Cognitive Neurodynamics最新文献_第2页

The role of beta band phase resetting in audio-visual temporal order judgment. 波段相位重置在视听时间顺序判断中的作用。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-15 DOI: 10.1007/s11571-024-10183-0

Yueying Li, Yasuki Noguchi

{"title":"The role of beta band phase resetting in audio-visual temporal order judgment.","authors":"Yueying Li, Yasuki Noguchi","doi":"10.1007/s11571-024-10183-0","DOIUrl":"https://doi.org/10.1007/s11571-024-10183-0","url":null,"abstract":"The integration of auditory and visual stimuli is essential for effective language processing and social perception. The present study aimed to elucidate the mechanisms underlying audio-visual (A-V) integration by investigating the temporal dynamics of multisensory regions in the human brain. Specifically, we evaluated inter-trial coherence (ITC), a neural index indicative of phase resetting, through scalp electroencephalography (EEG) while participants performed a temporal-order judgment task that involved auditory (beep, A) and visual (flash, V) stimuli. The results indicated that ITC phase resetting was greater for bimodal (A + V) stimuli compared to unimodal (A or V) stimuli in the posterior temporal region, which resembled the responses of A-V multisensory neurons reported in animal studies. Furthermore, the ITC got lager as the stimulus-onset asynchrony (SOA) between beep and flash approached 0 ms. This enhancement in ITC was most clearly seen in the beta band (13-30 Hz). Overall, these findings highlight the importance of beta rhythm activity in the posterior temporal cortex for the detection of synchronous audiovisual stimuli, as assessed through temporal order judgment tasks.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-024-10183-0.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"28"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001022","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}

引用次数: 0

Aberrant regional neural fluctuations and functional connectivity in insomnia comorbid depression revealed by resting-state functional magnetic resonance imaging. 静息状态功能磁共振成像揭示失眠伴发抑郁症的异常区域神经波动和功能连通性。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-06 DOI: 10.1007/s11571-024-10206-w

Shuang Wang, Bo Li, Minghe Xu, Chunlian Chen, Zhe Liu, Yuqing Ji, Shaowen Qian, Kai Liu, Gang Sun

{"title":"Aberrant regional neural fluctuations and functional connectivity in insomnia comorbid depression revealed by resting-state functional magnetic resonance imaging.","authors":"Shuang Wang, Bo Li, Minghe Xu, Chunlian Chen, Zhe Liu, Yuqing Ji, Shaowen Qian, Kai Liu, Gang Sun","doi":"10.1007/s11571-024-10206-w","DOIUrl":"https://doi.org/10.1007/s11571-024-10206-w","url":null,"abstract":"Insomnia is a common mental illness seriously affecting people lives, that might progress to major depression. However, the neural mechanism of patients with CID comorbid MDD remain unclear. Combining fractional amplitude of low-frequency fluctuation (fALFF) and seed-based functional connectivity (FC), this study investigated abnormality in local and long-range neural activity of patients with CID comorbid MDD. Here, we acquired resting-state blood oxygenation level dependent (BOLD) data from 57 patients with CID comorbid MDD and 57 healthy controls (HC). Compared with the controls, patients with CID comorbid MDD exhibited abnormal functional activity in posterior cerebral cortex related to the visual cortex, including the middle occipital gyrus (MOG), the cuneus and the lingual gyrus, specifically, lower fALFF values in the right MOG, left cuneus, and right postcentral gyrus, increased FC between the right MOG and the left cerebellum, and decreased FC between the right MOG and the right lingual gyrus. Neuropsychological correlation analysis revealed that the decreased fALFF in the right MOG was negatively correlated with all the neuropsychological scores of insomnia and depression, reflecting common relationships with symptoms of CID and MDD. While the decreased fALFF of the left cuneus was distinctly correlated with the scores of depression related scales. The decreased FC between the right MOG and the right lingual gyrus was distinctly correlated with the scores of insomnia related scales. This study not only widened neuroimaging evidence that associated with insomnia and depressive symptoms of patients with CID comorbid MDD, but also provided new potential targets for clinical treatment.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"8"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11704111/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142945837","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}

引用次数: 0

Reconstruction and application of multilayer brain network for juvenile myoclonic epilepsy based on link prediction. 基于链路预测的青少年肌阵挛性癫痫多层脑网络重建及应用。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-06 DOI: 10.1007/s11571-024-10191-0

Ming Ke, Xinyi Yao, Peihui Cao, Guangyao Liu

{"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":"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.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-024-10191-0.","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}

引用次数: 0

Unraveling the functional complexity of the locus coeruleus-norepinephrine system: insights from molecular anatomy to neurodynamic modeling.

