Spinal cord injury (SCI), which is the injury of the spinal cord site resulting in motor dysfunction, has prompted the use of motor imagery (MI)-based brain computer interface (BCI) systems for motor function reconstruction. However, analyzing electroencephalogram signals and brain function mechanisms for SCI patients is challenging. This is due to their low signal-to-noise ratio and high variability. We propose using the phase locking value (PLV) to construct the brain network in α and β rhythms for both SCI patients and healthy individuals. This approach aims to analyze the changes in brain network connectivity and brain function mechanisms following SCI. The results show that the connection strength of the α rhythm in the healthy control (HC) group is stronger than that in the SCI group, and the connection strength in the β rhythm of the SCI group is stronger than that in the HC group. Moreover, we extract the PLV with common spatial pattern (PLV-CSP) feature from the MI data of the SCI group. The experimental results for 12 SCI patients include that the peak classification accuracy is 100%, and the average accuracy of the ten-fold cross-verification is 95.6%. Our proposed approach can be used as a potential valuable method for SCI pathological studies and MI-based BCI rehabilitation systems.

This work aims to explore the control effect of DBS on Alzheimer's disease (AD) from a neurocomputational perspective. Firstly, a data-driven cortical network model is constructed using the Diffusion Tensor Imaging data. Then, a typical electrophysiological feature of EEG slowing in AD is reproduced by reducing the synaptic connectivity parameters. The corresponding changes in kinetic behavior mainly include an oscillation decrease in the amplitude and frequency of the pyramidal neuron population. Subsequently, DBS current with specific parameters is introduced into three potential targets of the hippocampus, the nucleus accumbens and the olfactory tubercle, respectively. The results indicate that applying DBS to simulated mild AD patients induces an increase in relative alpha power, a decrease in relative theta power, and a significant rightward shift of the dominant frequency. This is consistent with the EEG reversal in pharmacological treatments for AD. Further, the optimal stimulation strategy of DBS is investigated through spectral and statistical analyses. Specifically, the pathological symptoms of AD could be alleviated by adjusting the critical parameters of DBS, and the control effect of DBS on various targets is that the hippocampus is superior to the olfactory tubercle and nucleus accumbens. Finally, using correlation analysis between the power increments and the nodal degrees, it is concluded that the control effect of DBS is related to the importance of the nodes in the brain network. This study provides a theoretical guidance for determining DBS targets and parameters, which may have a substantial impact on the development of DBS treatment for AD.

Tasks with high mental workload often involve higher cognitive functions of the human brain and complex information flow involving multiple brain regions. However, the dynamics of functional connectivity between brain regions during high mental workload have not been well-studied. We use an analysis approach designed to find repeating network states from gamma-band phase locking value networks built from electroencephalograph data collected while participants engaged in tasks with different levels of mental workload. First, we define network states as results of clustering based on the closeness centrality node-level network metric. Second, we found that the transition between network states is not completely random. And, we found significant differences in network state statistics between low and high mental workload. Third, we found significant correlation between features calculated from the network state sequence and behavioral performance. Finally, we use dynamic network features as input to a support vector machine classifier and obtain cross-participant average decoding accuracy of 69.6%. Our methods provide a new perspective for analyzing the dynamics of electroencephalograph signals and have potential application to the decoding of mental workload level.

Working memory is a complex cognitive system that temporarily maintains purpose-relevant information during human cognition performance. Working memory performance has also been found to be sensitive to high-altitude exposure. This study used a multilevel change detection task combined with Electroencephalogram data to explore the mechanism of working memory change from high-altitude exposure. When compared with the sea-level population, the performance of the change detection task with 5 memory load levels was measured in the Han population living in high-altitude areas, using the event-related potential analysis and task-related connectivity network analysis. The topological analysis of the brain functional network showed that the normalized modularity of the high-altitude group was higher in the memory maintenance phase. Event-related Potential analysis showed that the peak latencies of P1 and N1 components of the high-altitude group were significantly shorter in the occipital region, which represents a greater attentional bias in visual early processing. Under the condition of high memory loads, the high-altitude group had a larger negative peak in N2 amplitude compared to the low-altitude group, which may imply more conscious processing in visual working memory. The above results revealed that the visual working memory change from high-altitude exposure might be derived from the attentional bias and the more conscious processing in the early processing stage of visual input, which is accompanied by the increase of the modularity of the brain functional network. This may imply that the attentional bias in the early processing stages have been influenced by the increased modularity of the functional brain networks induced by high-altitude exposure.

Electroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation. Textural and statistical features are extracted in the first phase, while tunable q-factor wavelet transform (TQWT) aids in multileveled feature extraction. The second phase employs iterative neighborhood component analysis (INCA) for feature selection, and the k-nearest neighbors (kNN) classifier is applied for classification, yielding channel-specific results. A new cortex map generation model highlights the most active channels using median and intersection functions. Our BWHPat-driven model consistently achieved over 99% classification accuracy across three scenarios using the publicly available EEG pain dataset. Furthermore, a semantic cortex map precisely identifies pain-affected brain regions. This study signifies the contribution to EEG signal classification and neuroscience. The BWHPat pattern establishes a unique link between astronomy and feature extraction, enhancing the understanding of brain activities.

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.

Perception of voice means acoustic electric conversion in the auditory system, and changes of external magnetic field can affect the neural activities by taming the channel current via some field components including memristor and Josephson junction. Combination of two capacitors via an electric component is effective to describe the physical property of artificial cell membrane, which is often used to reproduce the characteristic of electric activities in cell membrane. Involvement of two capacitive variables for two capacitors in the neural circuit can discern the effect of field diversity in the media in two sides of the cell membrane in theoretical way. A Josephson junction is used to couple a piezoelectric neural circuit composed of two capacitors, one inductor and one nonlinear resistor. Field energy is mainly kept in the capacitive and inductive components, and it is obtained and converted into dimensionless energy function. The Hamilton energy function in an equivalent auditory neuron is verified by using the Helmholtz theorem. Noisy excitation on the neural circuit can be detected via the Josephson junction channel and similar stochastic resonance is detected by regulating the noise intensity, as a result, the average energy reaches a peak value under stochastic resonance. An adaptive law controls the bifurcation parameter, which is relative to the membrane property, and energy shift controls the mode selection during continuous growth of the bifurcation parameter. That is, external energy injection derived from acoustic wave or magnetic field will control the energy level, and then suitable firing patterns are controlled effectively.

Neurodevelopmental disorders (NDs) often hamper multiple functional prints of a child brain. Despite several studies on their neural and speech responses, multimodal researches on NDs are extremely rare. The present work examined the electroencephalography (EEG) and speech signals of the ND and control children, who performed "Hindi language" vocal tasks (V) of seven different categories, viz. 'vowel', 'consonant', 'one syllable', 'multi-syllable', 'compound', 'complex', and 'sentence' (V1-V7). Statistical testing of EEG parameters showed substantially high beta and gamma band energies in frontal, central, and temporal head sites of NDs for tasks V1-V5 and in parietal too for V6. For the 'sentence' task (V7), the NDs yielded significantly high theta and low alpha energies in the parietal area. These findings imply that even performing a general context-based task exerts a heavy cognitive loading in neurodevelopmental subjects. They also exhibited poor auditory comprehension while executing a long phrasing. Further, the speech signal analysis manifested significantly high amplitude (for V1-V7) and frequency (for V3-V7) perturbations in the voices of ND children. Moreover, the classification of subjects as ND or control was done via EEG and speech features. We attained 100% accuracy, precision, and F-measure using EEG features of all tasks, and using speech features of the 'complex' task. Jointly, the 'complex' task transpired as the best vocal stimuli among V1-V7 for characterizing ND brains. Meanwhile, we also inspected inter-relations between EEG energies and speech attributes of the ND group. Our work, thus, represents a unique multimodal layout to explore the distinctiveness of neuro-impaired children.

