Frontiers in Computational Neuroscience最新文献

{"title":"Computational modelling reveals neurobiological contributions to static and dynamic functional connectivity patterns.","authors":"Linnea Hoheisel, Hannah Hacker, Gereon R Fink, Silvia Daun, Joseph Kambeitz","doi":"10.3389/fncom.2025.1525785","DOIUrl":"10.3389/fncom.2025.1525785","url":null,"abstract":"<p><p>Functional connectivity (FC) is a widely used indicator of brain function in health and disease, yet its neurobiological underpinnings still need to be firmly established. Recent advances in computational modelling allow us to investigate the relationship of both static FC (sFC) and dynamic FC (dFC) with neurobiology non-invasively. In this study, we modelled the brain activity of 200 healthy individuals based on empirical resting-state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data. Simulations were conducted using a group-averaged structural connectome and four parameters guiding global integration and local excitation-inhibition balance: (i) G, a global coupling scaling parameter; (ii) J _<i>i</i> , an inhibitory coupling parameter; (iii) J _<i>N</i> , the excitatory NMDA synaptic coupling parameter; and (iv) w _<i>p</i> , the excitatory population recurrence weight. For each individual, we optimised the parameters to replicate empirical sFC and temporal correlation (TC). We analysed associations between brain-wide sFC and TC features with optimal model parameters and fits with a univariate correlation approach and multivariate prediction models. In addition, we used a group-average perturbation approach to investigate the effect of coupling in each region on overall network connectivity. Our models could replicate empirical sFC and TC but not the FC variance or node cohesion (NC). Both fits and parameters exhibited strong associations with brain connectivity. G correlated positively and J _<i>N</i> negatively with a range of static and dynamic FC features (|<i>r</i>| > 0.2, p _<i>FDR</i> < 0.05). TC fit correlated negatively, and sFC fit positively with static and dynamic FC features. TC features were predictive of TC fit, sFC features of sFC fit (<i>R</i> ² > 0.5). Perturbation analysis revealed that the sFC fit was most impacted by coupling changes in the left paracentral gyrus (Δr = 0.07), TC fit by alterations in the left pars triangularis (Δr = 0.24). Our findings indicate that neurobiological characteristics are associated with individual variability in sFC and dFC, and that sFC and dFC are shaped by small sets of distinct regions. By modelling both sFC and dFC, we provide new evidence of the role of neurophysiological characteristics in establishing brain network configurations.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1525785"},"PeriodicalIF":2.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12339445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144834618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of IP₃ receptors and SERCA pumps in restoring working memory under amyloid β induced Alzheimer's disease: a modeling study.","authors":"Ziyi Huang, Lei Wang","doi":"10.3389/fncom.2025.1643547","DOIUrl":"10.3389/fncom.2025.1643547","url":null,"abstract":"<p><p>Memory impairment is a prevalent symptom in patients with Alzheimer's disease (AD), with working memory loss being the most prominent deficit. Recent experimental evidence suggests that abnormal calcium levels in the Endoplasmic Reticulum (ER) may disrupt synaptic transmission, leading to memory loss in AD patients. However, the specific mechanisms by which intracellular calcium homeostasis influences memory formation, storage, and recall in the context of AD remain unclear. In this study, we investigate the effects of intracellular calcium homeostasis on AD-related working memory (WM) using a spiking network model. We quantify memory storage by measuring the similarity between images during the training and testing phases. The model results indicate that ~90% of memory can be stored in the WM network under normal conditions. In contrast, the presence of amyloid beta (<i>A</i>β), associated with AD, significantly reduces this similarity, allowing only 54%-58% of memory to be stored, this alteration trend is consistent with previous experimental findings. Further analysis reveals that downregulating the activation of inositol triphosphate (<i>IP</i> ₃) receptors and upregulating the activation of the sarco-endoplasmic reticulum <i>Ca</i> ²⁺ ATPase (<i>SERCA</i>) pumps can enhance memory performance, achieving about 78% and 77%, respectively. Moreover, simultaneously manipulating both <i>IP</i> ₃ and <i>SERCA</i> activations can increase memory capacity to around 81%. These findings suggest several potential therapeutic targets for addressing memory impairment in <i>A</i>β aggregation induced AD patients. Additionally, our network model could serve as a foundation for exploring further mechanisms that modulate memory dysfunction at the genetic, cellular, and network levels.