Frontiers in Neuroscience最新文献

{"title":"Correction: Src inhibition attenuates neuroinflammation and protects dopaminergic neurons in Parkinson's disease models.","authors":"Hanyu Yang, Lu Wang, Caixia Zang, Yue Wang, Junmei Shang, Zihong Zhang, Hui Liu, Xiuqi Bao, Xiaoliang Wang, Dan Zhang","doi":"10.3389/fnins.2025.1640774","DOIUrl":"https://doi.org/10.3389/fnins.2025.1640774","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fnins.2020.00045.].</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1640774"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12517003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel fast detection algorithm for depression based on 3-channel EEG signals.","authors":"XiWu Guo, ZiHan Guo, TaoLi Xie","doi":"10.3389/fnins.2025.1651762","DOIUrl":"10.3389/fnins.2025.1651762","url":null,"abstract":"<p><p>Medically unexplained symptoms (MUS) are an emerging field in current research. Among middle-aged and elderly patients, most MUS symptoms are mainly caused by depression, but early symptoms do not meet the international somatization standards, which delays treatment. Therefore, developing a rapid auxiliary diagnosis method is of great significance. This paper proposes a novel model for identifying depression based on 3-channel electroencephalogram (EEG) signals from the prefrontal lobe of the human brain. For the collected resting-state EEG signals, variational mode decomposition (VMD) is first used for signal decomposition, and the power spectrum is employed to select intrinsic mode function (IMF) components. After extracting energy features via sample entropy, LightGBM is adopted for classification, with a classification accuracy of 97.42%. Through comparative experiments, the model proposed in this paper achieves a balance between high accuracy and timeliness. This is conducive to the development of a depression detection system based on portable real-time electroencephalography (EEG), and provides a solution for EEG signal devices in real-time depression detection and pre-triage of patients with Medically Unexplained Symptoms (MUS).</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1651762"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515845/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291909","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GenCPM: a toolbox for generalized connectome-based predictive modeling.","authors":"Baijia Xu, Shengxian Ding, Wanwan Xu, Carolyn Fredericks, Yize Zhao","doi":"10.3389/fnins.2025.1627497","DOIUrl":"10.3389/fnins.2025.1627497","url":null,"abstract":"<p><p>Understanding brain-behavior relationships and predicting cognitive and clinical outcomes from neuromarkers are central tasks in neuroscience. Connectome-based Predictive Modeling (CPM) has been widely adopted to predict behavioral traits from brain connectivity data; however, existing implementations are largely restricted to continuous outcomes, often overlook essential non-imaging covariates, and are difficult to apply in clinical or disease cohort settings. To address these limitations, we present GenCPM, a generalized CPM framework implemented in open-source R software. GenCPM extends traditional CPM by supporting binary, categorical, and time-to-event outcomes and allows the integration of covariates such as demographic and genetic information, thereby improving predictive accuracy and interpretability. To handle high-dimensional data, GenCPM incorporates marginal screening and regularized regression techniques, including LASSO, ridge, and elastic net, for efficient selection of informative brain connections. We demonstrate the utility of GenCPM through analyses of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) Study and the Alzheimer's Disease Neuroimaging Initiative (ADNI), showing enhanced predictive performance and improved signal attribution compared to standard methods. GenCPM offers a flexible, scalable, and interpretable solution for predictive modeling in brain connectivity research, supporting broader applications in cognitive and clinical neuroscience.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1627497"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515856/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the correlation between serum vitamin D and hypothalamic-pituitary-adrenal axis hormone levels in patients with post-traumatic stress disorder.","authors":"Lei Ge, Weihua Xu, Wencong Liu, Panpan Cui, Lei Zhang, Hui Ju","doi":"10.3389/fnins.2025.1622978","DOIUrl":"10.3389/fnins.2025.1622978","url":null,"abstract":"<p><strong>Objective: </strong>Post-Traumatic Stress Disorder (PTSD) is a psychological disorder triggered by extreme traumatic events. It is characterized by impaired cognitive function and neuroendocrine dysfunction, particularly dysregulation of the hypothalamic-pituitary-adrenal axis. In recent years, the role of vitamin D in neuroprotection and cognitive function has garnered increasing interest; however, its relationship with hypothalamic-pituitary-adrenal (HPA) axis hormone levels in patients with post-traumatic stress disorder (PTSD) remains poorly understood.