International journal of neural systems最新文献

{"title":"Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.","authors":"Jingyi He, Joël M H Karel, Marcus L F Janssen, Erik D Gommer, Catherine J Vossen, Enrique Hortal","doi":"10.1142/S0129065725500339","DOIUrl":"10.1142/S0129065725500339","url":null,"abstract":"<p><p>Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated with adverse outcomes such as postoperative delirium, extended recovery time, and increased postoperative mortality. The detection and prediction of BS can help expedite the evaluation of patient conditions, optimize anesthesia administration, and improve patient safety. This study explores the potential for automatic BS detection using intraoperative EEG and BS prediction using preoperative EEG signals and patient characteristics. A dataset comprising 287 patients who underwent carotid endarterectomy procedures at Maastricht University Medical Center+ was analyzed. An EEG toolbox developed by T. Zhan at the Massachusetts Institute of Technology was utilized for the automatic detection/annotation of BS, while five machine learning classifiers were employed to predict BS occurrence using preoperative data. Based on the 160 patients manually annotated by EEG experts (regarding the presence or absence of BS), the automatic detection tool demonstrated an accuracy of 0.75. For the BS prediction task, an initial subset of 120 patients was evaluated, showing modest performance, with the <i>K</i>-nearest neighbors ([Formula: see text]) classifier achieving the best results, with an accuracy of 0.72. Subsequent experiments indicated that increasing the number of patients (by using <i>Zhan's Toolbox</i> to annotate the unlabeled instances), applying SMOTE to balance the training set, and enriching the feature set was beneficial. The final experiment demonstrated a significant improvement, with Random Forest and Gradient Boosting outperforming other classifiers, achieving an accuracy of 0.86 and ROC-AUC of 0.94. Patient characteristics, including type of anesthetic agents, symptoms, age, mean absolute delta power, mean absolute theta power, and cognitive impairment, were identified by an xAI method as important features potentially indicating the predisposition to experience BS.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550033"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144033130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directed Weighted EEG Connectogram Insights of One-to-One Causality for Identifying Developmental Dyslexia.","authors":"Ignacio Rodríguez-Rodríguez, José Ignacio Mateo-Trujillo, Andrés Ortiz, Nicolás J Gallego-Molina, Diego Castillo-Barnes, Juan L Luque","doi":"10.1142/S0129065725500327","DOIUrl":"10.1142/S0129065725500327","url":null,"abstract":"<p><p>Developmental dyslexia (DD) affects approximately 5-12% of learners, posing persistent challenges in reading and writing. This study presents a novel electroencephalography (EEG)-based methodology for identifying DD using two auditory stimuli modulated at 4.8[Formula: see text]Hz (prosodic) and 40[Formula: see text]Hz (phonemic). EEG signals were processed to estimate one-to-one Granger causality, yielding directed and weighted connectivity matrices. A novel Mutually Informed Correlation Coefficient (MICC) feature selection method was employed to identify the most relevant causal links, which were visualized using connectograms. Under the 4.8[Formula: see text]Hz stimulus, altered theta-band connectivity between frontal and occipital regions indicated compensatory frontal activation for prosodic processing and visual-auditory integration difficulties, while gamma-band anomalies between occipital and temporal regions suggested impaired visual-prosodic integration. Classification analysis under the 4.8[Formula: see text]Hz stimulus yielded area under the ROC curve (AUC) values of 0.92 (theta) and 0.91 (gamma band). Under the 40[Formula: see text]Hz stimulus, theta abnormalities reflected dysfunctions in integrating auditory phoneme signals with executive and motor regions, and gamma alterations indicated difficulties coordinating visual and auditory inputs for phonological decoding, with AUC values of 0.84 (theta) and 0.89 (gamma). These results support both the Temporal Sampling Framework and the Phonological Core Deficit Hypothesis. Future research should extend the range of stimuli frequencies and include more diverse cohorts to further validate these potential biomarkers.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550032"},"PeriodicalIF":0.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonlinear Spiking Neural Systems for thermal Image Semantic Segmentation Networks.","authors":"Peng Wang, Minglong He, Hong Peng, Zhicai Liu","doi":"10.1142/S0129065725500388","DOIUrl":"https://doi.org/10.1142/S0129065725500388","url":null,"abstract":"<p><p>Thermal and RGB images exhibit significant differences in information representation, especially in low-light or nighttime environments. Thermal images provide temperature information, complementing the RGB images by restoring details and contextual information. However, the spatial discrepancy between different modalities in RGB-Thermal (RGB-T) semantic segmentation tasks complicates the process of multimodal feature fusion, leading to a loss of spatial contextual information and limited model performance. This paper proposes a channel-space fusion nonlinear spiking neural P system model network (CSPM-SNPNet) to address these challenges. This paper designs a novel color-thermal image fusion module to effectively integrate features from both modalities. During decoding, a nonlinear spiking neural P system is introduced to enhance multi-channel information extraction through the convolution of spiking neural P systems (ConvSNP) operations, fully restoring features learned in the encoder. Experimental results on public datasets MFNet and PST900 demonstrate that CSPM-SNPNet significantly improves segmentation performance. Compared with the existing methods, CSPM-SNPNet achieves a 0.5% improvement in mIOU on MFNet and 1.8% on PST900, showcasing its effectiveness in complex scenes.