Steady-state visual evoked potentials (SSVEPs) in patients with schizophrenia and healthy controls are significantly different and have a potential application in distinguishing between these groups. However, the underlying neural dynamic mechanism of SSVEPs in schizophrenia remains unclear. Here, we investigated this mechanism by stimulating SSVEP responses and analyzing them using large-scale computational brain models based on neuroimaging data from schizophrenia patients and healthy controls. The results revealed that SSVEP responses to alpha-band (8–12 Hz) visual stimulation were significantly attenuated in schizophrenia patients compared with healthy controls under alpha-band, whereas the opposite pattern was observed under low-band (2–3 Hz) visual stimulation. The efficiencies of these functional networks followed a similar pattern of change, and differences in internetwork functional connectivity (FC), especially the visual network (VIS) and default mode network (DMN), between schizophrenia patients and healthy controls were identified as contributors to SSVEP responses. We subsequently developed a regulatory strategy using the large-scale brain model of schizophrenia, and this strategy improved the SSVEP response to levels similar to those in healthy controls. This study not only elucidates the underlying mechanisms of the differences in SSVEP responses between patients with schizophrenia and healthy controls but also validates the application of large-scale brain modeling for brain disorders.

People celebrate the 100th anniversary of the discovery of human EEG from various perspectives and envision its promising future. In 1948, 20 years after the discovery of human EEG, the first EEG equipment was introduced in China. A long and slow period of development followed. The number of EEG papers from China, about 20 a year, represented about 1/80 of the global total by the 70th anniversary of EEG in 1994. After a steady rise, Chinese EEG research reached about 1/3 of the global total by the 100th anniversary of EEG in 2024. Academic organizations related to EEG were established during this process, along with the widespread use of EEG in thousands of clinical hospitals and closely collaborating with international colleagues. Chinese and global EEG research are being bridged in this article to benefit mankind in the future by jointly creating more advancements in EEG technology.

ABSTRACT Objective To investigate the reliability of very high‐frequency oscillations (VHFOs, 500–2000 Hz) as biomarkers in identifying the epileptogenic zone (EZ) in mesial temporal lobe epilepsy (MTLE) patients. Methods We reviewed 19 MTLE patients who underwent stereo‐electroencephalography‐guided radiofrequency thermocoagulation. Interictal and ictal VHFOs were captured in four regions of interest (ROIs): amygdalohippocampal complex (AHC), parahippocampal gyrus (PG), white matter, and lateral temporal lobe. We analyzed the occurrence of VHFOs per minute, per contact: (a) in each ROI; (b) during awake resting state, sleep, and seizure within the seizure onset zone (SOZ) and non‐SOZ contacts; (c) in the EZ, propagation zone (PZ), and non‐involved zone (NIZ). The percentage and ratio of thermocoagulated VHFOs contacts in seizure‐free (SF) and non‐seizure‐free (NSF) patients were also analyzed. Results At the last follow‐up (19.31 ± 6.36 months), 13 patients were SF and 6 were NSF. The VHFOs rate was significantly higher in the SOZ contacts during the ictal than interictal period compared to non‐SOZ contacts. Very‐fast ripples rate was more prevalent in the EZ than in the NIZ. VHFOs rate was higher in the AHC and PG compared to other regions. At a group level, SF patients had a higher percentage and ratio of thermocoagulated VHFOs contacts compared to NSF patients. At a patient level, some NSF patients had most of their VHFO‐generating tissues ablated, while some SF did not. Interpretation The presence and ablation of VHFO‐generating areas was associated with good surgical outcome at a group level. However, at a patient level, VHFOs may not be as reliable in identifying the EZ.

Over the past decade, the digital twin brain (DTB) has emerged as a transformative brain science paradigm, integrating multimodal data to construct dynamic models closely simulating biological brain function. This approach has advanced understanding of structure-function relationships, cognitive behaviors, and disease mechanisms, while supporting personalized therapies. Recent progress highlights DTB's potential in capturing functional heterogeneity, simulating information integration, and predicting individual cognitive and pathological variations. Looking forward, the development of a high-precision DTB is expected to drive breakthroughs in understanding brain mechanisms and enabling precision medicine. This perspective summarizes DTB modeling strategies, including multimodal data integration and optimization, while addressing challenges such as model granularity, and biological interpretability. Future efforts should focus on refining modeling techniques and integrating with brain cognition and disease. We believe these advancements will pave the way for breakthroughs in brain science and precision medicine, ushering in a new era of neuroscience and personalized healthcare.

