工程技术最新文献

{"title":"Baicalein Alleviates Lithium-Pilocarpine-Induced Status Epilepticus by Regulating DNMT1/GABRD Pathway in Rats.","authors":"Zhenggang Wu, Jing Liu, Deju Yin, Jing Huang, Yujing Huang, Pengfei Wang","doi":"10.1080/15476278.2025.2519607","DOIUrl":"10.1080/15476278.2025.2519607","url":null,"abstract":"<p><strong>Background: </strong>Epilepsy is a common disease of the nervous system. Recent advances in epigenetics have revealed DNA methylation as a key mechanism in epilepsy pathogenesis, particularly through dysregulation of GABAergic signaling. Baicalein has been shown to have anticonvulsant and neuroprotective effects. However, its epigenetic regulatory effects on GABA receptor function remain unexplored.</p><p><strong>Methods: </strong>The status epilepticus (SE) model was induced by lithium chloride-pilocarpine (LiCl-PILO) in Sprague-Dawley (SD) rats. The rats were divided into control group, epileptic SE group and baicalein intervention group. Morris water maze (MWM) test, Nissl staining, immunofluorescence and enzyme-linked immunosorbent assay (ELISA) were used to detect cognitive functions and neuronal damage. Online sites, chromatin immunoprecipitation (ChIP) and western blotting were used to identify DNA methyltransferase 1 (DNMT1)-mediated methylation of gamma-aminobutyric acid type A receptor subunit delta (GABRD) promoter region.</p><p><strong>Results: </strong>Baicalein treatment significantly prolonged the latency of SE onset and seizure onset, and improved the development of epilepsy. Meanwhile, baicalein improved the cognitive impairment in rats induced by LiCl-PILO. After treatment with baicalein, a sustained elevation in the number of neurons and NeuN levels was observed, along with a decrease in the contents of tumor necrosis factor -alpha (TNF-α), interleukin-1β (IL-1β), and ionized calcium-binding adapter molecule 1 (Iba-1) in the hippocampus. Mechanistically, baicalein interacted with DNMT1 to suppress GABRD promoter region methylation, thus increasing GABRD protein level in the hippocampus of rats induced by LiCl-PILO.</p><p><strong>Conclusion: </strong>This study identifies DNMT1/GABRD axis as a novel epigenetic target for epilepsy intervention. Baicalein's ability to enhance tonic inhibition through demethylation of GABRD provides a groundbreaking strategy for drug-resistant epilepsy.</p>","PeriodicalId":19596,"journal":{"name":"Organogenesis","volume":"21 1","pages":"2519607"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12203850/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144497584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hippocampal navigation model through hierarchical memory organization.","authors":"Fei Song, Jinyu Li, Fenzhen Tang, Yandong Tang, Bailu Si","doi":"10.1007/s11571-025-10254-w","DOIUrl":"10.1007/s11571-025-10254-w","url":null,"abstract":"<p><p>Animals in nature exhibit exceptional navigational abilities, primarily due to the hippocampus's capacity to form and utilize spatial and non-spatial memories. However, existing models often fail to accurately capture the dynamic interplay between different hippocampal regions. This study presents a unified navigation model inspired by the functional interactions between the hippocampus and surrounding neural circuits, with a focus on the transition mechanisms between vector-based navigation, controlled by grid cells, and hierarchical memory-based navigation, coordinated by the ventral-dorsal hippocampal axis. Simulations show that the model effectively replicates complex path-planning behaviors, such as robust direction selection and efficient shortcut finding, similar to those observed in advanced animals. Furthermore, simulations of hippocampal lesions indicate that ventral lesions increase cognitive load without disrupting planned paths, while dorsal lesions cause additional trajectory oscillations due to impaired spatial memory recall. These findings provide new insights into hippocampal navigation strategies and suggest potential applications for studying memory, learning, and cognitive function across various contexts.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10254-w.