首页 > 最新文献

Cognitive Neurodynamics最新文献

英文 中文
Automatic detection of Alzheimer’s disease from EEG signals using an improved AFS–GA hybrid algorithm 使用改进的 AFS-GA 混合算法从脑电图信号中自动检测阿尔茨海默病
IF 3.7 3区 工程技术 Q2 NEUROSCIENCES Pub Date : 2024-06-10 DOI: 10.1007/s11571-024-10130-z
Ruofan Wang, Qiguang He, Lianshuan Shi, Yanqiu Che, Haojie Xu, Changzhi Song
{"title":"Automatic detection of Alzheimer’s disease from EEG signals using an improved AFS–GA hybrid algorithm","authors":"Ruofan Wang, Qiguang He, Lianshuan Shi, Yanqiu Che, Haojie Xu, Changzhi Song","doi":"10.1007/s11571-024-10130-z","DOIUrl":"https://doi.org/10.1007/s11571-024-10130-z","url":null,"abstract":"","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141363812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dynamic analysis of FN–HR neural network coupled of bistable memristor and encryption application based on Fibonacci Q-Matrix 双稳态忆阻器耦合 FN-HR 神经网络的动态分析以及基于斐波那契 Q 矩阵的加密应用
IF 3.7 3区 工程技术 Q2 NEUROSCIENCES Pub Date : 2024-06-10 DOI: 10.1007/s11571-023-10025-5
Junwei Sun, Chuangchuang Li, Yanfeng Wang, Zicheng Wang
{"title":"Dynamic analysis of FN–HR neural network coupled of bistable memristor and encryption application based on Fibonacci Q-Matrix","authors":"Junwei Sun, Chuangchuang Li, Yanfeng Wang, Zicheng Wang","doi":"10.1007/s11571-023-10025-5","DOIUrl":"https://doi.org/10.1007/s11571-023-10025-5","url":null,"abstract":"","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fixed-/preassigned-time synchronization for delayed complex-valued neural networks with discontinuous activations 具有不连续激活的延迟复值神经网络的固定/预分配时间同步
IF 3.7 3区 工程技术 Q2 NEUROSCIENCES Pub Date : 2024-06-07 DOI: 10.1007/s11571-024-10129-6
Leping Hu, Lian Duan
{"title":"Fixed-/preassigned-time synchronization for delayed complex-valued neural networks with discontinuous activations","authors":"Leping Hu, Lian Duan","doi":"10.1007/s11571-024-10129-6","DOIUrl":"https://doi.org/10.1007/s11571-024-10129-6","url":null,"abstract":"","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis 对昏迷和脑死亡诊断中的脑电信号进行定量分析和基于机器学习的解读
IF 3.7 3区 工程技术 Q2 NEUROSCIENCES Pub Date : 2024-06-06 DOI: 10.1007/s11571-024-10131-y
Boning Li, Jinsha Liu, Tao Zhang, Yang Cao, Jianting Cao
{"title":"Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis","authors":"Boning Li, Jinsha Liu, Tao Zhang, Yang Cao, Jianting Cao","doi":"10.1007/s11571-024-10131-y","DOIUrl":"https://doi.org/10.1007/s11571-024-10131-y","url":null,"abstract":"","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141378430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data-driven natural computational psychophysiology in class 课堂上的数据驱动自然计算心理生理学
IF 3.7 3区 工程技术 Q2 Neuroscience Pub Date : 2024-06-04 DOI: 10.1007/s11571-024-10126-9
Yong Huang, Yuxiang Huan, Zhuo Zou, Yijun Wang, Xiaorong Gao, Lirong Zheng

Objective. The assessment of mental fatigue (MF) and attention span in educational and healthcare settings frequently relies on subjective scales or methods such as induced-task interruption tools. However, these approaches are deficient in real-time evaluation and dynamic definitions. To address this gap, this paper proposes a Continuous Quantitative Scale (CQS) that allows for the natural and real-time measurement of MF based on group-synchronized electroencephalogram (EEG) data. Approach. In this study, computational psychophysiology was used to measure MF scores during a realistic class. Our methodology continuously monitored participants’ psychological states without interrupting their regular routines, providing an objective evaluation. By analyzing multi-subject brain-computer interface (mBCI) data with a collaborative computing approach, the group-synchronized data were obtained from 10 healthy participants to assess MF levels. Each participant wore an EEG headset for only 10 min of preparation before performing a sustained task for 80 min. Main results. Our findings indicate that a lecture duration of 18.9 min is most effective, while a duration of 43.1 min leads to heightened MF levels. By focusing on the group-level simultaneous data analysis, the effects of individual variability were mitigated and the efficiency of cognitive computing was improved. From the perspective of a neurocomputational measure, these results confirm previous research. Significance. The proposed CQS provides a reliable, objective, memory- and emotion-free approach to the assessment of MF and attention span. These findings have significant implications not only for education, but also for the study of group cognitive mechanisms and for improving the quality of mental healthcare.