Spiking neural networks (SNNs), known for their rich spatio-temporal dynamics, have recently gained considerable attention in EEG-based emotion recognition. However, conventional model training approaches often fail to fully exploit the capabilities of SNNs, posing challenges for effective EEG data analysis. In this work, we propose a novel bio-information-fused SNN (BISNN) model to enhance EEG-based emotion recognition. The BISNN model incorporates biologically plausible intrinsic parameters into spiking neurons and is initialized with a structurally equivalent pre-trained ANN model. By constructing a bio-information-fused loss function, the BISNN model enables simultaneous training under dual constraints. Extensive experiments on benchmark EEG-based emotion datasets demonstrate that the BISNN model achieves competitive performance compared to state-of-the-art methods. Additionally, ablation studies investigating various components further elucidate the mechanisms underlying the model’s effectiveness and evolution, aligning well with previous findings.

Spiking Neural Networks (SNNs) have emerged as a promising tool for event-based optical flow estimation tasks due to their capability for spatio-temporal information processing and low-power computation. However, the performance of SNN models is often constrained, limiting their applications in real-world scenarios. To address this challenge, we propose ST-FlowNet, a novel neural network architecture specifically designed for optical flow estimation from event-based data. The ST-FlowNet architecture integrates ConvGRU modules to facilitate cross-modal feature augmentation and temporal alignment of the predicted optical flow, thereby improving the network's ability to capture complex motion patterns. Additionally, we introduce two strategies for deriving SNN models from pre-trained artificial neural networks (ANNs): a standard ANN-to-SNN conversion pipeline and our proposed BISNN method. Notably, the BISNN method alleviates the complexities involved in selecting biologically inspired parameters, further enhancing the robustness of SNNs for optical flow estimation tasks. Extensive evaluations on three benchmark event-based datasets demonstrate that the SNN-based ST-FlowNet model outperforms state-of-the-art methods, achieving superior accuracy in optical flow estimation across a diverse range of dynamic visual scenes. Furthermore, the energy efficiency of models also underscores the potential of SNNs for practical deployment in energy-constrained environments. Overall, our work presents a novel framework for optical flow estimation using SNNs and event-based data, contributing to the advancement of neuromorphic vision applications.

As a foundational architecture of artificial intelligence models, Transformer has been recently adapted to spiking neural networks with promising performance across various tasks. However, existing spiking Transformer (ST)-based models require a substantial number of parameters and incur high computational costs, thus limiting their deployment in resource-constrained environments. To address these challenges, we propose combining synapse pruning with a synergistic learning-based compensation strategy to derive lightweight ST-based models. Specifically, two types of tailored pruning strategies are introduced to reduce redundancy in the weight matrices of ST blocks: an unstructured L_1P method to induce sparse representations, and a structured DSP method to induce low-rank representations. In addition, we propose an enhanced spiking neuron model, termed the synergistic leaky integrate-and-fire (sLIF) neuron, to effectively compensate for model pruning through synergistic learning between synaptic and intrinsic plasticity mechanisms. Extensive experiments on benchmark datasets demonstrate that the proposed methods significantly reduce model size and computational overhead while maintaining competitive performance. These results validate the effectiveness of the proposed pruning and compensation strategies in constructing efficient and high-performing ST-based models.

Steady-state visual evoked potentials (SSVEPs) are brain nonlinear responses evoked by repetitive visual stimuli with specific frequencies. In addition to the frequency of visual stimuli, visual attention can influence SSVEPs. For instance, an attended stimulus was found to evoke enhanced SSVEPs. However, because of significant challenges associated with quantifying attention levels during SSVEP experiments, the mechanism underlying the attentional modulation of SSVEPs has not been fully established. In the present study, we addressed this issue via large-scale brain dynamics modeling. Consistent with previous experimental observations, our model successfully reproduced the phenomenon of the attentional modulation of SSVEPs, and showed that both the power and the signal–noise ratio of SSVEPs were positively related to attention levels. We also found that when attention levels were high, the brain network displayed higher local and more global efficiencies, and the inter-network connectivity among the visual network, the default mode network, and the dorsal attentional network increased. In addition, our model supported a negative relationship between the features of the intermodulation component of SSVEPs with respect to attention levels. Overall, these results represent a quantification of the effect of attention on SSVEPs, and highlight the potential of large-scale brain dynamics modeling in elucidating cognitive mechanisms.

