Cognitive NeurodynamicsPub Date : 2025-12-01Epub Date: 2025-03-22DOI: 10.1007/s11571-025-10239-9
Hongze Sun, Shifeng Mao, Wuque Cai, Yan Cui, Duo Chen, Dezhong Yao, Daqing Guo
{"title":"BISNN: bio-information-fused spiking neural networks for enhanced EEG-based emotion recognition.","authors":"Hongze Sun, Shifeng Mao, Wuque Cai, Yan Cui, Duo Chen, Dezhong Yao, Daqing Guo","doi":"10.1007/s11571-025-10239-9","DOIUrl":"10.1007/s11571-025-10239-9","url":null,"abstract":"<p><p>Spiking neural networks (SNNs), known for their rich spatio-temporal dynamics, have recently gained considerable attention in EEG-based emotion recognition. However, conventional model training approaches often fail to fully exploit the capabilities of SNNs, posing challenges for effective EEG data analysis. In this work, we propose a novel bio-information-fused SNN (BISNN) model to enhance EEG-based emotion recognition. The BISNN model incorporates biologically plausible intrinsic parameters into spiking neurons and is initialized with a structurally equivalent pre-trained ANN model. By constructing a bio-information-fused loss function, the BISNN model enables simultaneous training under dual constraints. Extensive experiments on benchmark EEG-based emotion datasets demonstrate that the BISNN model achieves competitive performance compared to state-of-the-art methods. Additionally, ablation studies investigating various components further elucidate the mechanisms underlying the model's effectiveness and evolution, aligning well with previous findings.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"52"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929665/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143699844","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}
Cognitive NeurodynamicsPub Date : 2025-12-01Epub Date: 2025-06-05DOI: 10.1007/s11571-025-10269-3
Indu Dokare, Sudha Gupta
{"title":"Shap-driven explainable AI with simulated annealing for optimized seizure detection using multichannel EEG signal.","authors":"Indu Dokare, Sudha Gupta","doi":"10.1007/s11571-025-10269-3","DOIUrl":"10.1007/s11571-025-10269-3","url":null,"abstract":"<p><p>The aim of this research is to combine Explainable AI (XAI) with advanced optimization techniques to provide a unique framework for seizure detection. This proposed work investigates how to enhance patient-specific and patient-non-specific seizure detection models by combining multiband feature extraction, SHAP-based feature selection, SMOTE, and a metaheuristic algorithm for hyperparameter tuning.The discrete wavelet transform (DWT) is used to decompose EEG signals to retrieve entropy-based and statistical information. Simulated Annealing (SA) is employed to optimize the Random Forest (RF) classifier's hyperparameters, and SHAP (SHapley Additive exPlanations) values are utilized for feature selection. Furthermore, a novel technique SHAP-RELFR has been demonstrated to select patient-non-specific features. Additionally, SMOTE is employed to handle imbalanced data. The proposed methodology is evaluated on the CHB-MIT and Siena datasets using both patient-specific and patient-non-specific feature selection approaches. Experimental findings demonstrate that the proposed methodology significantly improves the performance of seizure detection. The average accuracy, precision, sensitivity, specificity, F1-score, and AUC obtained for a patient-non-specific case are 96.58%, 95.19%, 94.52%, 98.02%, 94.72%, and 0.9452, respectively, using the CHB-MIT dataset. For the Seina dataset, the average accuracy, precision, sensitivity, specificity, F1-score, and AUC obtained for a patient-non-specific case are 94.81%, 94.51%, 94.04%, 96.87%, 94.28%, and 0.9400, respectively. Explainable AI combined with SMOTE and a metaheuristic optimization algorithm facilitates an enhanced seizure detection. The novel SHAP-RELFR method provides an effective patient-non-specific feature selection, enabling this approach to be applicable across diverse patients. This proposed framework offers a step toward enhancing clinical decision-making by providing interpretable and versatile seizure detection models.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"85"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144246797","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}
Cognitive NeurodynamicsPub Date : 2025-12-01Epub Date: 2025-06-30DOI: 10.1007/s11571-025-10290-6
Yiming Li, Haoyu Qiu, Haijun Zhu