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"103"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144526729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mifnet: a MamBa-based interactive frequency convolutional neural network for motor imagery decoding.","authors":"Luoqian Yang, Weina Zhu","doi":"10.1007/s11571-025-10287-1","DOIUrl":"10.1007/s11571-025-10287-1","url":null,"abstract":"<p><p>Motor imagery (MI) decoding remains a critical challenge in brain-computer interface (BCI) systems due to the low signal-to-noise ratio, non-stationarity, and complex spatiotemporal dynamics of electroencephalography (EEG) signals. Although deep learning architectures have advanced MI-EEG decoding, existing approaches-including convolutional neural networks (CNNs), Transformers, and recurrent neural networks (RNNs)-still face limitations in capturing global temporal dependencies, maintaining positional coherence, and ensuring computational efficiency. To address these challenges, we propose MIFNet, a MamBa-based Interactive Frequency Convolutional Neural Network that systematically integrates spectral, spatial, and temporal feature extraction. Specifically, MIFNet incorporates: non-overlapping frequency decomposition, which selectively extracts motor imagery-related mu (8-12 Hz) and beta (12-32 Hz) rhythms; a ConvEncoder module, which autonomously learns to fuse spectral-spatial features from both frequency bands; and a MamBa-based temporal module, leveraging selective state-space models (SSMs) to efficiently capture long-range dependencies with linear complexity. Extensive experiments on three public MI-EEG datasets (BCIC-IV-2A, OpenBMI, and High Gamma) demonstrate that MIFNet outperforms existing models, achieving an average classification accuracy improvement of 12.3%, 8.3%, 4.7%, and 5.5% over EEGNet, FBCNet, IFNet, and Conformer, respectively. Ablation studies further validate the necessity of each component, with the MamBa module contributing a 5.5% improvement in accuracy on the BCIC-IV-2A dataset. Moreover, MIFNet exhibits strong generalization performance in cross-validation settings, establishing a robust foundation for real-time BCI applications. Our findings highlight the potential of hybridizing CNNs with state-space models (SSMs) for improving EEG decoding performance, effectively bridging the gap between localized feature extraction and global temporal modeling.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"106"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Alterations of synaptic plasticity and brain oscillation are associated with autophagy induced synaptic pruning during adolescence.","authors":"Hui Wang, Xiaxia Xu, Zhuo Yang, Tao Zhang","doi":"10.1007/s11571-024-10185-y","DOIUrl":"10.1007/s11571-024-10185-y","url":null,"abstract":"<p><p>Adolescent brain development is characterized by significant anatomical and physiological alterations, but little is known whether and how these alterations impact the neural network. Here we investigated the development of functional networks by measuring synaptic plasticity and neural synchrony of local filed potentials (LFPs), and further explored the underlying mechanisms. LFPs in the hippocampus were recorded in young (21 ~ 25 days), adolescent (1.5 months) and adult (3 months) rats. Long term potentiation (LTP) and neural synchrony were analyzed. The results showed that the LTP was the lowest in adolescent rats. During development, the theta coupling strength was increased progressively but there was no significant change of gamma coupling between young rats and adolescent rats. The density of dendrite spines was decreased progressively during development. The lowest levels of NR2A, NR2B and PSD95 were detected in adolescent rats. Importantly, it was found that the expression levels of autophagy markers were the highest during adolescent compared to that in other developmental stages. Moreover, there were more co-localization of autophagosome and PSD95 in adolescent rats. It suggests that autophagy is possibly involved in synaptic elimination during adolescence, and further impacts synaptic plasticity and neural synchrony.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"2"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beta-band oscillations and spike-local field potential synchronization in the motor cortex are correlated with movement deficits in an exercise-induced fatigue mouse model.","authors":"Xudong Zhao, Hualin Wang, Ke Li, Shanguang Chen, Lijuan Hou","doi":"10.1007/s11571-024-10182-1","DOIUrl":"10.1007/s11571-024-10182-1","url":null,"abstract":"<p><p>Fatigue, a complex and multifaceted symptom, profoundly influences quality of life, particularly among individuals suffering from chronic medical conditions or neurological disorders. This symptom not only exacerbates existing conditions but also hinders daily functioning, thereby perpetuating a vicious cycle of worsening symptoms and reduced physical activity. Given the pivotal role of the motor cortex (M1) in coordinating and executing voluntary movements, understanding how the cortex regulates fatigue is crucial. Despite its importance, the neural mechanisms underlying fatigue remain inadequately explored. In this study, we employed electrophysiological recordings in the