The mesial temporal lobe epilepsy (MTLE) seizures are believed to originate from medial temporal structures, including the amygdala, hippocampus, and temporal cortex. Thus, the seizures onset zones (SOZs) of MTLE locate in these regions. However, whether the neural features of SOZs are specific to different medial temporal structures are still unclear and need more investigation. To address this question, the present study tracked the features of two different high frequency oscillations (HFOs) in the SOZs of these regions during MTLE seizures from 10 drug-resistant MTLE patients, who received the stereo electroencephalography (SEEG) electrodes implantation surgery in the medial temporal structures. Remarkable difference of HFOs features, including the proportions of HFOs contacts, percentages of HFOs contacts with significant coupling and firing rates of HFOs, could be observed in the SOZs among three medial temporal structures during seizures. Specifically, we found that the amygdala might contribute to the generation of MTLE seizures, while the hippocampus plays a critical role for the propagation of MTLE seizures. In addition, the HFOs firing rates in SOZ regions were significantly larger than those in NonSOZ regions, suggesting the potential biomarkers of HFOs for MTLE seizure. Moreover, there existed higher percentages of SOZs contacts in the HFOs contacts than in all SEEG contacts, especially those with significant coupling to slow oscillations, implying that specific HFOs features would help identify the SOZ regions. Taken together, our results displayed the features of HFOs in different medial temporal structures during MTLE seizures, and could deepen our understanding concerning the neural mechanism of MTLE.

Spiking neural networks (SNNs) mimic brain computational strategies, and exhibit substantial capabilities in spatiotemporal information processing. As an essential factor for human perception, visual attention refers to the dynamic process for selecting salient regions in biological vision systems. Although visual attention mechanisms have achieved great success in computer vision applications, they are rarely introduced into SNNs. Inspired by experimental observations on predictive attentional remapping, we propose a new spatial-channel-temporal-fused attention (SCTFA) module that can guide SNNs to efficiently capture underlying target regions by utilizing accumulated historical spatial-channel information in the present study. Through a systematic evaluation on three event stream datasets (DVS Gesture, SL-Animals-DVS, and MNIST-DVS), we demonstrate that the SNN with the SCTFA module (SCTFA-SNN) not only significantly outperforms the baseline SNN (BL-SNN) and two other SNN models with degenerated attention modules, but also achieves competitive accuracy with the existing state-of-the-art (SOTA) methods. Additionally, our detailed analysis shows that the proposed SCTFA-SNN model has strong robustness to noise and outstanding stability when faced with incomplete data, while maintaining acceptable complexity and efficiency. Overall, these findings indicate that incorporating appropriate cognitive mechanisms of the brain may provide a promising approach to elevate the capabilities of SNNs.

In temporal lobe epilepsy (TLE), the epileptogenic zones, such as the temporal lobe structure, could generate pathological high-frequency oscillations (pHFOs, 250-500 Hz) before the ictal period. These pHFOs have also been observed during the process of seizures in both TLE patients and animals, exhibiting a critical role as promising biomarkers for TLE seizures. TLE seizures could be modulated via regulating the neural excitability in epileptogenic zones, for that TLE is primarily associated with the excitation-inhibition imbalance. However, whether these kinds of modulations could also impact the pHFOs characteristics during TLE seizures is still unclear. For this purpose, we pharmaco-genetically inhibited the principal cells (PCs) in the mouse CA3 region and tracked the difference in the behavioral and electrophysiological features during LiCl-pilocarpine-induced TLE seizure between the hM4Di+CNO (experimental) mice and mCherry+CNO (control) mice. Delayed latency, decreased averaged duration, and reduced counts of the generalized seizure were observed in the experimental mice. Besides, the electrophysiological characteristics, such as the firing rate of PCs and the count of pHFO, exhibited significant decline in the CA3 and CA1 regions. During TLE seizure, there existed strong phase-coupling between pHFO and PCs spike timing in the control mice, while it was abolished in the experimental mice. In addition, we also found that the counts of pHFO were significantly associated with the behavioral features, indicating the close relationships within them. Collectively, our findings suggested that alterations in pHFO and the retardation of seizures may be attributed to disruptions in neuronal excitability, and the variations of electrophysiological features were related to seizure severity during TLE seizures. These results provide valuable insights into the role of pHFOs in TLE and shed light on the underlying mechanisms involved.

