Frontiers in Computational Neuroscience最新文献

Introduction: The onset of a sensory stimulus elicits transient bursts of activity in neural populations, which are presumed to convey information about the stimulus to downstream populations. Although the timing at which these synchronized bursts reach their peak is highly variable across stimulus presentations, the relative timing of bursts across interconnected brain regions may be less variable, particularly for regions that are strongly functionally coupled.

Methods: We developed a simple analytical framework that provides accurate trial-by-trial estimates of population burst times and of the correlations in the timing of evoked population bursts across areas. The method was evaluated using simulated data and compared to a recently published alternative model. We then applied the approach to large-scale Neuropixels recordings from six cortical visual areas and one visual thalamic nucleus in thirteen mice presented with drifting grating stimuli.

Results: Our method performed well on simulated data and was 85-90% faster than the alternative model while being substantially easier to apply. Applied to real data, the approach enabled identification of mouse-to-mouse variation in both peak times and region-to-region functional coupling for the first two population bursts following stimulus onset. The observed timing relationships were consistent with known anatomy and physiology.

Discussion: Examining sequences of activity across areas revealed that some timing relationships were preserved across all mice, while others varied across individuals. These findings demonstrate that the general approach can produce sensitive, trial-resolved analyses of timing relationships across neural populations and can capture both shared and individual-specific patterns of population burst propagation.

Long-range correlations are a key signature of systems operating near criticality, indicating spatially-extended interactions across large distances. These extended dependencies underlie other emergent properties of critical dynamics, such as high susceptibility and multi-scale coordination. In the brain, along with other signatures of criticality, long-range correlations have been observed across various spatial scales, suggesting that the brain may operate near a critical point to optimize information processing and adaptability. However, the mechanisms underlying these long-range correlations remain poorly understood. Here, we investigate the role of synergistic interactions in mediating long-range correlations in the visual cortex of awake mice. We leverage recent advances in mesoscale two-photon calcium imaging to analyse the activity of thousands of neurons across a wide field of view, allowing us to confirm the presence of long-range correlations at the level of neuronal populations. By applying the Partial Information Decomposition (PID) framework, we decompose the correlations into synergistic and redundant information interactions. Our results reveal that the increase in long-range correlations during visual stimulation is accompanied by a significant increase in synergistic rather than redundant interactions among neurons. Furthermore, we analyse a combined network formed by the union of synergistic and redundant interaction networks, and find that both types of interactions complement each other to facilitate efficient information processing across long distances. This complementarity is further enhanced during the visual stimulation. These findings provide new insights into the computational mechanisms that give rise to long-range correlations in neural systems and highlight the importance of considering different types of information interactions in understanding correlations in the brain.

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by atypical brain connectivity and impaired cognitive flexibility. Electroencephalography (EEG) based microstate analysis provides insight into the rapid temporal dynamics of brain networks, offering potential biomarkers for ASD.

Objective: This study proposes an interpretable classification framework for ASD diagnosis using multidomain microstate-informed features derived from EEG, integrating temporal, spectral, complexity-based, and higher-order metrics to comprehensively characterize brain dynamics.

Methods: Resting state EEG data from 56 participants (28 with ASD and 28 neurotypical controls; age range: 18-68 years) from the publicly available Sheffield dataset were preprocessed and segmented into microstates using a data-driven clustering approach. From these microstate sequences, we extracted a rich set of features across four domains: (i) temporal, (ii) spectral, (iii) temporal complexity, and (iv) higher-order metrics. Multiple classifiers were evaluated using 10-fold cross-validation, with hyperparameter tuning via a randomized search.

Results: Among all classifiers, XGBoost achieved the highest performance, with an accuracy of 80.87% when utilizing the complete multidomain feature set, significantly outperforming single domain models. Explainable AI analysis using SHapley Additive exPlanations (SHAP) identified the top 20 discriminative features, including fractional occupancy derivative for microstate 3, delta-band power in states 1 and 3, and mean inter-transition interval. Retraining XGBoost on these SHAP-selected features yielded 80.34% accuracy, confirming their robustness as potential biomarkers. Statistical validation via Mann-Whitney U-tests and effect size measures further established their significance.