M1 region of mice to investigate how excitation-inhibition dynamics and neural oscillations are regulated during exercise-induced fatigue. We observed that fatigue led to decreased voluntary physical activity and cognitive performance, manifesting as reduced running wheel distance, mean speed, exercise intensity, and exploratory behaviour. At the neural level, we detected increased firing frequencies for M1 neurons, including both pyramidal neurons and interneurons, along with heightened beta-band oscillatory activity and stronger coupling between beta-band oscillations and interneurons. These findings enhance our understanding of the mechanisms underlying fatigue, offering insights into behavioural, excitability, and oscillatory changes. The results of this study could pave the way for the development of novel intervention strategies to combat fatigue.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"3"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11688262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142920741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG-based cross-subject passive music pitch perception using deep learning models.","authors":"Qiang Meng, Lan Tian, Guoyang Liu, Xue Zhang","doi":"10.1007/s11571-024-10196-9","DOIUrl":"https://doi.org/10.1007/s11571-024-10196-9","url":null,"abstract":"<p><p>Pitch plays an essential role in music perception and forms the fundamental component of melodic interpretation. However, objectively detecting and decoding brain responses to musical pitch perception across subjects remains to be explored. In this study, we employed electroencephalography (EEG) as an objective measure to obtain the neural responses of musical pitch perception. The EEG signals from 34 subjects under hearing violin sounds at pitches G3 and B6 were collected with an efficient passive Go/No-Go paradigm. The lightweight modified EEGNet model was proposed for EEG-based pitch classification. Specifically, within-subject modeling with the modified EEGNet model was performed to construct individually optimized models. Subsequently, based on the within-subject model pool, a classifier ensemble (CE) method was adopted to construct the cross-subject model. Additionally, we analyzed the optimal time window of brain decoding for pitch perception in the EEG data and discussed the interpretability of these models. The experiment results show that the modified EEGNet model achieved an average classification accuracy of 77% for within-subject modeling, significantly outperforming other compared methods. Meanwhile, the proposed CE method achieved an average accuracy of 74% for cross-subject modeling, significantly exceeding the chance-level accuracy of 50%. Furthermore, we found that the optimal EEG data window for the pitch perception lies 0.4 to 0.9 s onset. These promising results demonstrate that the proposed methods can be effectively used in the objective assessment of pitch perception and have generalization ability in cross-subject modeling.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"6"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11699146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142930820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sg-snn: a self-organizing spiking neural network based on temporal information.","authors":"Shouwei Gao, Ruixin Zhu, Yu Qin, Wenyu Tang, Hao Zhou","doi":"10.1007/s11571-024-10199-6","DOIUrl":"10.1007/s11571-024-10199-6","url":null,"abstract":"<p><p>Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network. The TSO method incorporates information from multiple time steps into the selection strategy of the Best Matching Unit (BMU) neurons. It enables the coupled BMUs to radiate the weight across the same layer of neurons, ultimately forming a hierarchical self-organizing topographic map of concern. Additionally, we simulate real neuronal dynamics, introduce a glial cell-mediated Glial-LIF (Leaky Integrate-and-fire) model, and adjust multiple levels of BMUs to optimize the attention topological map.Experiments demonstrate that the proposed Self-organizing Glial Spiking Neural Network (SG-SNN) can generate attention topographies for dynamic event data from coarse to fine. A heuristic method based on cognitive science effectively guides the network's distribution of excitatory regions. Furthermore, the SG-SNN shows improved accuracy on three standard neuromorphic datasets: DVS128-Gesture, CIFAR10-DVS, and N-Caltech 101, with accuracy improvements of 0.3%, 2.4%, and 0.54% respectively. Notably, the recognition accuracy on the DVS128-Gesture dataset reaches 99.3%, achieving state-of-the-art (SOTA) performance.