Childhood focal epilepsy (CFE) is a serious neurological disorder characterized by epileptic seizures arising from a focal or multi-focal zone of the brain in clinics. During non-rapid eye movement (NREM) sleep stage, epileptiform discharges become frequent, and sleep spindles are generated through local interaction between thalamic neurons for CFE patients. Recent research has shown that epileptiform spikes significantly induce spindle oscillations within 1 s (say, spike-spindle coupling) during NREM sleep in focal epilepsy, which might damage cognitive function of epilepsy patients. However, the temporal interaction mechanism between spikes and spindles is lack of understanding. In this paper, we first develop a new thalamocortical model of CFE (CFE-TCM) by integrating M-type potassium current, persistent sodium current and NMDAR current into Costa model, where the three types of currents are important for modulating the excitability of thalamocortical system. Then we demonstrate in simulations that: (1) the temporal spike-spindle coupling oscillatory patterns do exist in real CFE-EEGs recorded in clinics; (2) the constructed model CFE-TCM has a capacity of generating spike-spindle coupling discharges, and the corresponding statistical results are consistent with those obtained from real EEGs; (3) the spike-spindle coupling discharges are mediated by the strength of long-range thalamus-cortex connections where the excitable projection from thalamocortical neuron in thalamus to pyramidal neuron in cortex takes a great role. The obtained results reveal that pathological spike-spindle coupling may be a potential marker of thalamocortical circuit dysfunction, which will provide a possible treatment strategy for disease progression and cognition impairment in focal epilepsy.

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.

Methamphetamine (MA) addiction leads to impairment of neural communication functions in the brain, and functional connectivity (FC) may be a valid indicator. However, it is unclear how FC in the brain changes in methamphetamine use disorder (MUD) after treatment with repetitive transcranial magnetic stimulation (rTMS). Thirty-four patients with MUD participated in this study. The subjects were randomized to receive the active or sham rTMS for four weeks. Subjects performed electroencephalography (EEG) examinations and visual analogue scale (VAS) assessments before and after the treatment. The FC networks were constructed and visualized, and then the graph theory analysis was carried out. Finally, machine learning was used to classify FC networks before and after rTMS. The results showed that (1) the active group showed a significant enhancement in connectivity in the beta band; (2) the global efficiency, local efficiency, and aggregation coefficient of the active group in the beta band decreased significantly; (3) the LDA algorithm combined with the beta band FC matrix achieved an average accuracy of 82.5% in distinguishing before and after treatment. This study demonstrated that brain FC could effectively assess the therapeutic effect of rTMS, among which the beta band was the most sensitive and effective frequency band.

The basal ganglia (BG) in the brain exhibit diverse functions for motor, cognition, and emotion. Such BG functions could be made via competitive harmony between the two competing pathways, direct pathway (DP) (facilitating movement) and indirect pathway (IP) (suppressing movement). As a result of break-up of harmony between DP and IP, there appear pathological states with disorder for movement, cognition, and psychiatry. In this paper, we are concerned about the Huntington's disease (HD), which is a genetic neurodegenerative disorder causing involuntary movement and severe cognitive and psychiatric symptoms. For the HD, the number of D2 SPNs ( ) is decreased due to degenerative loss, and hence, by decreasing (fraction of ), we investigate break-up of harmony between DP and IP in terms of their competition degree , given by the ratio of strength of DP ( ) to strength of IP ( ) (i.e., ). In the case of HD, the IP is under-active, in contrast to the case of Parkinson's disease with over-active IP, which results in increase in (from the normal value). Thus, hyperkinetic dyskinesia such as chorea (involuntary jerky movement) occurs. We also investigate treatment of HD, based on optogenetics and GP ablation, by increasing strength of IP, resulting in recovery of harmony between DP and IP. Finally, we study effect of loss of healthy synapses of all the BG cells on HD. Due to loss of healthy synapses, disharmony between DP and IP increases, leading to worsen symptoms of the HD.

