Cognitive Neurodynamics最新文献

Neurofeedback, when combined with cognitive reappraisal, offers promising potential for emotion regulation training. However, prior studies have predominantly relied on functional magnetic resonance imaging, which could impede its clinical feasibility. Furthermore, these studies have primarily focused on reducing negative emotions while overlooking the importance of enhancing positive emotions. In our current study, we developed a novel electroencephalogram (EEG) neurofeedback-guided cognitive reappraisal training protocol for emotion regulation. We recruited forty-two healthy subjects (20 females; 22.4 ± 2.2 years old) who were randomly assigned to either the neurofeedback group or the control group. We evaluated the efficacy of this newly proposed neurofeedback training approach in regulating emotions evoked by pictures with different valence levels (low positive and high negative). Initially, we trained an EEG-based emotion decoding model for each individual using offline data. During the training process, we calculated the subjects’ real-time self-regulation performance based on the decoded emotional states and fed it back to the subjects as feedback signals. Our results indicate that the proposed decoded EEG neurofeedback-guided cognitive reappraisal training protocol significantly enhanced emotion regulation performance for stimuli with low positive valence. Additionally, wavelet energy and differential entropy features in the high-frequency band played a crucial role in emotion classification and were associated with neural plasticity changes induced by emotion regulation. These findings validate the beneficial effects of the proposed EEG neurofeedback protocol and offer insights into the neural mechanisms underlying its training effects. This novel decoded neurofeedback training protocol presents a promising cost-effective and non-invasive treatment technique for emotion-related mental disorders.

Social behaviors such as cooperation are crucial for mammals. A deeper knowledge of the neuronal mechanisms underlying cooperation can be beneficial for people suffering from pathologies with impaired social behavior. Our aim was to study the brain activity when two animals synchronize their behavior to obtain a mutual reinforcement. In a previous work, we showed that the activity of the prelimbic cortex (PrL) was enhanced during cooperation in rats, especially in the ones leading most cooperative trials (leader rats). In this study, we investigated the specific cells in the PrL contributing to cooperative behaviors. To this end, we collected rats’ brains at key moments of the learning process to analyze the levels of c-FOS expression in the main cellular groups of the PrL. Leader rats showed increased c-FOS activity in cells expressing D1 receptors during cooperation. Besides, we analyzed the levels of anxiety, dominance, and locomotor behavior, finding that leader rats are in general less anxious and less dominant than followers. We also recorded local field potentials (LFPs) from the PrL, the nucleus accumbens septi (NAc), and the basolateral amygdala (BLA). A spectral analysis showed that delta activity in PrL and NAc increased when rats cooperated, while BLA activity in delta and theta bands decreased considerably during cooperation. The PrL and NAc also increased their connectivity in the high theta band during cooperation. Thus, the present work identifies the specific PrL cell types engaged in this behavior, as well as the way this information is propagated to selected downstream brain regions (BLA, NAc).

Currently, electroencephalogram (EEG)-based motor imagery (MI) signals have been received extensive attention, which can assist disabled subjects to control wheelchair, automatic driving and other activities. However, EEG signals are easily affected by some factors, such as muscle movements, wireless devices, power line, etc., resulting in the low signal-to-noise ratios and the worse recognition results on EEG decoding. Therefore, it is crucial to develop a stable model for decoding MI-EEG signals. To address this issue and further improve the decoding performance for MI tasks, a hybrid structure combining convolutional neural networks and bidirectional long short-term memory (BLSTM) model, namely CBLSTM, is developed in this study to handle the various EEG-based MI tasks. Besides, the attention mechanism (AM) model is further adopted to adaptively assign the weight of EEG vital features and enhance the expression which beneficial to classification for MI tasks. First of all, the spatial features and the time series features are extracted by CBLSTM from preprocessed MI-EEG data, respectively. Meanwhile, more effective features information can be mined by the AM model, and the softmax function is utilized to recognize intention categories. Ultimately, the numerical results illustrate that the model presented achieves an average accuracy of 98.40% on the public physioNet dataset and faster training process for decoding MI tasks, which is superior to some other advanced models. Ablation experiment performed also verifies the effectiveness and feasibility of the developed model. Moreover, the established network model provides a good basis for the application of brain-computer interface in rehabilitation medicine.