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1643547"},"PeriodicalIF":2.3,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource-dependent heterosynaptic spike-timing-dependent plasticity in recurrent networks with and without synaptic degeneration.","authors":"James Humble","doi":"10.3389/fncom.2025.1593837","DOIUrl":"10.3389/fncom.2025.1593837","url":null,"abstract":"<p><p>Many computational models that incorporate spike-timing-dependent plasticity (STDP) have shown the ability to learn from stimuli, supporting theories that STDP is a sufficient basis for learning and memory. However, to prevent runaway activity and potentiation, particularly within recurrent networks, additional global mechanisms are commonly necessary. A STDP-based learning rule, which involves local resource-dependent potentiation and heterosynaptic depression, is shown to enable stable learning in recurrent spiking networks. A balance between potentiation and depression facilitates synaptic homeostasis, and learned synaptic characteristics align with experimental observations. Furthermore, this resource-based STDP learning rule demonstrates an innate compensatory mechanism for synaptic degeneration.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1593837"},"PeriodicalIF":2.3,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12321831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144788599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural correspondence to spectrum of environmental uncertainty in multiple-cue probability judgment system with time delay.","authors":"Yoo-Sang Chang, Younho Seong, Sun Yi","doi":"10.3389/fncom.2025.1595278","DOIUrl":"10.3389/fncom.2025.1595278","url":null,"abstract":"<p><p>Despite state-of-the-art technologies like artificial intelligence, human judgment is critically essential in cooperative systems, such as the multi-agent system (MAS), which collect information among agents based on multiple-cue judgment. Human agents can prevent impaired situational awareness of automated agents by confirming situations under environmental uncertainty. System error caused by uncertainty can result in an unreliable system environment, and this environment affects the human agent, resulting in non-optimal decision-making in MAS. Thus, it is necessary to know how human behavior is changed to capture system reliability under uncertainty. Another issue affecting MAS is time delay, which can delay agent information transfer, resulting in low performance and instability. However, it is difficult to find studies on the influence of time delay on human agents. This study is about understanding the human decision-making process under a specific system reliability environment by uncertainty with time delay. We used concepts of expected and unexpected uncertainty to implement reliability of the system usage environment with three types of time delay conditions: no time delay, regular time delay, and irregular time delay conditions. We used electroencephalogram (EEG) for human cognitive neural mechanisms in multiple-cue judgment systems to understand human decision-making. In the reliability of system usage environment, the unreliable system environment significantly creates less memory load by less utilization of system rules for decision-making. In terms of time delay, delayed information delivery does not significantly affect memory load for decision-making.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1595278"},"PeriodicalIF":2.3,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12310571/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144759602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ModFus-PD: synergizing cross-modal attention and contrastive learning for enhanced multimodal diagnosis of Parkinson's disease.","authors":"Xiangze Teng, Xiang Li, Benzheng Wei","doi":"10.3389/fncom.2025.1604399","DOIUrl":"10.3389/fncom.2025.1604399","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by a high rate of misdiagnosis, underscoring the critical importance of early and accurate diagnosis. Although existing computer-aided diagnostic systems integrate clinical assessment scales with neuroimaging data, they typically rely on superficial feature concatenation, which fails to capture the deep inter-modal dependencies essential for effective multimodal fusion. To address this limitation, we propose ModFus-PD, Contrastive learning effectively aligns heterogeneous modalities such as imaging and clinical text, while the cross-modal attention mechanism further exploits semantic interactions between them to enhance feature fusion. The framework comprises three key components: (1) a contrastive learning-based feature alignment module that projects MRI data and clinical text prompts into a unified embedding space via pretrained image and text encoders; (2) a bidirectional cross-modal attention module in which textual semantics guide MRI feature refinement for improved sensitivity to PD-related brain regions, while MRI features simultaneously enhance the contextual understanding of clinical text; (3) a hierarchical classification module that integrates the fused representations through two fully connected layers to produce final PD classification probabilities. Experiments on the PPMI dataset demonstrate the superior performance of ModFus-PD, achieving an accuracy of 0.903, AUC of 0.892, and F1 score of 0.840, surpassing several state-of-the-art baselines. These results validate the effectiveness of our cross-modal fusion strategy, which enables interpretable and reliable diagnostic support, holding promise for future clinical translation.