responses. Furthermore, overall delta and anterior theta amplitudes were reduced in older compared to younger during target processing. The findings are partly congruent with previous studies, showing a shift of oscillatory networks to anterior locations in older individuals. The results support the hypotheses that with increasing age individuals show increased difficulty to create templates of the targets in working memory, and to inhibit orienting to stimuli that should no longer be experienced as new.

Magnetic resonance imaging mapping of brain function. Human visual cortex.

Magnetic resonance imaging (MRI) studies of human brain activity are described. Task-induced changes in brain cognitive state were measured using high-speed MRI techniques sensitive to changes in cerebral blood volume (CBV), blood flow (CBF), and blood oxygenation. These techniques were used to generate the first functional MRI maps of human task activation, by using a visual stimulus paradigm. The methodology of MRI brain mapping and results from the investigation of the functional organization and frequency response of human primary visual cortex (V1) are presented.