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-23 DOI: 10.1007/s11571-024-10208-8

Chun-Wang Su, Fan Yang, Runchen Lai, Yanhai Li, Hadia Naeem, Nan Yao, Si-Ping Zhang, Haiqing Zhang, Youjun Li, Zi-Gang Huang

引用次数: 0

Multi-view domain adaption based multi-scale convolutional conditional invertible discriminator for cross-subject electroencephalogram emotion recognition. 基于多视域自适应的多尺度卷积条件可逆判别器在跨主体脑电图情绪识别中的应用。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-13 DOI: 10.1007/s11571-024-10193-y

Sivasaravana Babu S, Prabhu Venkatesan, Parthasarathy Velusamy, Saravana Kumar Ganesan

{"title":"Multi-view domain adaption based multi-scale convolutional conditional invertible discriminator for cross-subject electroencephalogram emotion recognition.","authors":"Sivasaravana Babu S, Prabhu Venkatesan, Parthasarathy Velusamy, Saravana Kumar Ganesan","doi":"10.1007/s11571-024-10193-y","DOIUrl":"https://doi.org/10.1007/s11571-024-10193-y","url":null,"abstract":"Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people. Accurately predicting emotions poses challenges due to dynamic traits. Models struggle with feature extraction, convergence, and negative transfer issues, hindering cross subject emotion recognition. The proposed model employs thorough signal preprocessing, Short-Time Geodesic Flow Kernel Fourier Transform (STGFKFT) for feature extraction, enhancing classifiers' accuracy. Multi-view sheaf attention improves feature discrimination, while the Multi-Scale Convolutional Conditional Invertible Puma Discriminator Neural Network (MSCCIPDNN) framework ensures generalization. Efficient computational techniques and the puma optimization algorithm enhance model robustness and convergence. The suggested framework demonstrates extraordinary success with high accuracy, of 99.5%, 99% and 99.50% for SEED, SEED-IV, and DEAP dataset sequentially. By incorporating these techniques, the proposed method aims to precisely recognition emotions, and accurately captures the features, thereby overcoming the limitations of existing methodologies.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"23"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729617/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001038","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}

引用次数: 0

An effective encryption approach using a combination of a non-chain ring and a four-dimensional chaotic map. 一种利用非链环和四维混沌映射相结合的有效加密方法。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-15 DOI: 10.1007/s11571-025-10217-1

Muhammad Umair Safdar, Tariq Shah, Asif Ali

{"title":"An effective encryption approach using a combination of a non-chain ring and a four-dimensional chaotic map.","authors":"Muhammad Umair Safdar, Tariq Shah, Asif Ali","doi":"10.1007/s11571-025-10217-1","DOIUrl":"https://doi.org/10.1007/s11571-025-10217-1","url":null,"abstract":"Algebraic structures are highly effective in designing symmetric key cryptosystems; however, if the key space is not sufficiently large, such systems become vulnerable to brute-force attacks. To address this challenge, our research focuses on enlarging the key space in symmetric key schemes by integrating the non-chain ring with a four-dimensional chaotic system. While chaotic maps offer significant potential for data processing, relying solely on them does not fully leverage their operational advantages. Therefore, it is essential to incorporate algebraic structures that enhance the complexity of the scheme. In the proposed technique, four-dimensional chaotic sequences are employed for image pixel permutation, diffusion, and exclusive-or operations. The scheme is further strengthened against chosen and known plaintext attacks by incorporating pixel values during the exclusive-or operation, where images are XORed with hashed images and keys generated from chaotic sequences. The effectiveness of the technique, its resilience to various forms of attack, and its feasibility for practical implementation are demonstrated through extensive testing and a comprehensive security analysis.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"27"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143000913","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}

引用次数: 0

3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data. 利用静息状态fMRI数据增强自闭症谱系障碍诊断的3T扩展初始网络。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-13 DOI: 10.1007/s11571-024-10202-0

V Kavitha, R Siva

{"title":"3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data.","authors":"V Kavitha, R Siva","doi":"10.1007/s11571-024-10202-0","DOIUrl":"https://doi.org/10.1007/s11571-024-10202-0","url":null,"abstract":"Autism spectrum disorder (ASD) is one of the complicated neurodevelopmental disorders that impacts the daily functioning and social interactions of individuals. It includes diverse symptoms and severity levels, making it challenging to diagnose and treat efficiently. Various deep learning (DL) based methods have been developed for diagnosing ASD, which rely heavily on behavioral assessment. However, existing techniques have suffered from poor diagnostic outcomes, higher computational complexity, and overfitting issues. To address these challenges, this research work introduces an innovative framework called 3T Dilated Inception Network (3T-DINet) for effective ASD diagnosis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) images. The proposed 3T-DINet technique designs a 3T dilated inception module that incorporates dilated convolutions along with the inception module, allowing it to extract multi-scale features from brain connectivity patterns. The 3T dilated inception module uses three distinct dilation rates (low, medium, and high) in parallel to determine local, mid-level, and global features from the brain. In addition, the proposed approach implements Residual networks (ResNet) to avoid the vanishing gradient problem and enhance the feature extraction ability. The model is further optimized using a Crossover-based Black Widow Optimization (CBWO) algorithm that fine-tunes the hyperparameters thereby enhancing the overall performance of the model. Further, the performance of the 3T-DINet model is evaluated using the five ASD datasets with distinct evaluation parameters. The proposed 3T-DINet technique achieved superior diagnosis results compared to recent previous works. From this simulation validation, it's clear that the 3T-DINet provides an excellent contribution to early ASD diagnosis and enhances patient treatment outcomes.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"22"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001551","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}