目的。在教育和医疗机构中,对精神疲劳(MF)和注意力集中时间的评估通常依赖于主观量表或诱导任务中断工具等方法。然而,这些方法在实时评估和动态定义方面存在不足。为了弥补这一不足,本文提出了一种连续定量量表(CQS),可根据群体同步脑电图(EEG)数据自然、实时地测量精神疲劳度。方法。在本研究中,计算心理生理学被用于测量现实课堂中的 MF 分数。我们的方法能在不影响参与者正常作息的情况下持续监测他们的心理状态,从而提供客观的评估。通过使用协同计算方法分析多主体脑机接口(mBCI)数据,从 10 名健康参与者那里获得了群体同步数据,以评估中频水平。每位参与者在执行一项持续 80 分钟的任务之前,只需佩戴脑电图耳机进行 10 分钟的准备工作。主要结果。我们的研究结果表明,18.9 分钟的授课时间最有效,而 43.1 分钟的授课时间则会导致中频水平的提高。通过侧重于群体层面的同步数据分析,减轻了个体差异的影响,提高了认知计算的效率。从神经计算测量的角度来看,这些结果证实了之前的研究。意义重大。所提出的 CQS 提供了一种可靠、客观、无记忆和无情绪的方法来评估中频和注意广度。这些发现不仅对教育,而且对研究群体认知机制和提高心理保健质量都具有重要意义。
{"title":"Data-driven natural computational psychophysiology in class","authors":"Yong Huang, Yuxiang Huan, Zhuo Zou, Yijun Wang, Xiaorong Gao, Lirong Zheng","doi":"10.1007/s11571-024-10126-9","DOIUrl":"https://doi.org/10.1007/s11571-024-10126-9","url":null,"abstract":"<p><i>Objective.</i> The assessment of mental fatigue (MF) and attention span in educational and healthcare settings frequently relies on subjective scales or methods such as induced-task interruption tools. However, these approaches are deficient in real-time evaluation and dynamic definitions. To address this gap, this paper proposes a Continuous Quantitative Scale (CQS) that allows for the natural and real-time measurement of MF based on group-synchronized electroencephalogram (EEG) data. <i>Approach.</i> In this study, computational psychophysiology was used to measure MF scores during a realistic class. Our methodology continuously monitored participants’ psychological states without interrupting their regular routines, providing an objective evaluation. By analyzing multi-subject brain-computer interface (mBCI) data with a collaborative computing approach, the group-synchronized data were obtained from 10 healthy participants to assess MF levels. Each participant wore an EEG headset for only 10 min of preparation before performing a sustained task for 80 min. <i>Main results.</i> Our findings indicate that a lecture duration of 18.9 min is most effective, while a duration of 43.1 min leads to heightened MF levels. By focusing on the group-level simultaneous data analysis, the effects of individual variability were mitigated and the efficiency of cognitive computing was improved. From the perspective of a neurocomputational measure, these results confirm previous research. <i>Significance.</i> The proposed CQS provides a reliable, objective, memory- and emotion-free approach to the assessment of MF and attention span. These findings have significant implications not only for education, but also for the study of group cognitive mechanisms and for improving the quality of mental healthcare.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis. 机器学习与多模态神经成像数据分类阿尔茨海默病的阶段:系统回顾和荟萃分析
IF 3.7 3区 工程技术 Q2 Neuroscience Pub Date : 2024-06-01 Epub Date: 2023-08-18 DOI: 10.1007/s11571-023-09993-5
Modupe Odusami, Rytis Maskeliūnas, Robertas Damaševičius, Sanjay Misra

In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87-87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.