</p><p><strong>Methods: </strong>This study aimed to investigate the correlation between serum vitamin D levels and HPA axis hormones in patients with PTSD. A total of 96 patients with severe trauma admitted to Rizhao People's Hospital between March 2022 and December 2024 were enrolled and categorized into PTSD and non-PTSD groups according to diagnostic criteria. PTSD symptoms were evaluated using the PTSD Checklist-Civilian Version. Serum levels of 25-hydroxyvitamin D, corticotropin-releasing hormone, adrenocorticotropic hormone, and cortisol were measured. Spearman's correlation analysis and receiver operating characteristic curves were employed to assess associations between vitamin D, HPA axis biomarkers, and PCL-C Scores.</p><p><strong>Results: </strong>The results showed that serum 25-hydroxyvitamin D levels were significantly lower in the PTSD group compared to the non-PTSD group (<i>P</i> < 0.001), while CRH and ACTH levels were significantly higher, and cortisol levels were significantly lower (<i>P</i> < 0.001). Spearman correlation analysis indicated that vitamin D levels were negatively correlated with CRH and ACTH levels and positively correlated with cortisol levels (<i>P</i> < 0.05). ROC curve analysis revealed that serum 25-hydroxyvitamin D levels have diagnostic potential for PTSD, with a cutoff value of 16.32 ng/mL, an AUC of 0.698, sensitivity of 86.2%, and specificity of 51.1%.</p><p><strong>Conclusion: </strong>This study demonstrated a correlation between serum vitamin D levels and HPA axis hormone levels in patients with PTSD, suggesting that vitamin D deficiency may be associated with HPA axis dysregulation in PTSD. These findings underscore a potential link between vitamin D deficiency and PTSD, warranting further investigation into the role of vitamin D in the disorder's pathophysiology and its potential as a therapeutically modifiable factor.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1622978"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515925/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A time-frequency feature fusion-based deep learning network for SSVEP frequency recognition.","authors":"Yiwei Dai, Zhengkui Chen, Tian-Ao Cao, Hongyou Zhou, Min Fang, Yanyun Dai, Lurong Jiang, Jijun Tong","doi":"10.3389/fnins.2025.1679451","DOIUrl":"10.3389/fnins.2025.1679451","url":null,"abstract":"<p><strong>Introduction: </strong>Steady-state visual evoked potential (SSVEP) has emerged as a pivotal branch in brain-computer interfaces (BCIs) due to its high signal-to-noise ratio (SNR) and elevated information transfer rate (ITR). However, substantial inter-subject variability in electroencephalographic (EEG) signals poses a significant challenge to current SSVEP frequency recognition. In particular, it is difficult to achieve high cross-subject classification accuracy in calibration-free scenarios, and the classification performance heavily depends on extensive calibration data.</p><p><strong>Methods: </strong>To mitigate the reliance on large calibration datasets and enhance cross-subject generalization, we propose SSVEP time-frequency fusion network (SSVEP-TFFNet), an improved deep learning network fusing time-domain and frequency-domain features dynamically. The network comprises two parallel branches: a time-domain branch that ingests raw EEG signals and a frequency-domain branch that processes complex-spectrum features. The two branches extract the time-domain and frequency-domain features, respectively. Subsequently, these features are fused via a dynamic weighting mechanism and input to the classifier. This fusion strategy strengthens the feature expression ability and generalization across different subjects.</p><p><strong>Results: </strong>Cross-subject classification was conducted on publicly available 12-class and 40-class SSVEP datasets. We also compared SSVEP-TFFNet with traditional approaches and principal deep learning methods. Results demonstrate that SSVEP-TFFNet achieves an average classification accuracy of 89.72% on the 12-class dataset, surpassing the best baseline method by 1.83%. SSVEP-TFFNet achieves average classification accuracies of 72.11 and 82.50% (40-class datasets), outperforming the best controlled method by 7.40 and 6.89% separately.</p><p><strong>Discussion: </strong>The performance validates the efficacy of dynamic time-frequency feature fusion and our proposed method provides a new paradigm for calibration-free SSVEP-based BCI systems.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1679451"},"PeriodicalIF":3.2,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145291957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Graph neural networks in Alzheimer's disease diagnosis: a review of unimodal and multimodal advances.","authors":"Shahzad Ali, Michele Piana, Matteo Pardini, Sara Garbarino","doi":"10.3389/fnins.2025.1623141","DOIUrl":"10.3389/fnins.2025.1623141","url":null,"abstract":"<p><p>Alzheimer's Disease (AD), a leading neurodegenerative disorder, presents significant global health challenges. Advances in graph neural networks (GNNs) offer promising tools for analyzing multimodal neuroimaging data to improve AD diagnosis. This review provides a comprehensive overview of GNN applications in AD diagnosis, focusing on data sources, modalities, sample sizes, classification tasks, and diagnostic performance. Drawing on extensive literature searches across PubMed, IEEE Xplorer, Scopus, and Springer, we analyze key GNN frameworks and critically evaluate their limitations, challenges, and opportunities for improvement. In addition, we present a comparative analysis to evaluate the generalizability and robustness of GNN methods across different datasets, such as ADNI, OASIS, TADPOLE, UK Biobank, in-house, etc. Furthermore, we provide a critical methodological comparison across families of GNN architectures (i.e., GCN, ChebNet, GraphSAGE, GAT, GIN, etc.) in the context of AD. Finally, we outline future research directions to refine GNN-based diagnostic methods and highlight their potential role in advancing AI-driven neuroimaging solutions. Our findings aim to foster the integration of AI technologies in neurodegenerative disease research and clinical practice.