Cognitive Neurodynamics最新文献

{"title":"Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction.","authors":"Ming Ke, Xinyi Yao, Peihui Cao, Guangyao Liu","doi":"10.1007/s11571-024-10191-0","DOIUrl":"https://doi.org/10.1007/s11571-024-10191-0","url":null,"abstract":"<p><p>Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10191-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"7"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11703786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945880","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":"Unraveling the functional complexity of the locus coeruleus-norepinephrine system: insights from molecular anatomy to neurodynamic modeling.","authors":"Chun-Wang Su, Fan Yang, Runchen Lai, Yanhai Li, Hadia Naeem, Nan Yao, Si-Ping Zhang, Haiqing Zhang, Youjun Li, Zi-Gang Huang","doi":"10.1007/s11571-024-10208-8","DOIUrl":"10.1007/s11571-024-10208-8","url":null,"abstract":"<p><p>The locus coeruleus (LC), as the primary source of norepinephrine (NE) in the brain, is central to modulating cognitive and behavioral processes. This review synthesizes recent findings to provide a comprehensive understanding of the LC-NE system, highlighting its molecular diversity, neurophysiological properties, and role in various brain functions. We discuss the heterogeneity of LC neurons, their differential responses to sensory stimuli, and the impact of NE on cognitive processes such as attention and memory. Furthermore, we explore the system's involvement in stress responses and pain modulation, as well as its developmental changes and susceptibility to stressors. By integrating molecular, electrophysiological, and theoretical modeling approaches, we shed light on the LC-NE system's complex role in the brain's adaptability and its potential relevance to neurological and psychiatric disorders.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"29"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11757662/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045808","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":"Attenuated heterogeneity of hippocampal neuron subsets in response to novelty induced by amyloid-β.","authors":"Xiaoxin Ren, Yimeng Wang, Xin Li, Xueling Wang, Zhaodi Liu, Jiajia Yang, Ling Wang, Chenguang Zheng","doi":"10.1007/s11571-025-10237-x","DOIUrl":"10.1007/s11571-025-10237-x","url":null,"abstract":"<p><p>Alzheimer's disease (AD) patients exhibited episodic memory impairments including location-object recognition in a spatial environment, which was also presented in animal models with amyloid-β (Aβ) accumulation. A potential cellular mechanism was the unstable representation of spatial information and lack of discrimination ability of novel stimulus in the hippocampal place cells. However, how the firing characteristics of different hippocampal subsets responding to diverse spatial information were interrupted by Aβ accumulation remains unclear. In this study, we observed impaired novel object-location recognition in Aβ-treated Long-Evans rats, with larger receptive fields of place cells in hippocampal CA1, compared with those in the saline-treated group. We identified two subsets of place cells coding object information (ObjCell) and global environment (EnvCell) during the task, with firing heterogeneity in response to introduced novel information. ObjCells displayed a dynamic representation responding to the introduction of novel information, while EnvCells exhibited a stable representation to support the recognition of the familiar environment. However, the dynamic firing patterns of these two subsets of cells were disrupted to present attenuated heterogeneity under Aβ accumulation. The impaired spatial representation novelty information could be due to the disturbed gamma modulation of neural activities. Taken together, these findings provide new evidence for novelty recognition impairments of AD rats with spatial representation dysfunctions of hippocampal subsets.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"56"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11947398/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143751144","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":"Altered effective connectivity within brain lesioned regions and cognitive impairment after stroke.","authors":"Jing Zhang, Hui Tang, Lijun Zuo, Hao Liu, Zixiao Li, Jing Jing, Yongjun Wang, Tao Liu","doi":"10.1007/s11571-024-10209-7","DOIUrl":"10.1007/s11571-024-10209-7","url":null,"abstract":"<p><p>Poststroke cognitive impairments (PSCI) reflect widespread network dysfunction due to structural damage, abnormal neural activity, or abnormal connections in affected brain regions. The exact influence of these lesioned regions on the related functional network and their role in PSCI remains unclear. We recruited 35 first-time stroke patients who had basal ganglia infarcts and PSCI, along with 29 age-matched healthy controls. We utilized T1-weighted imaging to inspect structural damage with regional gray matter volume (GMV). Resting-state fMRI data were utilized to examine spontaneous activities with regional Wavelet-ALFF metric, investigate dynamic functional connectivity (dFC) by seeding the region with damaged GMV, and further