近年来,阿尔茨海默病(AD)已严重威胁人类健康。研究人员和临床医生在试图准确识别和划分阿尔茨海默病的阶段时都遇到了巨大的障碍。多项研究表明,多模态神经成像输入可以帮助人们深入了解与阿兹海默症有关的大脑结构和功能变化。机器学习(ML)算法可以利用强大的计算方法识别多模态神经影像数据中的模式和联系,从而准确地对 AD 阶段进行分类。本研究旨在评估 ML 方法对使用多模态神经影像数据准确划分 AD 阶段的贡献。我们在 IEEE Xplore、Science Direct/Elsevier、ACM DigitalLibrary 和 PubMed 数据库中进行了系统搜索,并在 Google Scholar 上进行了前向滚雪球式搜索。定量分析使用了 47 项研究。对所选研究中使用的分类算法和融合方法进行了可解释性分析。通过对多模态神经影像数据和 ML 方法在 AD 分期分类中的分层汇总接收器操作特征(ROC)曲线进行基于双变量模型的荟萃分析,评估了汇总的敏感性和特异性,包括诊断效率。Wilcoxon 符号秩检验进一步用于统计比较现有模型的准确性得分。在 95% 置信区间(78.87%-87.71%)内,将轻度认知障碍(MCI)患者与健康对照(NC)患者区分开来的综合灵敏度为 83.77%;将 AD 患者与 NC 患者区分开来的综合灵敏度为 94.60% (90.76%, 96.89%);将进行性 MCI(pMCI)患者与稳定型 MCI(sMCI)患者区分开来的综合灵敏度为 80.41% (74.73%, 85.06%)。在 95% 的置信区间(78.87%,87.71%)内,区分轻度认知障碍(MCI)和健康对照(NC)参与者的汇总灵敏度为 83.77%,在 95% 的置信区间(90.76%,96.89%)内,区分轻度认知障碍(MCI)和健康对照(NC)参与者的汇总灵敏度为 83.77%。将 AD 与 NC 区分开来的汇总灵敏度为 94.60%,将 MCI 与健康对照(NC)参与者区分开来的汇总灵敏度为 83.77%,将进行性 MCI(pMCI)与稳定型 MCI(sMCI)区分开来的汇总灵敏度为 80.41%(74.73%,85.06%),将早期 MCI(EMCI)与 NC 区分开来的汇总灵敏度为 86.63%(82.43%,89.95%)。区分 MCI 和 NC 的汇总特异性为 79.16% (70.97%, 87.71%),区分 AD 和 NC 的汇总特异性为 93.49% (91.60%, 94.90%),区分 pMCI 和 sMCI 的汇总特异性为 81.44% (76.32%, 85.66%),区分 EMCI 和 NC 的汇总特异性为 85.68% (81.62%, 88.96%)。Wilcoxon 符号秩检验显示,所有分类任务的 P 值都很低。多模态神经影像数据与 ML 在对 AD 分期进行分类方面前景广阔,但要提高其在临床实践中应用的有效性,还需要更多的研究。
{"title":"Machine learning with multimodal neuroimaging data to classify stages of Alzheimer's disease: a systematic review and meta-analysis.","authors":"Modupe Odusami, Rytis Maskeliūnas, Robertas Damaševičius, Sanjay Misra","doi":"10.1007/s11571-023-09993-5","DOIUrl":"10.1007/s11571-023-09993-5","url":null,"abstract":"<p><p>In recent years, Alzheimer's disease (AD) has been a serious threat to human health. Researchers and clinicians alike encounter a significant obstacle when trying to accurately identify and classify AD stages. Several studies have shown that multimodal neuroimaging input can assist in providing valuable insights into the structural and functional changes in the brain related to AD. Machine learning (ML) algorithms can accurately categorize AD phases by identifying patterns and linkages in multimodal neuroimaging data using powerful computational methods. This study aims to assess the contribution of ML methods to the accurate classification of the stages of AD using multimodal neuroimaging data. A systematic search is carried out in IEEE Xplore, Science Direct/Elsevier, ACM DigitalLibrary, and PubMed databases with forward snowballing performed on Google Scholar. The quantitative analysis used 47 studies. The