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1623141"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511118/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient spiking convolutional neural networks accelerator with multi-structure compatibility.","authors":"Jiadong Wu, Lun Lu, Yinan Wang, Zhiwei Li, Changlin Chen, Qingjiang Li, Kairang Chen","doi":"10.3389/fnins.2025.1662886","DOIUrl":"10.3389/fnins.2025.1662886","url":null,"abstract":"<p><p>Spiking Neural Networks (SNNs) possess excellent computational energy efficiency and biological credibility. Among them, Spiking Convolutional Neural Networks (SCNNs) have significantly improved performance, demonstrating promising applications in low-power and brain-like computing. To achieve hardware acceleration for SCNNs, we propose an efficient FPGA accelerator architecture with multi-structure compatibility. This architecture supports both traditional convolutional and residual topologies, and can be adapted to diverse requirements from small networks to complex networks. This architecture uses a clock-driven scheme to perform convolution and neuron updates based on the spike-encoded image at each timestep. Through hierarchical pipelining and channel parallelization strategies, the computation speed of SCNNs is increased. To address the issue of current accelerators only supporting simple network, this architecture combines configuration and scheduling methods, including grouped reuse computation and line-by-line multi-timestep computation to accelerate deep networks with lots of channels and large feature map sizes. Based on the proposed accelerator architecture, we evaluated two scales of networks, named small-scale LeNet and deep residual SCNN, for object detection. Experiments show that the proposed accelerator achieves a maximum recognition speed of 1, 605 frames/s at a 100 MHz clock for the LeNet network, consuming only 0.65 mJ per image. Furthermore, the accelerator, combined with the proposed configuration and scheduling methods, achieves acceleration for each residual module in the deep residual SCNN, reaching a processing speed of 2.59 times that of the CPU with a power consumption of only 16.77% of the CPU. This demonstrates that the proposed accelerator architecture can achieve higher energy efficiency, compatibility, and wider applicability.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1662886"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered brain structural covariance networks of the thalamic subfields in right chronic capsular stroke.","authors":"Jun Guo, Hongchuan Zhang, Jingchun Liu, Caihong Wang, Chen Cao, Jingliang Cheng, Chunshui Yu, Wen Qin","doi":"10.3389/fnins.2025.1650937","DOIUrl":"10.3389/fnins.2025.1650937","url":null,"abstract":"<p><strong>Background: </strong>The thalamus, along with its component nuclei, possesses extensive connections with various brain regions and is engaged in diverse functions. However, it is unknown whether the gray matter volume (GMV) covariance networks of thalamic subfields are selectively affected in chronic capsular stroke.</p><p><strong>Methods: </strong>We recruited 45 patients with chronic right capsular strokes (CS) and 93 normal controls (NC) from three centers. The thalamus was segmented into 25 subfields using FreeSurfer (v7.1.1). A general linear model was applied to investigate intergroup differences in the GMV covariance network of each thalamic subfield with each voxel of the entire brain between CS and NC, correcting for confounders such as age, gender, total intracranial volume (TIV), and scanners (voxel-wise <i>p</i> < 0.001, cluster-wise FWE corrected <i>p</i> < 0.05).</p><p><strong>Results: </strong>Our findings revealed that all 25 ipsilesional thalamic subfields in CS were atrophied (<i>p</i> < 0.05, FDR correction). Among these, 16 ipsilesional thalamic subfields (including AV, LD, LP, VLa, VLp, VPL, VM, CeM, CL, MDm, LGN, PuM, PuI, CM, Pf, and Pt) exhibited significantly subfield-specific increased GMV covariance connectivity with the anterior orbital gyrus, superior occipital gyrus, calcarine, anterior cingulate cortex, precentral gyrus, and other regions. Additionally, although none of the contralesional thalamic subfields demonstrated regional GMV changes, 3/25 showed subfield-specific increased GMV covariance connectivity with the ipsilesional anterior orbital gyrus and subcortex.</p><p><strong>Conclusion: </strong>The GMV covariance networks of thalamic subfields are selectively involved in patients with chronic capsular stroke, which affect not only the ipsilesional thalamic subfields but also the contralesional ones.