Through emotion recognition with EEG signals, brain responses can be analyzed to monitor and identify individual emotional states. The success of emotion recognition relies on comprehensive emotion information extracted from EEG signals and the constructed emotion identification model. In this work, we proposed an innovative approach, called spatial-spectral-temporal-based convolutional recurrent neural network (CRNN) with lightweight attention mechanism (SST-CRAM). Firstly, we combined power spectral density (PSD) with differential entropy (DE) features to construct four-dimensional (4D) EEG feature maps and obtain more spatial, spectral, and temporal information. Additional, with a spatial interpolation algorithm, the utilization of the obtained valuable information was enhanced. Next, the constructed 4D EEG feature map was input into the convolutional neural network (CNN) integrated with convolutional block attention module (CBAM) and efficient channel attention module (ECA-Net) for extracting spatial and spectral features. CNN was used to learn spatial and spectral information and CBAM was employed to prioritize global information and obtain detailed and accurate features. ECA-Net was also used to further highlight key brain regions and frequency bands. Finally, a bidirectional long short-term memory (LSTM) network was used to explore the temporal correlation of EEG feature maps for comprehensive feature extraction. To assess the performance of our model, we tested it on the publicly available DEAP dataset. Our model demonstrated excellent performance and achieved high accuracy (98.63% for arousal classification and 98.66% for valence classification). These results indicated that SST-CRAM could fully utilize spatial, spectral, and temporal information to improve the emotion recognition performance.

Depression is a mental disease involved in emotional and cognitive impairments. Neuroimaging studies have found abnormalities in the structure and functional network of brain for major depressive disorder (MDD).However, neural mechanism of the dynamic connectivity for emotional attention of MDD is currently insufficient. In this study, event-related potentials (ERP) and time-varying network were analyzed to investigate attention bias and corresponding neural mechanisms induced by emotional facial stimuli. In the ERP results, N100 components in MDD had shorter latencies and smaller amplitudes than those in healthy controls (HC) for sad and fear faces. The P200 amplitudes induced by sad faces in MDD were significantly higher than those induced by happy and fear faces in MDD, and those induced by sad faces in HC. It was indicated that MDD patients had attention bias towards sad faces. For the time-varying network analysis, adaptive directed transfer function was explored to construct dynamic network connectivity. MDD patients had stronger information outflow from the right frontal region and weaker information outflow from parieto-occipital regions for sad faces. In addition, the network properties of sad faces were significantly correlated with PHQ-9 scores for MDD group. These findings may provide further explanation for understanding the MDD’s neural mechanism of attention bias during facial emotional tasks.

Graph-theory-based topological impairment of the whole-brain network has been verified to be one of the characteristics of mild cognitive impairment (MCI). However, two major challenges impede the further understanding of topological features for the personalized functional connectivity network of early Parkinson’s disease (ePD) with MCI. The uncertain of characteristic frequency band reflecting the abnormality of ePD-MCI and the setting of fixed length of sliding window at a second level in the construction of conventional brain network both limit a deeper exploration of network characteristics for ePD-MCI. Thus, a convolutional neural network is constructed first and the gradient-weighted class activation mapping method is used to determine the characteristic frequency band of the ePD-MCI. It is found that 1–4 Hz is a characteristic frequency band for recognizing MCI in ePD. Then, we propose a microstate window construction method based on electroencephalography microstate sequences to build brain functional network. By exploring the graph-theory-based topological features and their clinical correlations with cognitive impairment, it is shown that the clustering coefficient, global efficiency, and local efficiency of the occipital lobe significantly decrease in ePD-MCI, which reflects the low degree of nodes interconnection, low efficiency of parallel information transmission and low communication efficiency among the nodes in the brain network of the occipital lobe may be the neural marker of ePD-MCI. The finding of personalized topological impairments of the brain network may be a potential characteristic of early PD-MCI.