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550038"},"PeriodicalIF":0.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144096588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-Supervised Image Segmentation Using Meta-Learning and Multi-Backbone Feature Fusion.","authors":"Muhammad Shahroz Ajmal, Guohua Geng, Xiaofeng Wang, Mohsin Ashraf","doi":"10.1142/S0129065725500121","DOIUrl":"10.1142/S0129065725500121","url":null,"abstract":"<p><p>Few-shot segmentation (FSS) aims to reduce the need for manual annotation, which is both expensive and time-consuming. While FSS enhances model generalization to new concepts with only limited test samples, it still relies on a substantial amount of labeled training data for base classes. To address these issues, we propose a multi-backbone few shot segmentation (MBFSS) method. This self-supervised FSS technique utilizes unsupervised saliency for pseudo-labeling, allowing the model to be trained on unlabeled data. In addition, it integrates features from multiple backbones (ResNet, ResNeXt, and PVT v2) to generate a richer feature representation than a single backbone. Through extensive experimentation on PASCAL-5i and COCO-20i, our method achieves 54.3% and 25.1% on one-shot segmentation, exceeding the baseline methods by 13.5% and 4%, respectively. These improvements significantly enhance the model's performance in real-world applications with negligible labeling effort.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550012"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coherence-Based Graph Convolution Network to Assess Brain Reorganization in Spinal Cord Injury Patients.","authors":"Jiancai Leng, Jiaqi Zhao, Yongjian Wu, Chengyan Lv, Zhixiao Lun, Yanzi Li, Chao Zhang, Bin Zhang, Yang Zhang, Fangzhou Xu, Changsong Yi, Tzyy-Ping Jung","doi":"10.1142/S0129065725500212","DOIUrl":"10.1142/S0129065725500212","url":null,"abstract":"<p><p>Motor imagery (MI) engages a broad network of brain regions to imagine a specific action. Investigating the mechanism of brain network reorganization during MI after spinal cord injury (SCI) is crucial because it reflects overall brain activity. Using electroencephalogram (EEG) data from SCI patients, we conducted EEG-based coherence analysis to examine different brain network reorganizations across different frequency bands, from resting to MI. Furthermore, we introduced a consistency calculation-based residual graph convolution (C-ResGCN) classification algorithm. The results show that the [Formula: see text]- and [Formula: see text]-band connectivity weakens, and brain activity decreases during the MI task compared to the resting state. In contrast, the [Formula: see text]-band connectivity increases in motor regions while the default mode network activity declines during MI. Our C-ResGCN algorithm showed excellent performance, achieving a maximum classification accuracy of 96.25%, highlighting its reliability and stability. These findings suggest that brain reorganization in SCI patients reallocates relevant brain resources from the resting state to MI, and effective network reorganization correlates with improved MI performance. This study offers new insights into the mechanisms of MI and potential biomarkers for evaluating rehabilitation outcomes in patients with SCI.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550021"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the Spatio-Temporal Coupling of Spikes and Spindles in Focal Epilepsy Through a Network-Level Computational Model.","authors":"Min Pan, Qiang Li, Jiangling Song, Bo Wang, Wenhua Wang, Rui Zhang","doi":"10.1142/S0129065725500182","DOIUrl":"10.1142/S0129065725500182","url":null,"abstract":"<p><p>The electrophysiological findings have shown that epileptiform spikes triggering sleep spindles within 1[Formula: see text]s across multiple channels are commonly observed during sleep in focal epilepsy (FE). Such spatio-temporal couplings of spikes and spindles (STCSSs) are defined as a kind of pathological waves, and frequent emergence of them may cause the degradation of cognitive function for FE patients. However, the neural mechanisms underlying STCSSs are not well understood. To this end, this work first develops a neural mass network model for focal epilepsy (FE-NMNM) with multiple thalamocortical columns being its nodes and the long-range synaptic interactions of thalamocortical columns being its edges, where each thalamocortical column is extended on the basis of Costa model and then they are connected through excitatory synapses between pyramidal cells. Then, how the cortico-cortical connectivity affects the evolution of STCSSs across the network is especially discussed by simulations in two cases, where the inter-ictal state and the ictal state are considered separately. Simulation results demonstrate that: (1) the more STCSSs occur in a more extensive area when the cortico-cortical connectivity becomes stronger, and the significant increase of coupling discharges is attributed to the presence of abundant spikes; (2) when the connectivity is excessively strong, the cortical hyperexcitability will happen, thereby inducing massive spike discharges which may further inhibit the occurrence of spindles, and hence, resulting in the disappearance of STCSSs. The obtained results provide a mechanistic insight into STCSSs, and suggest that such coupling patterns could reflect widespread network dysfunction in FE, thereby potentially advancing therapeutic strategies for FE.