This paper presents an innovative feature engineering framework based on lattice structures for the automated identification of Alzheimer's disease (AD) using electroencephalogram (EEG) signals. Inspired by the Shannon information entropy theorem, we apply a probabilistic function to create the novel Lattice123 pattern, generating two directed graphs with minimum and maximum distance-based kernels. Using these graphs and three kernel functions (signum, upper ternary, and lower ternary), we generate six feature vectors for each input signal block to extract textural features. Multilevel discrete wavelet transform (MDWT) was used to generate low-level wavelet subbands. Our proposed model mirrors deep learning approaches, facilitating feature extraction in frequency and spatial domains at various levels. We used iterative neighborhood component analysis to select the most discriminative features from the extracted vectors. An iterative hard majority voting and a greedy algorithm were used to generate voted vectors to select the optimal channel-wise and overall results. Our proposed model yielded a classification accuracy of more than 98% and a geometric mean of more than 96%. Our proposed Lattice123 pattern, dynamic graph generation, and MDWT-based multilevel feature extraction can detect AD accurately as the proposed pattern can extract subtle changes from the EEG signal accurately. Our prototype is ready to be validated using a large and diverse database.

The electroencephalogram (EEG) signal is being investigated as a more confidential biometric for person identification. Despite recent advancements, a persistent challenge lies in the influence of variations in affective states. Affective states consistently exist during data collection, regardless of the protocol used. Additionally, the inherently non-stationary nature of EEG makes it susceptible to fluctuations in affective states over time. Therefore, it would be highly crucial to perform precise EEG-based person identification under varying affective states. This paper employed an integrated Multi-scale Convolution and Graph Pooling network (MCGP) to mitigate the impact of affective state variations. MCGP utilized multiple 1D convolutions at different scales to dynamically extract and fuse features. Additionally, a graph pooling layer with an attention mechanism was incorporated to generate hierarchical graph embeddings. These embeddings were concatenated as inputs for a fully connected classification layer. Experiments were conducted on the SEED and SEED-V dataset, revealing that MCGP achieved an average accuracy of 85.51% for SEED and 88.69% for SEED-V in cross-session conditions involving mixed affective states. Under single affective state cross-session scenario, MCGP achieved an average accuracy of 85.75% for SEED and 88.06% for SEED-V for the same affective states, while obtaining 79.57% for SEED and 84.52% for SEED-V for different affective states. Results indicated that, compared to the baseline methods, MCGP effectively mitigated the impact of variations in affective states across different sessions. In single affective state cross-session scenario, identification performance for the same affective states was slightly higher than that for different affective states.

Attention is a core cognitive domain crucial in facilitating day-to-day life. Using an attention network test (ANT) along with event-related potentials (ERPs) in older individuals with hearing loss would provide excellent information about the impact of hearing loss on attentional processes. Thus, the current study aims to understand the attentional deficits and its cortical dynamics in older individuals with and without hearing loss. The study recruited 40 participants, 20 older individuals with hearing loss and 20 age and education-matched controls with normal hearing. All the participants underwent cognitive assessment using ANT with simultaneous 32-channel EEG recording. Results revealed significant impairment in executive attention and subtle alterations in alerting and orienting attention among older individuals with hearing loss compared to their normal-hearing counterparts. These findings suggest the negative impact of hearing loss on attentional networks. In addition, ANT and ERPs provide insight into the underlying neural mechanisms in specific attention network deficits associated with hearing loss.

The recent exceptional demand for emotion recognition systems in clinical and non-medical applications has attracted the attention of many researchers. Since the brain is the primary object of understanding emotions and responding to them, electroencephalogram (EEG) signal analysis is one of the most popular approaches in affect classification. Previously, different approaches have been presented to benefit from brain connectivity information. We envisioned analyzing the interactions between brain electrodes with the information potential and providing a new index to quantify the connectivity matrix. The current study proposed a simple measure based on the cross-information potential between pairs of EEG electrodes to characterize emotions. This measure was tested for different EEG frequency bands to realize which EEG waves could be fruitful in recognizing emotions. Support vector machine and k-nearest neighbor (kNN) were implemented to classify four emotion categories based on two-dimensional valence and arousal space. Experimental results on the Database for Emotion Analysis using Physiological signals revealed a maximum accuracy of 90.14%, a sensitivity of 89.71%, and an F-score of 94.57% using kNN. The gamma frequency band obtained the highest recognition rates. Furthermore, low valence-low arousal was classified more effectively than other classes.