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Visual object tracking, which is primarily based on visible light image sequences, encounters numerous challenges in complicated scenarios, such as low light conditions, high dynamic ranges, and background clutter. To address these challenges, incorporating the advantages of multiple visual modalities is a promising solution for achieving reliable object tracking. However, the existing approaches usually integrate multimodal inputs through adaptive local feature interactions, which cannot leverage the full potential of visual cues, thus resulting in insufficient feature modeling. In this study, we propose a novel multimodal hybrid tracker (MMHT) that utilizes frame-event-based data for reliable single object tracking. The MMHT model employs a hybrid backbone consisting of an artificial neural network (ANN) and a spiking neural network (SNN) to extract dominant features from different visual modalities and then uses a unified encoder to align the features across different domains. Moreover, we propose an enhanced transformer-based module to fuse multimodal features using attention mechanisms. With these methods, the MMHT model can effectively construct a multiscale and multidimensional visual feature space and achieve discriminative feature modeling. Extensive experiments demonstrate that the MMHT model exhibits competitive performance in comparison with that of other state-of-the-art methods. Overall, our results highlight the effectiveness of the MMHT model in terms of addressing the challenges faced in visual object tracking tasks.

Spiking neural networks (SNNs) have demonstrated excellent capabilities in various intelligent scenarios. Most existing methods for training SNNs are based on the concept of synaptic plasticity; however, learning in the realistic brain also utilizes intrinsic non-synaptic mechanisms of neurons. The spike threshold of biological neurons is a critical intrinsic neuronal feature that exhibits rich dynamics on a millisecond timescale and has been proposed as an underlying mechanism that facilitates neural information processing. In this study, we develop a novel synergistic learning approach that involves simultaneously training synaptic weights and spike thresholds in SNNs. SNNs trained with synapse-threshold synergistic learning~(STL-SNNs) achieve significantly superior performance on various static and neuromorphic datasets than SNNs trained with two degenerated single-learning models. During training, the synergistic learning approach optimizes neural thresholds, providing the network with stable signal transmission via appropriate firing rates. Further analysis indicates that STL-SNNs are robust to noisy data and exhibit low energy consumption for deep network structures. Additionally, the performance of STL-SNN can be further improved by introducing a generalized joint decision framework. Overall, our findings indicate that biologically plausible synergies between synaptic and intrinsic non-synaptic mechanisms may provide a promising approach for developing highly efficient SNN learning methods.

The local field potential (LFP) is an extracellular electrical signal associated with neural ensemble input and dendritic signaling. Previous studies have linked gamma band oscillations of the LFP in cortical circuits to sensory stimuli encoding, attention, memory, and perception. Inconsistent results regarding gamma tuning for visual features were reported, but it remains unclear whether these discrepancies are due to variations in electrode properties. Specifically, the surface area and impedance of the electrode are important characteristics in LFP recording. To comprehensively address these issues, we conducted an electrophysiological study in the V1 region of lightly anesthetized mice using two types of electrodes: one with higher impedance (1 M Omega) and a sharp tip (10 mu m), while the other had lower impedance (100 K Omega) but a thicker tip (200 mu m). Our findings demonstrate that gamma oscillations acquired by sharp-tip electrodes were significantly stronger than those obtained from thick-tip electrodes. Regarding size tuning, most gamma power exhibited surround suppression at larger gratings when recorded from sharp-tip electrodes. However, the majority showed enhanced gamma power at larger gratings when recorded from thick-tip electrodes. Therefore, our study suggests that microelectrode parameters play a significant role in accurately recording gamma oscillations and responsive tuning to sensory stimuli.

Through the modulation of its surround, an identical visual stimulus can be perceived as more or less salient, allowing it to either stand out or seamlessly integrate with the rest of the visual scene. Gamma rhythms are associated with processing stimulus features across extensive areas of the visual field. Consistent with this concept, the magnitude of visually induced gamma rhythm depends on how well stimulus features aligned both within and outside the classical receptive field (CRF) at the recording site. However, there still exists some uncertainty regarding the encoding of context-modulated orientation discontinuity by gamma rhythms. To address this concern, we conducted extracellular recordings in layers II/III and IV of area V1 using lightly anaesthetized mice to investigate the gamma tuning for stimuli with orientation discontinuity. Our study revealed that gamma rhythms exhibit a preference for stimuli with orientation discontinuity similar to the spiking responses observed in V1, which contradicts the findings of previous studies. Furthermore, the gamma tuning of discontinuous orientations exhibits a moderate correlation with spike tuning and a positive correlation with the strength of surround suppression. Therefore, our study suggests a close association between gamma tuning and nearby spiking tuning; additionally, it highlights the connection between the encoding of visual features by gamma rhythms and functional architecture, as well as neural signal integration. KEY POINTS: Visual context modulates the gamma rhythms in the primary visual cortex. Discontinuous orientation elicits significantly enhanced gamma rhythms compared to the iso-orientation stimulus. The gamma tuning of discontinuous orientations exhibits a moderate correlation with spike tuning. Gamma tuning of orientation discontinuity exhibits a positive correlation with the strength of surround suppression.