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550018"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143625844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Dynamic Surface Control of Epileptor Model Based on Nonlinear Luenberger State Observer.","authors":"Mahdi Kamali Dolatabadi, Marzieh Kamali, Farzaneh Shayegh","doi":"10.1142/S0129065725500224","DOIUrl":"10.1142/S0129065725500224","url":null,"abstract":"<p><p>Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, which are sudden bursts of electrical activity in the brain. The Epileptor model is a computational model specifically created to replicate the complex dynamics of epileptic seizures. The parameters of the Epileptor model can be adjusted to simulate activities associated with some seizure classes seen in patients. Due to the closeness of this model to nonlinear systems with nonstrict feedback form and the existence of uncertainties in the model, an adaptive dynamic surface controller is chosen for control of the system. Considering that the states in the Epileptor model are not measurable and the only measurable output is the Local Field Potentials signal, a nonlinear Luenberger state observer is developed to estimate the system states. It is the first time that the Luenberger state observer is used for the Epileptor model. In this approach, Radial Basis Neural Networks are utilized to estimate the system's nonlinear dynamics. The stability of our proposed controller along with the observer is proved, and the performance is shown using simulation. Simulation results show that by using the suggested method, the output and states of the, system track their reference, value with an acceptable error.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 5","pages":"2550022"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scene Text Detection Based on Text Stroke Components.","authors":"Xinyue Hou, Pengsen Cheng, Hongyu Gao, Xin Li, Jiayong Liu","doi":"10.1142/S0129065725500200","DOIUrl":"10.1142/S0129065725500200","url":null,"abstract":"<p><p>The detection of scene text holds significant importance across a variety of application scenarios. However, previous methods were insufficient for detecting and recognizing text instances, such as variations in text size, chaotic background and diverse text orientations. To address these challenges, this paper proposes a novel methodology based on Text Stroke Components (TSC). The method leverages Harris corner detection to identify critical points of text strokes, such as endpoints, turning points, and curvatures. By analyzing the clustered regions of these points, the approach effectively localizes text characters. To enhance the detection process, a transparency parameter [Formula: see text] is introduced to control the fusion between original images and corner-detection images. This improves the localization of key stroke points, and reduces background noise interference. The proposed method is evaluated through extensive experiments, demonstrating superior performance compared to existing scene text detectors. Furthermore, the method is jointly trained with the ABINet recognition model across all stages. Comprehensive experiments conducted on 13 datasets reveal that this approach significantly outperforms SOTA methods. These results underscore the advantages of using text stroke components for key-point localization through the corner detection algorithm in scene text detection.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 5","pages":"2550020"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Heterogeneous Attractor Model for Neural Dynamical Mechanism of Movement Preparation.","authors":"Lining Yin, Lanyun Cui, Ying Yu, Qingyun Wang","doi":"10.1142/S0129065725500194","DOIUrl":"10.1142/S0129065725500194","url":null,"abstract":"<p><p>Preparatory activity is crucial for voluntary motor control, reducing reaction time and enhancing precision. To understand the neurodynamic mechanisms behind this, we construct a dynamical model within the motor cortex, which comprises coupled heterogeneous attractors to simulate delayed reaching tasks. This model replicates the neural activity patterns observed in the macaque motor cortex, within distinct attractor spaces for preparatory and executive activities. It can capture the transition from preparation to execution through shifts in an orthogonal subspace combined with a thresholding mechanism. Results show that the preparation duration modulates behavioral accuracy, with optimal preparation intervals enhancing performance. External inputs primarily shape the preparatory activity, while synaptic connections dominate execution. Our analysis of the network's multi-stable dynamics reveals that external inputs reshape the stable points of the heterogeneous attractor modules both before and after preparation, while synaptic strength affects dynamical stability and input sensitivity, allowing rapid and precise actions. Additionally, sensitivity to external perturbations decreases as preparatory time increases, emphasizing the importance of external inputs during preparation. Overall, this study provides insights into the neurodynamic mechanisms underlying the transition from motor preparation to execution and underscores the significance of preparatory activity for accurate motor control.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":"35 5","pages":"2550019"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Computational Complexity of Spiking Neural Membrane Systems with Colored Spikes.","authors":"Antonio Grillo, Claudio Zandron","doi":"10.1142/S0129065725500352","DOIUrl":"https://doi.org/10.1142/S0129065725500352","url":null,"abstract":"<p><p>Spiking Neural P Systems are parallel and distributed computational models inspired by biological neurons, emerging from membrane computing and applied to solving computationally difficult problems. This paper focuses on the computational complexity of such systems using neuron division rules and colored spikes for the SAT problem. We prove a conjecture stated in a recent paper, showing that enhancing the model with an input module reduces computing time. Additionally, we prove that the inclusion of budding rules extends the model's capability to solve all problems in the complexity class <b>PSPACE</b>. These findings advance research on Spiking Neural P Systems and their application to complex problems; however, whether both budding rules and division rules are required to extend these methods to problem domains beyond the NP class remains an open question.</p>","PeriodicalId":94052,"journal":{"name":"International journal of neural systems","volume":" ","pages":"2550035"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}