Attentional paradigm can have a significant influence on the processing and experience of positive and negative emotions. Attentional mechanism refers to the tendency to selectively attend to a particular stimulus while ignoring others. In the context of emotions, individuals may exhibit attentional biases towards either positive or negative emotional stimuli. By directing attention towards a specific stimulus, individuals can modulate their emotional responses. When attention is directed towards negative or threatening stimuli, it can intensify negative emotions such as fear, sadness, anger and anxiety. Conversely, directing attention away from negative stimuli can reduce emotional reactivity and promote emotional regulation. Similarly, paying attention to positive stimuli can amplify positive emotions and facilitate positive emotional experiences. Attentional paradigms are also responsible for cognitive appraisal of emotional stimuli. The allocation of attention can shape how emotional stimuli are evaluated and categorized, influencing the subsequent emotional response. Since the relationship between attention and emotions is complex and can vary across individuals and contexts, it is important to understand the underlying cognitive neural dynamics of the same. Custom rank allocation model (CRAM) was used to decode the underlying neural dynamics of cognitive and emotional resource sharing through the non-significant EEG channels. During the main effect of global–local (GL), CRAM ranks and scores indicated that the EEG channels C4, PZ, OZ, and P4 were found to be the most non-significant channels. Similarly, CRAM ranks and scores of the interaction effects between global–local and positive emotion-negative emotion and the interaction effects between global–local and frequent-deviant-equal indicated midline central EEG channels CZ, PZ, FZ and OZ to be the main contributor of the cognitive and emotional resources to others. Understanding the dynamics of attention-emotion conflicts with reference to significant and non-significant channels is important to gain insights into the complex computational interplay between attention and emotion, leading to a deeper understanding of human cognition and emotion regulation.

Adult hippocampal neurogenesis (AHN) is considered essential in memory formation. The dentate gyrus neural network containing newborn dentate gyrus granule cells at the critical period (4–6 weeks) have been widely discussed in neurophysiological and behavioral experiments. However, how newborn dentate gyrus granule cells at this critical period influence pattern separation of dentate gyrus remains unclear. To address this issue, we propose a biologically related dentate gyrus neural network model with AHN. By Leveraging this model, we find pattern separation is enhanced at the medium level of neurogenesis (5% of mature granule cells). This is because the sparse firing of mature granule cells is increased. We can understand this change from the following two aspects. On one hand, newborn granule cells compete with mature granule cells for inputs from the entorhinal cortex, thereby weakening the firing of mature granule cells. On the other hand, newborn granule cells effectively enhance the feedback inhibition level of the network by promoting the firing of interneurons (Mossy cells and Basket cells) and then indirectly regulating the sparse firing of mature granule cells. To verify the validity of the model for pattern separation, we apply the proposed model to a similar concept separation task and reveal that our model outperforms the original model counterparts in this task.

Transfer learning is increasingly used to decode multi-class motor imagery tasks. Previous transfer learning ignored the optimizability of the source model, weakened the adaptability to the target domain and limited the performance. This paper first proposes the multi-loss fusion convolutional neural network (MF-CNN) to make an optimizable source model. Then we propose a novel source optimized transfer learning (SOTL), which optimizes the source model to make it more in line with the target domain's features to improve the target model's performance. We transfer the model trained from 16 healthy subjects to 16 stroke patients. The average classification accuracy achieves 51.2 ± 0.17% in the four types of unilateral upper limb motor imagery tasks, which is significantly higher than the classification accuracy of deep learning (p < 0.001) and transfer learning (p < 0.05). In this paper, an MI model from the data of healthy subjects can be used for the classification of stroke patients and can demonstrate good classification results, which provides experiential support for the study of transfer learning and the modeling of stroke rehabilitation training.

Various studies have shown that it is necessary to estimate the drivers’ vigilance to reduce the occurrence of traffic accidents. Most existing EEG-based vigilance estimation studies have been performed on intra-subject and multi-channel signals, and these methods are too costly and complicated to implement in practice. Hence, aiming at the problem of cross-subject vigilance estimation of single-channel EEG signals, an estimation algorithm based on capsule network (CapsNet) is proposed. Firstly, we propose a new construction method of the input feature maps to fit the characteristics of CapsNet to improve the algorithm accuracy. Meanwhile, the self-attention mechanism is incorporated in the algorithm to focus on the key information in feature maps. Secondly, we propose substituting the traditional multi-channel signals with the single-channel signals to improve the utility of algorithm. Thirdly, since the single-channel signals carry fewer dimensions of the information compared to the multi-channel signals, we use the conditional generative adversarial network to improve the accuracy of single-channel signals by increasing the amount of data. The proposed algorithm is verified on the SEED-VIG, and Root-mean-square-error (RMSE) and Pearson Correlation Coefficient (PCC) are used as the evaluation metrics. The results show that the proposed algorithm improves the computing speed while the RMSE is reduced by 3%, and the PCC is improved by 12% compared to the mainstream algorithm. Experiment results prove the feasibility of using forehead single-channel EEG signals for cross-subject vigilance estimation and offering the possibility of lightweight EEG vigilance estimation devices for practical applications.