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.

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.

Emotion recognition plays an important role in human life and healthcare. The EEG has been extensively researched as an objective indicator of intense emotions. However, current existing methods lack sufficient analysis of shallow and deep EEG features. In addition, human emotions are complex and variable, making it difficult to comprehensively represent emotions using a single-modal signal. As a signal associated with gaze tracking and eye movement detection, Eye-related signals provide various forms of supplementary information for multimodal emotion analysis. Therefore, we propose a Pseudo-Siamese Pyramid Network (PSPN) for multimodal emotion analysis. The PSPN model employs a Depthwise Separable Convolutional Pyramid (DSCP) to extract and integrate intrinsic emotional features at various levels and scales from EEG signals. Simultaneously, we utilize a fully connected subnetwork to extract the external emotional features from eye-related signals. Finally, we introduce a Pseudo-Siamese network that integrates a flexible cross-modal dual-branch subnetwork to collaboratively utilize EEG emotional features and eye-related behavioral features, achieving consistency and complementarity in multimodal emotion recognition. For evaluation, we conducted experiments on the DEAP and SEED-IV public datasets. The experimental results demonstrate that multimodal fusion significantly improves the accuracy of emotion recognition compared to single-modal approaches. Our PSPN model achieved the best accuracy of 96.02% and 96.45% on the valence and arousal dimensions of the DEAP dataset, and 77.81% on the SEED-IV dataset, respectively. Our code link is: https://github.com/Yinyanyan003/PSPN.git.

Chaos is often described as the limited development of nonlinear dynamic systems that create intricate and non-repetitive patterns. In this study, we questioned how chaotic electronic signals can be transformed into sound stimuli and explored their impact on brain activity using Electroencephalography (EEG). Our experiment involved 31 participants exposed to sounds generated from three processes from electronic implementations: signals from chaotic attractors, periodic limit cycles,and aleatory distributions. Our goal was to analyze characteristics and EEG signals to uncover the complex relationship between chaotic auditory stimuli and cognitive processes. Interestingly the chaotic stimuli caused a reduction in synchronization in the delta ( ) and theta ( ) frequency bands. We observed differences of up to 30 and 40%, primarily concentrated in the brain's frontal areas. This desynchronization in and bands, seen in individuals, has implications for regulating irregular power in certain neural disorders. On the other hand, exposure to signals had mostly minimal effects on EEG readings. This research significantly contributes to our understanding of how the brain responds to stimuli derived from electronic systems. It sheds light on applications for modulating activity. Examining unpredictable sounds offers an understanding of the unique impacts of chaotic auditory inputs on brain activity, opening possibilities for further investigations at the crossroads of chaos theory, acoustics, and neuroscience.

Energy absorption and consumption are essential for the activity of single neurons and neuronal networks. The synchronization mode transition and energy dependence in a delay-coupled FitzHugh-Nagumo (FHN) neuronal system driven by chaotic activity are investigated in this paper. With the change of chaotic current intensity, it was found that the synchronization mode of coupled neurons undergoes synchronous state, transition state, anti-phase state, alternating asynchronous and anti-phase state, and chaotic current-induced chaotic state. The Hamiltonian energy is much dependent on the synchronization mode of coupled neurons. The synchronization mode and the Hamiltonian energy of coupled neurons can be modulated by chaotic current intensity, coupling strength and time delay. The introduction of the time delay induces the system to become bistable state. Chaotic current as an external force induced transitions between the synchronous and anti-phase states. Coupling strength is an intrinsic property of the system and can change the properties of the bistable state. Furthermore, the synchronous and anti-phase states appear intermittently with the increasing of time delay. A chained neuronal network is used to prove that the synchronization mode transition of the system of multiple neurons is similar to the two neurons. The results of this paper might help one to understand the intrinsic energy alteration mechanisms of neuronal synchronization.