引用次数: 0

Implementation of memristive emotion associative learning circuit. 记忆性情感联想学习电路的实现。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10211-z

Zhangzhi Zhou, Mi Lin, Xuanxuan Zhou, Chong Zhang

引用次数: 0

Optimal time-frequency localized wavelet filters for identification of Alzheimer's disease from EEG signals. 基于时频局部化小波滤波的脑电信号阿尔茨海默病识别。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10198-7

Digambar V Puri, Jayanand P Gawande, Pramod H Kachare, Ibrahim Al-Shourbaji

{"title":"Optimal time-frequency localized wavelet filters for identification of Alzheimer's disease from EEG signals.","authors":"Digambar V Puri, Jayanand P Gawande, Pramod H Kachare, Ibrahim Al-Shourbaji","doi":"10.1007/s11571-024-10198-7","DOIUrl":"10.1007/s11571-024-10198-7","url":null,"abstract":"Alzheimer's disease (AD) is a chronic disability that occurs due to the loss of neurons. The traditional methods to detect AD involve questionnaires and expensive neuro-imaging tests, which are time-consuming, subjective, and inconvenient to the target population. To overcome these limitations, Electroencephalogram (EEG) based methods have been developed to classify AD patients from normal controlled (NC) and mild cognitive impairment (MCI) subjects. Most of the EEG-based methods involved entropy-based feature extraction and discrete wavelet transform. However, the existing AD classification methods failed to provide promising classification accuracy. Here, we proposed a wavelet-machine learning (ML) framework to detect AD using a newly designed biorthogonal filter bank by optimization of frequency and time localization of triplet halfband filter banks (OTFL-THFB). The OTFL-THFB decomposes EEG signals into various EEG sub- bands. Hjorth Parameters (HP) and Higuchi's Fractal Dimension (HFD) have been investigated to extract features from each EEG subband. Subsequently, ML models are trained and tested using different features such as OTFL-THFB with HFD, OTFL-THFB with HP, and OTFL-THFB with HFD and HP used for detecting AD with 10-fold cross-validation. This method was applied to two publicly available datasets. Our model achieved an accuracy of <math><mrow><mn>98.91</mn> <mo>%</mo></mrow> </math> for AD versus NC and <math><mrow><mn>98.65</mn> <mo>%</mo></mrow> </math> for AD versus MCI versus NC using the least square support vector machine. Results indicate that this framework surpassed existing state-of-the-art techniques for classifying AD from NC.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"12"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717779/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969957","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}

引用次数: 0

The phonation test can distinguish the patient with Parkinson's disease via Bayes inference. 语音测试可以通过贝叶斯推理来区分帕金森病患者。

IF 3.1 3区工程技术

Cognitive Neurodynamics Pub Date : 2025-12-01 Epub Date: 2025-01-09 DOI: 10.1007/s11571-024-10194-x

Yifeng Liu, Hongjie Gong, Meimei Mouse, Fan Xu, Xianwei Zou, Jingsheng Yang, Qingping Xue, Min Huang

{"title":"The phonation test can distinguish the patient with Parkinson's disease via Bayes inference.","authors":"Yifeng Liu, Hongjie Gong, Meimei Mouse, Fan Xu, Xianwei Zou, Jingsheng Yang, Qingping Xue, Min Huang","doi":"10.1007/s11571-024-10194-x","DOIUrl":"10.1007/s11571-024-10194-x","url":null,"abstract":"Parkinson's disease (PD) is a neurodegenerative disease with various clinical manifestations caused by multiple risk factors. However, the effect of different factors and relationships between different features related to PD and the extent of those factors leading to the incidence of PD remains unclear. we employed Bayesian network to construct a prediction model. The prediction system was trained on the data of 35 patients and 26 controls. The structure learning and parameter learning of Bayesian Network was completed through the tree-augmented network (TAN) and Netica software, respectively. We employed four Bayesian Networks in terms of the syllable, including monosyllables, disyllables, multisyllables and unsegmented syllables. The area under the curve (AUC) of monosyllabic, disyllabic, multisyllabic, and unsegmented-syllable models were 0.95, 0.83, 0.80 and 0.84, respectively. In the monosyllabic tests, the best predictor of PD was duration, the posterior probability of which was 92.70%. Meanwhile, minimum f0 (61.60%) predicted best in the disyllabic tests and the variables that predicted best in multisyllables and unsegmented syllables were end f0 (59.40%) and maximum f0 (58.40%). In the cross-sectional comparison, the prediction effect of each variable in the monosyllabic tests was generally higher than that of other test groups. The monosyllabic models had the highest predicted performance of PD. Among acoustic parameters, duration was the strongest feature in predicting the prevalence of PD in monosyllabic tests. We believe that this network methodology will be a useful tool for the clinical prediction of Parkinson's disease.Supplementary information: The online version contains supplementary material available at 10.1007/s11571-024-10194-x.","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"19 1","pages":"18"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11717751/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142969961","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}

引用次数: 0