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1604399"},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12307351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144752839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"System-level brain modeling.","authors":"Birger Johansson, Trond A Tjøstheim, Christian Balkenius","doi":"10.3389/fncom.2025.1607239","DOIUrl":"10.3389/fncom.2025.1607239","url":null,"abstract":"<p><p>System-level brain modeling is a powerful method for building computational models of the brain and allows biologically motivated models to produce measurable behavior that can be tested against empirical data. System-level brain models occupy an intermediate position between detailed neuronal circuit models and abstract cognitive models. They are distinguished by their structural and functional resemblance to the brain, while also allowing for thorough testing and evaluation. In designing system-level brain models, several questions need to be addressed. What are the components of the system? At what level should these components be modeled? How are the components connected-that is, what is the structure of the system? What is the function of each component? What kind of information flows between the components, and how is that information coded? We mainly address models of cognitive abilities or subsystems that produce measurable behavior rather than models that to reproduce internal states, signals or activation patterns. In this method paper, we argue that system-level modeling is an excellent method for addressing complex cognitive and behavioral phenomena.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1607239"},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12307420/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144752840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The cognitive impacts of large language model interactions on problem solving and decision making using EEG analysis.","authors":"Ting Jiang, Jihua Wu, Stephen C H Leung","doi":"10.3389/fncom.2025.1556483","DOIUrl":"10.3389/fncom.2025.1556483","url":null,"abstract":"<p><strong>Introduction: </strong>The increasing integration of large language models (LLMs) into human-AI collaboration necessitates a deeper understanding of their cognitive impacts on users. Traditional evaluation methods have primarily focused on task performance, overlooking the underlying neural dynamics during interaction.</p><p><strong>Methods: </strong>In this study, we introduce a novel framework that leverages electroencephalography (EEG) signals to assess how LLM interactions affect cognitive processes such as attention, cognitive load, and decision-making. Our framework integrates an Interaction-Aware Language Transformer (IALT), which enhances token-level modeling through dynamic attention mechanisms, and an Interaction-Optimized Reasoning Strategy (IORS), which employs reinforcement learning to refine reasoning paths in a cognitively aligned manner.</p><p><strong>Results: </strong>By coupling these innovations with real-time neural data, the framework provides a fine-grained, interpretable assessment of LLM-induced cognitive changes. Extensive experiments on four benchmark EEG datasets Database for Emotion Analysis using Physiological Signals (DEAP), A Dataset for Affect, Personality and Mood Research on Individuals and Groups (AMIGOS), SJTU Emotion EEG Dataset (SEED), and Database for Emotion Recognition through EEG and ECG Signals (DREAMER) demonstrate that our method outperforms existing models in both emotion classification accuracy and alignment with cognitive signals. The architecture maintains high performance across varied EEG configurations, including low-density, noise-prone portable systems, highlighting its robustness and practical applicability.</p><p><strong>Discussion: </strong>These findings offer actionable insights for designing more adaptive and cognitively aware LLM systems, and open new avenues for research at the intersection of artificial intelligence and neuroscience.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1556483"},"PeriodicalIF":2.3,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12307350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144752841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical computation for segmentation and classification of lumbar vertebral fractures.","authors":"Roseline Nyange, Hemachandran Kannan, Channabasava Chola, Saurabh Singh, Jaejeung Kim, Anil Audumbar Pise","doi":"10.3389/fncom.2025.1536441","DOIUrl":"10.3389/fncom.2025.1536441","url":null,"abstract":"<p><p>Spinal health forms the cornerstone of the overall human body functionality with the lumbar spine playing a critical role and prone to various types of injuries due to inflammation and diseases, including lumbar vertebral fractures. This paper proposes automated method for segmentation of lumbar vertebral body (VB) using image processing techniques such as shape features and morphological operations. This entails an initial phase of image preprocessing, followed by detection and localizing of vertebral regions. Subsequently, vertebral are segmented and labeled, with each classified into normal or fractured using classification techniques, k-nearest neighbors (KNN) and support vector machines (SVM). The methodology leverages unique vertebral characteristics like gray scales, shape features, and textural elements through a range of machine learning methods. The approach is assessed and validated on a clinical spine dataset dice score used for segmentation, achieving an average accuracy rate of 95%, and for classification, achieving average accuracy of 97.01%.