study effective connectivity within the abnormal dFC network and its impact on PSCI. In comparison to HC, patients showed significant reduced GMV in the bilateral Rolandic operculum (ROL), along with notable abnormal Wavelet-ALFF values in the right Precuneus (PCUN) and left Cerebellum_9 (CER9). Particularly, an abnormal dFC network seeded in the left ROL, demonstrating significantly differential between PSCI and HC groups and remaining consistent across all time windows, was observed. This abnormal dFC network comprised the left ROL as the seed region, the right ROL, bilateral PCUN, bilateral CER9, right Superior Temporal Gyrus (STG), and right Parahippocampal Gyrus (PHG). Notably, in patients, impaired functions across various cognitive domains significantly influenced the altered effective connections among the abnormal regions, particularly impacting the connections between structurally damaged regions and those with abnormal spontaneous activity. These findings suggest that altered effective connectivity networks within lesioned regions may contribute to deficits in various cognitive domains in PSCI.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10209-7.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"36"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11794930/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364024","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":"Dynamic modeling of astrocyte-neuron interactions under the influence of Aβ deposition.","authors":"JiangNing Wang, XiaoLi Yang","doi":"10.1007/s11571-025-10246-w","DOIUrl":"https://doi.org/10.1007/s11571-025-10246-w","url":null,"abstract":"<p><p>β-amyloid (Aβ) protein accumulation is recognized as a key factor in Alzheimer's disease (AD) pathogenesis. Its effects on astrocyte function appear primarily as disturbances to intracellular calcium signaling, which, in turn, affects neuronal excitability. We propose an innovative neuron-astrocyte interaction model to examine how Aβ accumulation influences astrocyte calcium oscillation and neuronal excitability, emphasizing its significance in AD pathogenesis. This comprehensive model describes not only the response of the astrocyte to presynaptic neuron stimulation but also the release of the downstream signaling glutamate and its consequential feedback on neurons. Our research concentrates on changes within two prominent pathways affected by Aβ: the creation of Aβ astrocyte membrane pores and the enhanced sensitivity of ryanodine receptors. By incorporating these adjustments into our astrocyte model, we can reproduce previous experimental findings regarding aberrant astrocyte calcium activity and neural behavior associated with Aβ from a neural computational viewpoint. Within a specified range of Aβ influence, our numerical analysis reveals that astrocyte cytoplasmic calcium rises, calcium oscillation frequency increases, and the time to the first calcium peak shortens, indicating the disrupted astrocyte calcium signaling. Simultaneously, the neuronal firing rate and cytosolic calcium concentration increase while the threshold current for initiating repetitive firing diminishes, implying heightened neuronal excitability. Given that increased neuronal excitability commonly occurs in early AD patients and correlates with cognitive decline, our findings may highlight the importance of Aβ accumulation in AD pathogenesis and provide a theoretical basis for identifying neuronal markers in the early stages of the disease.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"60"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11985881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143961717","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":"Multilevel Inter-modal and Intra-modal Transformer network with domain adversarial learning for multimodal sleep staging.","authors":"Yang-Yang He, Jian-Wei Liu","doi":"10.1007/s11571-025-10262-w","DOIUrl":"10.1007/s11571-025-10262-w","url":null,"abstract":"<p><p>Sleep staging identification is a fundamental task for the diagnosis of sleep disorders. With the development of biosensing technology and deep learning technology, it is possible to automatically decode sleep process through electroencephalogram signals. However, most sleep staging methods do not consider multimodal sleep signals such as electroencephalogram and electrooculograms signals simultaneously for sleep staging due to the limitation of performance improvement. To this regard, we design a Multilevel Inter-modal and Intra-modal Transformer network with domain adversarial learning for multimodal sleep staging, we introduce a multilevel Transformer structure to fully capture the temporal dependencies within sleep signals of each modality and the interdependencies among different modalities. Simultaneously, we strive for the multi-scale CNNs to learn time and frequency features separately. Our research promotes the application of Transformer models in the field of sleep staging identification. Moreover, considering individual differences among subjects, models trained on one group's data often perform poorly when applied to another group, known as the domain generalization problem. While domain adaptation methods are commonly used, fine-tuning on the target domain each time is cumbersome and impractical. To effectively address these issues without using target domain information, we introduce domain adversarial learning to help the model learn domain-invariant features for better generalization across domains. We validated the superiority of our model on two commonly used datasets, significantly outperforming other baseline models. Our model efficiently extracts dependencies of intra-modal level and inter-modal level from multimodal sleep data, making it suitable for scenarios requiring high accuracy.