Conclusion: The findings from the study demonstrated that microstate-informed features capturing temporal instability, transition unpredictability, and spectral alterations serve as clinically relevant and interpretable candidate neurophysiological markers of ASD, offering translational potential for objective diagnosis, treatment monitoring, and personalized interventions.

Introduction: Epilepsy is a chronic neurological disorder characterized by abnormal brain activity, often diagnosed through visual analysis of electroencephalography (EEG) signals. However, the existing works focused only on general epilepsy and failed to focus on location-based wave detection.

Methods: In this work, a novel deep learning-based EPIC-NET is proposed for epilepsy classification and brain localization using EEG signal. The EEG signals are fed into ResGoogleNet to extract both temporal and spatial features such as frequency variations, waveform morphology, and amplitude changes for epilepsy detection and localization of the affected brain regions. Stochastic Variance Reduced Gradient Langevin Dynamics based Honey Badger (SVGL-HBO) algorithm is utilized for feature selection effectively reducing dimensionality and retaining the most relevant features for detection. Based on the selected features, a fully connected layer classifies the normal and epilepsy. The Seizure Activity Index of epilepsy is classified into Low, Medium, and High using a Bell Elliptic Fuzzy Logic System (BE-FLS) guided by predefined fuzzy rules. The Optuna Wave-Gated Recurrent Unit (OW-GRU) combines GRU with wavelet processing to extract both temporal and frequency-domain features from EEG signals. Optuna is used for automatic hyperparameter tuning, which improves GRU performance, reduces overfitting, and enables accurate localization of epilepsy within specific brain lobes.

Results: The proposed EPIC-NET achieves the classification accuracy (CA) of 98.80% and Matthews Correlation Coefficient (MCC) of 97.43%.

Discussion: The EPIC-NET model improves the overall accuracy by 5.92, 10.02, and 0.59% better than RNN, SVM and CNN, respectively.

Neural oscillations have long been considered important markers of interaction across brain regions, yet identifying coordinated oscillatory activity from high-dimensional multiple-electrode recordings remains challenging. We sought to quantify time-varying covariation of oscillatory amplitudes across two brain regions, during a memory task, based on local field potentials recorded from 96 electrodes in each region. We extended Canonical Correlation Analysis (CCA) to multiple time series through the cross-correlation of latent time series. This, however, introduces a large number of possible lead-lag cross-correlations across the two regions. To manage that high dimensionality, we developed rigorous statistical procedures aimed at finding a small number of dominant lead-lag effects. The method correctly identified ground truth structure in realistic simulation-based settings. When we used it to analyze local field potentials recorded from the prefrontal cortex and visual area V4, we obtained highly plausible results. The new statistical methodology could also be applied to other slowly varying high-dimensional time series.

Introduction: Accurate determination of the progression phase of Alzheimer's disease (AD) is crucial for timely clinical decision-making, improved patient management, and personalized therapeutic interventions. However, reliably distinguishing between multiple disease stages using neuroimaging data remains a challenging task.

Methods: This study proposes an advanced machine learning framework for multi-stage AD classification using magnetic resonance imaging (MRI) data. The architecture follows a two-tier design. In the first stage, convolutional neural networks (CNNs) are employed to extract deep and discriminative feature representations from MRI images. In the second stage, these features are classified using ensemble learning models, specifically XGBoost and LightGBM. Metaheuristic optimization strategies are applied to further enhance model performance. The proposed framework was evaluated using a publicly available Alzheimer's disease dataset under three different experimental configurations.

Results: Experimental results demonstrate that the proposed approach effectively addresses the multi-class classification problem across different AD progression stages. The optimized models achieved a maximum classification accuracy of 89.55%, indicating robust predictive performance and strong generalization capability.

Discussion: To improve transparency and clinical relevance, explainable artificial intelligence (XAI) techniques were incorporated to interpret model predictions and highlight feature importance. The results provide meaningful insights into neuroimaging biomarkers associated with AD progression and support the development of more interpretable and trustworthy diagnostic systems. Overall, the proposed framework contributes to improved data-driven decision support and offers a promising direction for future Alzheimer's disease diagnosis and staging research.