Resistance to traditional antiepileptic drugs is a majorchallengein chronic epilepsy treatment. MicroRNA-based gene therapy is a promisingalternative but has demonstrated limited efficacy due to poor blood-brainbarrier permeability, cellular uptake, and targeting efficiency. Adenosineis an endogenous antiseizure agent deficient in the epileptic braindue to elevated adenosine kinase (ADK) activity in reactive A1 astrocytes.We designed a nucleic acid nanoantiepileptic drug (tFNA-ADK(ASO)@AS1) based on a tetrahedral framework nucleic acid (tFNA), carryingan antisense oligonucleotide targeting ADK (ADK(ASO)) andA1 astrocyte-targeted peptide (AS1). This tFNA-ADK(ASO)@AS1construct effectively reduced brain ADK, increased brain adenosine,mitigated aberrant mossy fiber sprouting, and reduced the recurrentspontaneous epileptic spike frequency in a mouse model of chronictemporal lobe epilepsy. Further, the treatment did not induce anyneurotoxicity or major organ damage. This work provides proof-of-conceptfor a novel antiepileptic drug delivery strategy and for endogenousadenosine as a promising target for gene-based modulation.

BACKGROUND:The anesthetic states are accompanied by functional alterations. However, the dose-related adaptive alterations in the higher-order network under anesthesia, e. g. default mode network (DMN), are poorly revealed. METHODS:We implanted electrodes in brain regions of the rat DMN to acquire local field potentials to investigate the perturbations produced by anesthesia. Relative power spectral density, static functional connectivity (FC), fuzzy entropy of dynamic FC, and topological features were computed from the data. RESULTS:The results showed that adaptive reconstruction was induced by isoflurane, exhibiting reduced static and stable long-range FC, and altered topological features. These reconstruction patterns were in a dose-related fashion. CONCLUSION:These results might impart insights into the neural network mechanisms underlying anesthesia and suggest the potential of monitoring the depth of anesthesia based on the parameters of DMN.

Aims: Very high-frequency oscillations (VHFOs, >500 Hz) are considered a highly sensitive biomarker of seizures. We hypothesized that VHFOs may exhibit specificity towards hypersynchronous (HYP) seizures and low-voltage fast (LVF) seizures in temporal lobe epilepsy (TLE). Methods: Local field potentials were recorded from the hippocampal network in TLE mice induced by pilocarpine. Subsequently, we analyzed the VHFO features, including their temporal-frequency characteristics and VHFO/theta coupling, during three states: baseline, preictal, and postictal for both HYP-and LVF-seizure groups. Results: Significant changes in most of the VHFO features were observed during the preictal state in both seizure groups. In the postictal state, VHFO features in the HYP-seizure group exhibited inverse alterations and appeared to align with those observed during baseline conditions. However, such phenomena were not observed after TLE seizures in the LVF-seizure group. Conclusion: Our findings highlight distinct patterns of VHFO feature changes across different states of HYP seizures and LVF seizures. These results suggest that VHFOs could serve as indicative biomarkers for seizure alterations specifically associated with HYP-seizure states

Purpose Schizophrenia (SCH) is a severe psychiatric disorder associated with brain connectivity abnormalities, and early diagnosis can significantly reduce the burden on the families of the patients. Though several classification methods have been created to identify SCH a reliable method is yet to be found. In this study, we explore the performance of multidimensional spatial feature fusion in the recognition of schizophrenia.Materials and Methods Using an MRI connectomes dataset, we extract the spatial pattern network (SPN) and diffusion map embedding (DME) features from functional connectivity (FC) and structural connectivity (SC) networks of both schizophrenic patients and healthy subjects, and we use both single mode features and fused features to classify the two groups.Results Compared to the single mode features, the fused features showed superior performance in classification. By fusing the SPN and DME features of the structural network, we obtained the highest accuracy of 87.50%.Conclusion Multidimensional spatial feature fusion is promising as a reliable method for the recognition of schizophrenia.