explainable analysis was performed on the classification algorithm and fusion methods used in the selected studies. The pooled sensitivity and specificity, including diagnostic efficiency, were evaluated by conducting a meta-analysis based on a bivariate model with the hierarchical summary receiver operating characteristics (ROC) curve of multimodal neuroimaging data and ML methods in the classification of AD stages. Wilcoxon signed-rank test is further used to statistically compare the accuracy scores of the existing models. With a 95% confidence interval of 78.87-87.71%, the combined sensitivity for separating participants with mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%; for separating participants with AD from NC, it was 94.60% (90.76%, 96.89%); for separating participants with progressive MCI (pMCI) from stable MCI (sMCI), it was 80.41% (74.73%, 85.06%). With a 95% confidence interval (78.87%, 87.71%), the Pooled sensitivity for distinguishing mild cognitive impairment (MCI) from healthy control (NC) participants was 83.77%, with a 95% confidence interval (90.76%, 96.89%), the Pooled sensitivity for distinguishing AD from NC was 94.60%, likewise (MCI) from healthy control (NC) participants was 83.77% progressive MCI (pMCI) from stable MCI (sMCI) was 80.41% (74.73%, 85.06%), and early MCI (EMCI) from NC was 86.63% (82.43%, 89.95%). Pooled specificity for differentiating MCI from NC was 79.16% (70.97%, 87.71%), AD from NC was 93.49% (91.60%, 94.90%), pMCI from sMCI was 81.44% (76.32%, 85.66%), and EMCI from NC was 85.68% (81.62%, 88.96%). The Wilcoxon signed rank test showed a low P-value across all the classification tasks. Multimodal neuroimaging data with ML is a promising future in classifying the stages of AD but more research is required to increase the validity of its application in clinical practice.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46398929","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
Exploration of interictal to ictal transition in epileptic seizures using a neural mass model. 应用神经质量模型探讨癫痫发作发作间期到发作期的转换
IF 3.7 3区 工程技术 Q2 Neuroscience Pub Date : 2024-06-01 Epub Date: 2023-05-16 DOI: 10.1007/s11571-023-09976-6
Chunfeng Yang, Qingbo Luo, Huazhong Shu, Régine Le Bouquin Jeannès, Jianqing Li, Wentao Xiang

An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation-inhibition (E-I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E-I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-023-09976-6.