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1650937"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12498287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145244296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized temporal interference stimulation targeting striatum reduces functional stability and dynamic connectivity variability in the sensorimotor network.","authors":"Dongsheng Tang, Lang Qin, Longfei Hu, Siqi Gao, Yixuan Jian, Zhiqiang Zhu","doi":"10.3389/fnins.2025.1645903","DOIUrl":"10.3389/fnins.2025.1645903","url":null,"abstract":"<p><strong>Background: </strong>Functional stability within brain networks, particularly the sensorimotor network (SMN), is crucial for coherent motor control. Temporal Interference (TI) stimulation offers a non-invasive method to modulate deep brain structures like the striatum, yet its impact on dynamic functional stability across motor networks remains largely unexplored.</p><p><strong>Methods: </strong>Twenty-six healthy male participants separately underwent TI stimulation and Sham stimulation in a crossover, double-blind, randomized controlled trial with counterbalanced protocol. resting-state functional magnetic resonance imaging (rs-fMRI) was acquired before and during the stimulation. A total of 20 min TI stimulation (10 mA, Δf = 20 Hz) was applied to the right striatum using personalized electrode montages optimized. Dynamic functional connectivity (dFC) was computed using a sliding-window approach. Voxel-wise functional stability across the whole brain was quantified by Kendall's concordance coefficient of voxel-to-voxel dFC. Seed-based dFC variability in the right striatum was measured as the standard deviation of dFC across windows.</p><p><strong>Results: </strong>(1) Functional stability: TI stimulation significantly decreased functional stability in bilateral SMA regions (predominantly SMA proper, with parts of pre-SMA) compared to Sham and baseline conditions (<i>P</i> < 0.01). (2) Dynamic functional connectivity: TI stimulation reduced dFC variability between the right striatum and left SMA region (predominantly SMA proper, with parts of pre-SMA) compared to baseline (<i>P</i> < 0.01). (3) Safety: No adverse cognitive effects or side effects were observed, with good blinding effectiveness maintained throughout the study.</p><p><strong>Conclusion: </strong>Our findings indicate that TI stimulation targeting the striatum effectively modulates sensorimotor network stability and dFC variability within the cortico-striatal pathway, highlighting its potential as a non-invasive neuromodulation approach for motor network disorders.</p><p><strong>Clinical trial registration: </strong>[www.chictr.org.cn;], identifier [ChiCTR2500098699].</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1645903"},"PeriodicalIF":3.2,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12511026/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145280000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frontiers of optic nerve regeneration research: an analysis of the top 100 most influential articles in the field from 2005 to 2025.","authors":"Saijilafu, Peng Chen, Lingchen Ye, Yuxi Shen, Qi Wang, Xuanwen Chen, Chimedragchaa Chimedtseren, Junqian Zhang, Linjun Fang, Renjie Xu","doi":"10.3389/fnins.2025.1634999","DOIUrl":"10.3389/fnins.2025.1634999","url":null,"abstract":"<p><strong>Objectives: </strong>In this study, we evaluated the key features of the 100 most-cited publications on optic nerve regeneration from 2005 to 2025 employing bibliometric and visual analysis.</p><p><strong>Methods: </strong>The data for this study were obtained from a comprehensive search across multiple databases, including the Web of Science, Scopus, and Dimensions. We identified the top 100 most-cited articles published in each database from 2005 to 2025, merged and deduplicated the results, and selected the 100 most-cited papers on optic nerve regeneration. After extracting key details such as titles, authors, keywords, publication information, and institutional affiliations, a bibliometric analysis was conducted.</p><p><strong>Results: </strong>The top 100 most cited papers on optic nerve regeneration published between 2005 and 2025, accumulating 34,636 total citations with a median of 346 citations per paper. Prof. Zhigang He emerged as the most prolific author with 19 publications. The United States contributed 59 papers, while Harvard University led institutions with 30 publications. Key research themes included optic nerve regeneration, CNTF, gene therapy, and retinal ganglion cells.</p><p><strong>Conclusion: </strong>Our analysis of top-cited optic nerve regeneration research reveals sustained United States leadership in output and innovation. Early work focused on neuronal signaling pathways (PTEN/mTOR, KLF family), while current studies explore novel targets and biomaterials. Global collaboration among the United States, China, and European nations has accelerated progress. Key challenges remain in achieving functional long-distance regeneration. Future direction should prioritize the development of multi-target therapeutic methods, precise drug delivery, and the control of inflammation to improve nerve regeneration efficiency.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1634999"},"PeriodicalIF":3.2,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12507791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145279974","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}