{"title":"Effects of transcranial magneto-acoustical stimulation on excitatory and inhibitory neuronal discharge patterns.","authors":"Yiming Li, Haoyu Qiu, Haijun Zhu","doi":"10.1007/s11571-025-10290-6","DOIUrl":"10.1007/s11571-025-10290-6","url":null,"abstract":"<p><p>Transcranial Magneto-Acoustical Stimulation (TMAS) represents an innovative, highly efficacious, and non-invasive modality for brain stimulation. Neurons, as integral components of neural networks, are crucial for the transmission of information. Nevertheless, the impact of TMAS on the discharge patterns of both excitatory and inhibitory neurons is not yet fully understood. To address this gap, the Hodgkin-Huxley neuronal model is analyzed using the improved Euler method. The neuronal discharge patterns are comprehensively examined by systematically adjusting ion channel parameters and stimulation parameters. The results indicate that ultrasound frequency exerted minimal influence on the properties of neuronal action potentials. Conversely, as the static magnetic field strength and ultrasound power are augmented, the excitability of both types of neurons progressively enhances. However, the changes in the electrical properties of action potentials are less pronounced in inhibitory neurons compared to excitatory neurons. Furthermore, alterations in ion channel parameters significantly influence the firing characteristics of both types of neurons. The present study elucidates that TMAS has a significant effect on the firing patterns of excitatory and inhibitory neurons. Excitatory neurons showed stronger regular discharges in response to static magnetic fields and increased ultrasound power, whereas inhibitory neurons did not respond to low-intensity static magnetic fields. In addition, our systematic analysis revealed synergistic effects between ion channel parameters and TMAS stimulation parameters. These findings shed light on how neuron type specificity and ion channel dynamics work together to shape the efficacy of TMAS, thus advancing previous studies.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"108"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209074/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552512","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}
Cognitive NeurodynamicsPub Date : 2025-12-01Epub Date: 2025-06-30DOI: 10.1007/s11571-025-10297-z
Guosheng Yi, Jiayi Cui, Ruifeng Bai
{"title":"Effects of dendritic properties on the correlations in ionic channels emerging from firing rate homeostasis: a two-compartment modeling study.","authors":"Guosheng Yi, Jiayi Cui, Ruifeng Bai","doi":"10.1007/s11571-025-10297-z","DOIUrl":"10.1007/s11571-025-10297-z","url":null,"abstract":"<p><p>Homeostatic regulation of firing rate is an important feature of neural excitability, which is achieved through feedback control of diverse ionic channel expression levels. The output firing rate is controlled by the active currents and passive properties of the dendrites. The objective of this study is to determine how dendritic properties affect the homeostatic regulation of somatic firing rate. We used a two-compartment Pinsky-Rinzel model to simulate action potentials in a pyramidal neuron in response to external inputs. We applied a feedback framework to determine the maximum ionic conductances during homeostatic regulation and examined the pairwise correlations among these conductances. We find that the effective regulation of somatic firing rate could be achieved through controlling both somatic and dendritic ionic channels. The correlations among these channels are lower than those emerging from the regulation through the control of somatic or dendritic channels. It is also shown that increasing the number of adjustable channels alters ionic channel correlations when the additional channel has a strong compensatory relationship with other channels. Compared to the coupling conductance between two compartments, varying the proportion of area occupied by the dendrite produces a greater effect on firing rate dynamics and expression correlations between adjustable channels in both dendrite and soma. The results reveal that dendritic ionic channels, morphological feature and dendritic-somatic coupling are all factors that influence the correlations in ionic channel expression. These findings provide a biophysical basis for the relationship between dendritic properties and neuronal information processing.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10297-z.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"107"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552511","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}
Cognitive NeurodynamicsPub Date : 2025-12-01Epub Date: 2025-06-30DOI: 10.1007/s11571-025-10294-2
Keyi Dong, April Burch, Kang Huang