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"80"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12106285/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173259","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":"DMPat-based SOXFE: investigations of the violence detection using EEG signals.","authors":"Kubra Yildirim, Tugce Keles, Sengul Dogan, Turker Tuncer, Irem Tasci, Abdul Hafeez-Baig, Prabal Datta Barua, U R Acharya","doi":"10.1007/s11571-025-10266-6","DOIUrl":"10.1007/s11571-025-10266-6","url":null,"abstract":"<p><p>Automatic violence detection is one of the most important research areas at the intersection of machine learning and information security. Moreover, we aimed to investigate violence detection in the context of neuroscience. Therefore, we have collected a new electroencephalography (EEG) violence detection dataset and presented a self-organized explainable feature engineering (SOXFE) approach. In the first phase of this research, we collected a new EEG violence dataset. This dataset contains two classes: (i) resting, (ii) violence. To detect violence automatically, we proposed a new SOXFE approach, which contains five main phases: (1) feature extraction with the proposed distance matrix pattern (DMPat), which generates three feature vectors, (2) feature selection with iterative neighborhood component analysis (INCA), and three selected feature vectors were created, (3) explainable results generation using Directed Lobish (DLob) and statistical analysis of the generated DLob string, (4) classification deploying t algorithm-based k-nearest neighbors (tkNN), and (5) information fusion employing mode operator and selecting the best outcome via greedy algorithm. By deploying the proposed model, classification and explainable results were generated. To obtain the classification results, tenfold cross-validation (CV), leave-one-record-out (LORO) CV were utilized, and the presented model attained 100% classification accuracy with tenfold CV and reached 98.49% classification accuracy with LORO CV. Moreover, we demonstrated the cortical connectome map related to violence. These results and findings clearly indicated that the proposed model is a good violence detection model. Moreover, this model contributes to feature engineering, neuroscience and social security.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"86"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246796","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":"EA-EEG: a novel model for efficient motor imagery EEG classification with whitening and multi-scale feature integration.","authors":"Yutao Miao, Kaijie Li, Wenhao Zhao, Yushi Zhang","doi":"10.1007/s11571-025-10278-2","DOIUrl":"10.1007/s11571-025-10278-2","url":null,"abstract":"<p><p>Electroencephalography (EEG) is a non-invasive technique widely used in neuroscience and brain-computer interfaces (BCI) due to its high temporal resolution. In motor imagery EEG (MI-EEG) tasks, EEG signals reflect movement-related brain activity, making them ideal for BCI control. However, the non-stationary nature of MI-EEG signals poses significant challenges for classification, as frequency characteristics vary across tasks and individuals. Traditional preprocessing methods, such as bandpass filtering and standardization, may struggle to adapt to these variations, potentially limiting classification performance. To address this issue, this study introduces EA-EEG, an improved MI-EEG classification model that incorporates whitening as a preprocessing step to reduce channel correlation and enhance the model feature extraction ability. EA-EEG further leverages a multi-scale pooling strategy, combining convolutional networks and root mean square pooling to extract key spatial and temporal features, and applies prototype-based classification to improve MI-EEG classification performance. Experiments on the BCI4-2A and BCI4-2B datasets demonstrate that EA-EEG achieves state-of-the-art performance, with 85.33% accuracy (Kappa = 0.804) on BCI4-2A and 88.05% accuracy (Kappa = 0.761) on BCI4-2B, surpassing existing approaches. These results confirm EA-EEG's effectiveness in handling non-stationary MI-EEG signals, demonstrating its potential for robust BCI applications, including rehabilitation, prosthetic control, and cognitive monitoring.