Computational neuroscience has traditionally focused on isolated scales, limiting understanding of brain function across multiple levels. While microscopic models capture biophysical details of neurons, macroscopic models describe large-scale network dynamics. Integrating these scales, however, remains a significant challenge. In this study, we present a novel co-simulation framework that bridges these levels by integrating the neural simulator Arbor with The Virtual Brain (TVB) platform. Arbor enables detailed simulations from single-compartment neurons to populations of such cells, while TVB models whole-brain dynamics based on anatomical features and the mean neural activity of a brain region. By linking these simulators for the first time, we provide an example of how to model and investigate the onset of seizures in specific areas and their propagation to the whole brain. This framework employs an MPI intercommunicator for real-time bidirectional interaction, translating between discrete spikes from Arbor and continuous TVB activity. Its fully modular design enables independent model selection for each scale, requiring minimal effort to translate activity across simulators. The novel Arbor-TVB co-simulator allows replacement of TVB nodes with biologically realistic neuron populations, offering insights into seizure propagation and potential intervention strategies. The integration of Arbor and TVB marks a significant advancement in multi-scale modeling, providing a comprehensive computational framework for studying neural disorders and optimizing treatments.

Modularity is as a key characteristic of structural and functional brain networks across species and spatial scales. We investigate the stochastic Wilson-Cowan model on a modular network in which synaptic strengths differ between intra-module and inter-module connections. The system exhibits a rich phase diagram comprising symmetric (with low and high activity) and "broken symmetry" phases. Symmetric phases are characterized by the same low or high activity in all the modules, while the broken symmetry phases are characterized by a high activity in a subset of the modules and low activity in the remaining ones. There are two lines of critical points, the first between the low activity symmetric phase and the high activity symmetric phase, and the second between the low activity symmetric phase and a broken symmetry phase with one active module. At those lines the system shows a critical behavior, with power law distributions in the avalanches. Avalanche shapes differ systematically along the two lines: they are symmetric or right-skewed at the transition with the symmetric phase, but become left-skewed over intermediate durations along critical line with the broken symmetry phase. These results provide a theoretical framework that accounts for both symmetric and left-skewed neural avalanche shapes observed experimentally, linking modular organization to critical brain dynamics.

This study investigates the characteristics and underlying patterns of sports media audiences from a human-computer interaction (HCI) perspective using artificial intelligence-based deep learning analysis, with the aim of providing foundational data for the sports media industry. To this end, a novel unsupervised clustering framework, the Column-conditioned Prototype-Enhanced Deep Embedded Clustering (CoPE-DEC) technique, was employed to model and analyze multidimensional viewer experience data derived from sports media consumption contexts. The analysis identified three distinct audience clusters with differentiated behavioral, attitudinal, and value-oriented characteristics. The first cluster, labeled "Sports Value Orientation," was characterized by enhanced concentration during sports viewing, promotion of cooperative skills, motivation for health and exercise, vicarious satisfaction, aesthetic appreciation of sports movements, and admiration for athletes' professional and economic success. The second cluster, termed "Sports Consumption Culture Orientation," exhibited a strong preference for sports broadcasts over entertainment content, frequent consumption of online sports media, active engagement with preferred sports, participation in sports-related tourism and activities, acquisition of sports skills through media, and consumption of sports-related products. The third cluster, identified as "Sports Attitude Orientation," reflected predominantly social and emotional dimensions of sports viewing, including improved social adaptation, relationship formation, group cohesion, stress relief, psychological stabilization, healthy competitive attitudes, and enhanced overall wellbeing. These findings demonstrate that AI-driven deep learning approaches, particularly the CoPE-DEC framework, are effective in uncovering latent audience typologies and preference structures in sports media consumption environments. By integrating HCI principles with advanced clustering techniques, this study offers a methodological contribution to audience analysis research and provides practical implications for audience segmentation, personalized content design, and strategic decision-making in the sports media industry. Future research is encouraged to extend this approach by incorporating diverse AI methodologies and multimodal data sources to further advance interdisciplinary insights at the intersection of HCI, artificial intelligence, and sports media studies.