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"14"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11718035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visual image reconstructed without semantics from human brain activity using linear image decoders and nonlinear noise suppression.","authors":"Qiang Li","doi":"10.1007/s11571-024-10184-z","DOIUrl":"10.1007/s11571-024-10184-z","url":null,"abstract":"<p><p>In recent years, substantial strides have been made in the field of visual image reconstruction, particularly in its capacity to generate high-quality visual representations from human brain activity while considering semantic information. This advancement not only enables the recreation of visual content but also provides valuable insights into the intricate processes occurring within high-order functional brain regions, contributing to a deeper understanding of brain function. However, considering fusion semantics in reconstructing visual images from brain activity involves semantic-to-image guide reconstruction and may ignore underlying neural computational mechanisms, which does not represent true reconstruction from brain activity. In response to this limitation, our study introduces a novel approach that combines linear mapping with nonlinear noise suppression to reconstruct visual images perceived by subjects based on their brain activity patterns. The primary challenge associated with linear mapping lies in its susceptibility to noise interference. To address this issue, we leverage a flexible denoised deep convolutional neural network, which can suppress noise from linear mapping. Our investigation encompasses linear mapping as well as the training of shallow and deep autoencoder denoised neural networks, including a pre-trained, state-of-the-art denoised neural network. The outcome of our study reveals that combining linear image decoding with nonlinear noise reduction significantly enhances the quality of reconstructed images from human brain activity. This suggests that our methodology holds promise for decoding intricate perceptual experiences directly from brain activity patterns without semantic information. Moreover, the model has strong neural explanatory power because it shares structural and functional similarities with the visual brain.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"20"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11718044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A fuzzy based computational model to analyze the influence of mitochondria, buffer, and ER fluxes on cytosolic calcium distribution in neuron cells.","authors":"Rituparna Bhattacharyya, Brajesh Kumar Jha","doi":"10.1007/s11571-024-10212-y","DOIUrl":"10.1007/s11571-024-10212-y","url":null,"abstract":"<p><p>A free calcium ion in the cytosol is essential for many physiological and physical functions. Also, it is known as a second messenger as the quantity of free calcium ions is an essential part of brain signaling. In this work, we have attempted to study calcium signaling in the presence of mitochondria, buffer, and endoplasmic reticulum fluxes. Small organelles called mitochondria are found in the nervous system and are involved in several cellular functions, including energy production, response to stress, calcium homeostasis regulation, and pathways leading to cell death. It has been discovered that buffer, endoplasmic reticulum, and mitochondria significantly affect calcium signaling. To investigate how various circumstances impact the quantity of calcium in the cytosol, a mathematical model of a second-order linear partial differential equation with fuzzy boundary conditions has been developed. Systems having ambiguous or imprecise boundary values can be effectively modeled and simulated with the help of fuzzy boundary conditions. Models can provide more dependable and instructive outcomes and become adaptable to real-world circumstances by implementing fuzzy logic into boundary conditions. In this paper, we observed the Fuzzy Laplace Transform to solve variable coefficient fuzzy differential equations using triangular fuzzy numbers. It is noted that maintaining the delicate calcium ion balance, which controls essential cellular functions, depends on the buffer affinity. Also, neurodegenerative illnesses like Alzheimer's, Parkinson's, etc. are linked to disruptions in the control of components such as buffers, mitochondria, and the endoplasmic reticulum.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"25"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1080/21691401.2025.2476906","DOIUrl":"https://doi.org/10.1080/21691401.2025.2476906","url":null,"abstract":"","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"104"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}