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1536441"},"PeriodicalIF":2.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12287019/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DTCNet: finger flexion decoding with three-dimensional ECoG data.","authors":"Fufeng Wang, Zihe Luo, Wei Lv, XiaoLin Zhu","doi":"10.3389/fncom.2025.1627819","DOIUrl":"10.3389/fncom.2025.1627819","url":null,"abstract":"<p><p>ECoG signals are widely used in Brain-Computer Interfaces (BCIs) due to their high spatial resolution and superior signal quality, particularly in the field of neural control. ECoG enables more accurate decoding of brain activity compared to traditional EEG. By obtaining cortical ECoG signals directly from the cerebral cortex, complex motor commands, such as finger movement trajectories, can be decoded more efficiently. However, existing studies still face significant challenges in accurately decoding finger movement trajectories. Specifically, current models tend to confuse the movement information of different fingers and fail to fully exploit the dependencies within time series when predicting long sequences, resulting in limited decoding performance. To address these challenges, this paper proposes a novel decoding method that transforms 2D ECoG data samples into 3D spatio-temporal spectrograms with time-stamped features via wavelet transform. The method further enables accurate decoding of finger bending by using a 1D convolutional network composed of Dilated-Transposed convolution, which together extract channel band features and temporal variations in tandem. The proposed method achieved the best performance among three subjects in BCI Competition IV. Compared with existing studies, our method made the correlation coefficient between the predicted multi-finger motion trajectory and the actual multi-finger motion trajectory exceed 80% for the first time, with the highest correlation coefficient reaching 82%. This approach provides new insights and solutions for high-precision decoding of brain-machine signals, particularly in precise command control tasks, and advances the application of BCI systems in real-world neuroprosthetic control.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1627819"},"PeriodicalIF":2.1,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling autonomous shifts between focus state and mind-wandering using a predictive-coding-inspired variational recurrent neural network.","authors":"Henrique Oyama, Takazumi Matsumoto, Jun Tani","doi":"10.3389/fncom.2025.1578135","DOIUrl":"10.3389/fncom.2025.1578135","url":null,"abstract":"<p><p>Mind-wandering reflects a dynamic interplay between focused attention and off-task mental states. Despite its relevance in understanding fundamental cognitive processes, such as attention regulation, decision-making, and creativity, previous models have not yet provided an account of the neural mechanisms for autonomous shifts between focus state (FS) and mind-wandering (MW). To address this, we conduct model simulation experiments employing predictive coding as a theoretical framework of perception to investigate possible neural mechanisms underlying these autonomous shifts between the two states. In particular, we modeled perception processes of continuous sensory sequences using our previously proposed variational RNN model under free energy minimization. The current study extends this model by introducing an online adaptation mechanism of a meta-level parameter, referred to as the meta-prior <b>w</b>, which regulates the complexity term in the free energy minimization. Our simulation experiments demonstrated that autonomous shifts between FS and MW take place when <b>w</b> switches between low and high values responding to a decrease and increase of the average reconstruction error over a past time window. Particularly, high <b>w</b> prioritized top-down predictions while low <b>w</b> emphasized bottom-up sensations. In this work, we speculate that self-awareness of MW may occur when the error signal accumulated over time exceeds a certain threshold. Finally, this paper explores how our experiment results align with existing studies and highlights their potential for future research.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"19 ","pages":"1578135"},"PeriodicalIF":2.1,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263957/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144648974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}