{"title":"Application of deep learning for multi-scale behavioral analysis in SNCA E46K Parkinson's disease drosophila.","authors":"Keyi Dong, April Burch, Kang Huang","doi":"10.1007/s11571-025-10294-2","DOIUrl":"10.1007/s11571-025-10294-2","url":null,"abstract":"<p><p><i>Drosophila melanogaster</i> is widely used as a model organism in Parkinson's disease research. However, due to the complexity of motion capture and the challenges of quantitatively assessing spontaneous behavior in <i>Drosophila melanogaster</i>, it remains technically difficult to identify symptoms of Parkinson's disease within <i>Drosophila</i> based on objective spontaneous behavioral characteristics. Here, we present an automated multi-scale behavioral phenotyping pipeline that classifies phenotypes related to Parkinson's disease using motion features extracted from pose estimation data of wild-type and Synuclein Alpha E46K mutant <i>Drosophila melanogaster</i>. Locomotor activity was recorded in a custom-designed 3D-printed behavioral trap, and body kinematics were analyzed using a markerless pose estimation tool to extract numerical features such as movement speed, tremor-like oscillations, and limb motion patterns. Beyond kinematic analysis, we applied unsupervised clustering to the pose-derived trajectories to extract recurrent movement subtypes that characterize spontaneous behavioral sequences. We found that kinematic features alone were insufficient to distinguish mutant flies from normal individuals, whereas behavioral sequence patterns captured through unsupervised clustering enabled robust group separation. Combining both feature types further enhanced classification accuracy, with the best model achieving 85%. This system provides an objective and scalable approach for analyzing behavior related to Parkinson's disease in <i>Drosophila melanogaster</i>, with potential applications in monitoring disease progression and screening pharmaceutical compounds.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"105"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144552510","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":"Identification of mitophagy-related biomarkers in human rheumatoid arthritis using machine learning models.","authors":"Jiayi Chen, Zuhai Huang, Chengyu Qin, Zixiang Pang, Yuanming Chen","doi":"10.1080/21691401.2025.2533361","DOIUrl":"https://doi.org/10.1080/21691401.2025.2533361","url":null,"abstract":"<p><p>Rheumatoid arthritis (RA) is a systemic immune-mediated disease characterized by synovitis and joint cartilage destruction. Although many studies have shown that mitophagy is crucial in the development of bone metabolism disorders, its exact function in rheumatoid arthritis (RA) is still not well understood. This study analysed the GSE77298 dataset from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) between rheumatoid arthritis (RA) patients and healthy controls. Mitophagy-related genes (MRGs) were extracted from the literature and screened using bioinformatics techniques, resulting in differentially expressed MRGs (DE-MRGs). The diagnostic value of these genes was assessed using receiver operating characteristic (ROC) curves, and an ANN model was constructed. In the GSE77298 dataset, 267 differentially expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) identified 2191 key module genes, leading to 63 DE-MRGs. Two MRGs, TMEM45A and ZBTB25, were identified as hub genes with areas under the curve (AUC) of 0.991 and 0.911, respectively. The nomogram model demonstrated high diagnostic value. Mitophagy plays a critical role in the progression of rheumatoid arthritis (RA). Identifying two genes associated with mitophagy may aid in the early diagnosis, mechanistic understanding, and treatment of RA.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"287-303"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144648359","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}
Cognitive NeurodynamicsPub Date : 2025-12-01Epub Date: 2025-07-18DOI: 10.1007/s11571-025-10307-0
Bettina Bustos, Jiefeng Jiang, Wouter Kool
{"title":"Reward masks the learning of cognitive control demand.","authors":"Bettina Bustos, Jiefeng Jiang, Wouter Kool","doi":"10.1007/s11571-025-10307-0","DOIUrl":"https://doi.org/10.1007/s11571-025-10307-0","url":null,"abstract":"<p><p>Cognitive control refers to a set of cognitive functions that modulate other cognitive processes to align with internal goals. Recent research has shown that cognitive control can flexibly adapt to internal and external factors such as reward, effort, and environmental demands. This suggests that learning processes track changes in these factors and drive an optimization process to determine how cognitive control should be applied in changing situations. In real life, multiple factors often simultaneously affect how cognitive control is deployed. However, previous studies mainly concern how cognitive control adjusts to changes in a single factor. Here, we investigate how cognitive control learns to adjust to two concurrently changing factors: statistical regularity in cognitive control demand and performance-contingent reward. We consider two competing hypotheses: reward promotes cognitive control to adjust to cognitive control demand, and the processing of reward information obstructs the adaptation to cognitive control demand. In our experiment, statistical regularity in cognitive control demand is manipulated within subjects such that some stimuli require higher levels of cognitive control than others. Reward is manipulated across subjects. Using a computational model that captures temporal changes in cognitive control, we find that in the absence of reward, participants can adjust to different levels of cognitive control demand. Importantly, when performance-contingent reward is available, participants fail to adapt to changes in cognitive control demand. The findings support the hypothesis that reward blocks the learning of cognitive control.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-025-10307-0.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"114"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274171/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674029","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}