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"94"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12173996/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332587","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":"MBRSTCformer: a knowledge embedded local-global spatiotemporal transformer for emotion recognition.","authors":"Chenglin Lin, Huimin Lu, Chenyu Pan, Songzhe Ma, Zexing Zhang, Runhui Tian","doi":"10.1007/s11571-025-10277-3","DOIUrl":"10.1007/s11571-025-10277-3","url":null,"abstract":"<p><p>Emotion recognition is an essential prerequisite for realizing generalized BCI, which possesses an extensive range of applications in real life. EEG-based emotion recognition has become mainstream due to its real-time mapping of brain emotional activities, so a robust EEG-based emotion recognition model is of great interest. However, most existing deep learning emotion recognition methods treat the EEG signal as a whole feature extraction, which will destroy its local stimulation differences and fail to extract local features of the brain region well. Inspired by the cognitive mechanisms of the brain, we propose the multi-brain regions spatiotemporal collaboration transformer (MBRSTCfromer) framework for EEG-based emotion recognition. First, inspired by the prior knowledge, we propose the Multi-Brain Regions Collaboration Network. The EEG data are processed separately after being divided by brain regions, and stimulation scores are presented to quantify the stimulation produced by different brain regions and feedback on the stimulation degree to the MBRSTCfromer. Second, we propose a Cascade Pyramid Spatial Fusion Temporal Convolution Network for multi-brain regions EEG features fusion. Finally, we conduct comprehensive experiments on two mainstream emotion recognition datasets to validate the effectiveness of our proposed MBRSTCfromer framework. We achieved 98.63 <math><mo>%</mo></math> , 98.15 <math><mo>%</mo></math> , and 98.58 <math><mo>%</mo></math> accuracy on the three dimensions (arousal, valence, and dominance) on the DEAP dataset; and 97.66 <math><mo>%</mo></math> , 97.07 <math><mo>%</mo></math> , and 97.97 <math><mo>%</mo></math> on the DREAMER dataset.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"95"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174000/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144332588","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":"Neuroenhancement by repetitive transcranial magnetic stimulation (rTMS) on DLPFC in healthy adults.","authors":"Elias Ebrahimzadeh, Seyyed Mostafa Sadjadi, Mostafa Asgarinejad, Amin Dehghani, Lila Rajabion, Hamid Soltanian-Zadeh","doi":"10.1007/s11571-024-10195-w","DOIUrl":"10.1007/s11571-024-10195-w","url":null,"abstract":"<p><p>The term \"neuroenhancement\" describes the enhancement of cognitive function associated with deficiencies resulting from a specific condition. Nevertheless, there is currently no agreed-upon definition for the term \"neuroenhancement\", and its meaning can change based on the specific research being discussed. As humans, our continual pursuit of expanding our capabilities, encompassing both cognitive and motor skills, has led us to explore various tools. Among these, repetitive Transcranial Magnetic Stimulation (rTMS) stands out, yet its potential remains underestimated. Historically, rTMS was predominantly employed in studies focused on rehabilitation objectives. A small amount of research has examined its use on healthy subjects with the goal of improving cognitive abilities like risk-seeking, working memory, attention, cognitive control, learning, computing speed, and decision-making. It appears that the insights gained in this domain largely stem from indirect outcomes of rehabilitation research. This review aims to scrutinize these studies, assessing the effectiveness of rTMS in enhancing cognitive skills in healthy subjects. Given that the dorsolateral prefrontal cortex (DLPFC) has become a popular focus for rTMS in treating psychiatric disorders, corresponding anatomically to Brodmann areas 9 and 46, and considering the documented success of rTMS stimulation on the DLPFC for cognitive improvement, our focus in this review article centers on the DLPFC as the focal point and region of interest. Additionally, recognizing the significance of theta burst magnetic stimulation protocols (TBS) in mimicking the natural firing patterns of the brain to modulate excitability in specific cortical areas with precision, we have incorporated Theta Burst Stimulation (TBS) wave patterns. This inclusion, mirroring brain patterns, is intended to enhance the efficacy of the rTMS method. To ascertain if brain magnetic stimulation consistently improves cognition, a thorough meta-analysis of the existing literature has been conducted. The findings indicate that, after excluding outlier studies, rTMS may improve cognition when compared to appropriate control circumstances. However, there is also a considerable degree of variation among the researches. The navigation strategy used to reach the stimulation site and the stimulation location are important factors that contribute to the variation between studies. The results of this study can provide professional athletes, firefighters, bodyguards, and therapists-among others in high-risk professions-with insightful information that can help them perform better on the job.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"34"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11759757/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045801","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}