Si Su, Tengarile A, Ruhan A, Riga Wu, Lisi Wei, La Ta, Wenfeng Huo, Lijun Tong, Jing Zhang, Rilebagen Hu, Li Li
{"title":"Revealing the impact of gut microbiota-derived metabolites on depression through network pharmacology.","authors":"Si Su, Tengarile A, Ruhan A, Riga Wu, Lisi Wei, La Ta, Wenfeng Huo, Lijun Tong, Jing Zhang, Rilebagen Hu, Li Li","doi":"10.1080/21691401.2025.2531752","DOIUrl":"https://doi.org/10.1080/21691401.2025.2531752","url":null,"abstract":"<p><p>A total of 208 metabolites and 223 targets were initially extracted from the gutMGene v1.0 database. In addition, 1,630 and 1,321 targets were identified using the Similarity Ensemble Approach and Swiss Target Prediction databases, respectively, resulting in 921 overlapping targets. By integrating data from gutMGenev1.0, 13 core targets were finally identified. A microbiota-metabolite-target-signal pathway-disease network was constructed using Cytoscape 3.9.1, revealing 15 metabolites associated with the IL-17, TLR, and PI3K-Akt signalling pathways. Among these, five metabolites exhibited favourable drug similarity and acceptable toxicological profiles. Molecular docking confirmed the stable binding of two key metabolites-succinate and phenylacetylglutamine-to their respective targets. The results showed that succinate and phenylacetylglutamine demonstrated strong binding affinities to IL-1β and GSK3B, both involved in the IL-17, TLR, and PI3K-Akt signalling pathways. IL-17 and TLR4, as important pro-inflammatory cytokines, are closely associated with the development of depression, while the PI3K/AKT signalling pathway plays a key role in its pathogenesis. The present study reveals the potential mechanisms by which gut microbiota influence MDD and provides a scientific basis and a comprehensive research framework for future investigations.</p>","PeriodicalId":8736,"journal":{"name":"Artificial Cells, Nanomedicine, and Biotechnology","volume":"53 1","pages":"327-342"},"PeriodicalIF":4.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706120","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}
{"title":"The Role of SUMO1 Modification of SOX9 in Cartilage Development Stimulated by Zinc Ions in Mice.","authors":"Na Xue, Jing Zhao, Jing Yin, Liang Liu, Zhong Yang, Shuchao Zhai, Xiyun Bian, Xiang Gao","doi":"10.1080/15476278.2025.2460269","DOIUrl":"10.1080/15476278.2025.2460269","url":null,"abstract":"<p><p>Zinc ions play a pivotal role in facilitating the development of cartilage in mice. Nevertheless, the precise underlying mechanism remains elusive. Our investigation was centered on elucidating the impact of zinc deficiency on cartilage maturation by modulating SUMO1 and UBC9 at both the protein and mRNA levels. We administered a regimen inducing zinc deficiency to gravid mice from E0.5 until euthanasia. Subsequently, we subjected the embryos to scrutiny employing HE, Safranin O staining and IHC. Primary chondrocytes were isolated from fetal mouse femoral condyles and utilized for Western blot analysis to discern the expression profiles of SUMO1, SUMO2/3, UBC9, SOX9, MMP13, Collagen II, RUNX2, and aggrecan. Furthermore, ATDC5 murine chondrocytes were subjected to treatment with ZnCl<sub>2</sub>, followed by RT-PCR assessment to scrutinize the expression levels of MMP13, Collagen II, RUNX2, and aggrecan. Additionally, we conducted Co-IP assays on ZnCl<sub>2</sub>-treated ATDC5 cells to explore the interaction between SOX9 and SUMO1. Our investigation unveiled that zinc deficiency led to a reduction in cartilage development, as evidenced by the HE results in fetal murine femur. Moreover, diminished expression levels of SUMO1 and UBC9 were observed in the IHC and Western blot results. Furthermore, Western blot and Co-IP assays revealed an augmented interaction between SOX9 and SUMO1, which was potentiated by ZnCl<sub>2</sub> treatment. Significantly, mutations at the SUMOylation site of SOX9 resulted in alterations in the expression patterns of crucial chondrogenesis factors. This research underscores how zinc ions promote cartilage development through the modification of SOX9 by SUMO1.</p>","PeriodicalId":19596,"journal":{"name":"Organogenesis","volume":"21 1","pages":"2460269"},"PeriodicalIF":1.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11801356/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143190193","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}