癫痫发作通常可分为三个阶段:发作间期、发作前和发作期。然而,大脑中从发作间期向发作期活动过渡的癫痫发作涉及神经元群抑制和兴奋之间复杂的相互作用。为了在单个群体的水平上探索这一机制,本文采用了一种名为 "基于生理学的完整模型(cPBM)"的神经群模型来重建脑电图(EEG)信号,并根据记录的十名癫痫患者的开放数据集推断与兴奋-抑制(E-I)平衡相关的兴奋/抑制连接的变化。由于癫痫信号具有频谱特征,因此采用了频谱动态因果建模(DCM),通过最大化功率谱密度(PSD)框架下的自由能和估计 cPBM 参数来量化这些频率特性。此外,为了解决 DCM 可能存在的局部最大值问题,还提出了一种混合确定性 DCM(H-DCM)方法,在两个方向上应用基于确定性退火的方案。H-DCM 方法通过逐步降低温度来调整目标函数中引入的温度,以获得相对较好的初始化,然后在每次最大化后逐步提高温度以寻找更好的估计值。结果表明:(i) 属于三个阶段的重构脑电信号及其 PSD 均可从 cPBM 的估计参数中再现;(ii) 与 DCM、传统 D-DCM 和反 D-DCM 相比,所提出的 H-DCM 显示出更高的自由能和更低的均方根误差(RMSE),并且在所有阶段都具有最佳性能(例如,重构脑电信号和 PSD 之间的均方根误差(RMSE)均低于 DCM);(iii) 与 DCM 相比,所提出的 H-DCM 显示出更高的自由能和更低的均方根误差(RMSE)、根据重建脑电信号计算的重建 PSD 与根据真实脑电信号获得的样本 PSD 之间的均方根误差分别为 0.33 ± 0.08、0.67 ± 0.37 和 0.78 ± 0.57);(iii) 锥体细胞与兴奋性中间神经元之间、锥体细胞与快速抑制性中间神经元之间的连接增加,以及 cPBM 中快速抑制性中间神经元的自环连接减少,可以解释从发作间期到发作期活动的过渡。此外,E-I 平衡(定义为锥体细胞与快速抑制性中间神经元之间的兴奋性连接与快速抑制性中间神经元自环的抑制性连接之间的比率)在癫痫发作过渡期间也显著增加:在线版本包含补充材料,可查阅 10.1007/s11571-023-09976-6。
{"title":"Exploration of interictal to ictal transition in epileptic seizures using a neural mass model.","authors":"Chunfeng Yang, Qingbo Luo, Huazhong Shu, Régine Le Bouquin Jeannès, Jianqing Li, Wentao Xiang","doi":"10.1007/s11571-023-09976-6","DOIUrl":"10.1007/s11571-023-09976-6","url":null,"abstract":"<p><p>An epileptic seizure can usually be divided into three stages: interictal, preictal, and ictal. However, the seizure underlying the transition from interictal to ictal activities in the brain involves complex interactions between inhibition and excitation in groups of neurons. To explore this mechanism at the level of a single population, this paper employed a neural mass model, named the complete physiology-based model (cPBM), to reconstruct electroencephalographic (EEG) signals and to infer the changes in excitatory/inhibitory connections related to excitation-inhibition (E-I) balance based on an open dataset recorded for ten epileptic patients. Since epileptic signals display spectral characteristics, spectral dynamic causal modelling (DCM) was applied to quantify these frequency characteristics by maximizing the free energy in the framework of power spectral density (PSD) and estimating the cPBM parameters. In addition, to address the local maximum problem that DCM may suffer from, a hybrid deterministic DCM (H-DCM) approach was proposed, with a deterministic annealing-based scheme applied in two directions. The H-DCM approach adjusts the temperature introduced in the objective function by gradually decreasing the temperature to obtain relatively good initialization and then gradually increasing the temperature to search for a better estimation after each maximization. The results showed that (i) reconstructed EEG signals belonging to the three stages together with their PSDs can be reproduced from the estimated parameters of the cPBM; (ii) compared to DCM, traditional D-DCM and anti D-DCM, the proposed H-DCM shows higher free energies and lower root mean square error (RMSE), and it provides the best performance for all stages (e.g., the RMSEs between the reconstructed PSD computed from the reconstructed EEG signal and the sample PSD obtained from the real EEG signal are 0.33 ± 0.08, 0.67 ± 0.37 and 0.78 ± 0.57 in the interictal, preictal and ictal stages, respectively); and (iii) the transition from interictal to ictal activity can be explained by an increase in the connections between pyramidal cells and excitatory interneurons and between pyramidal cells and fast inhibitory interneurons, as well as a decrease in the self-loop connection of the fast inhibitory interneurons in the cPBM. Moreover, the E-I balance, defined as the ratio between the excitatory connection from pyramidal cells to fast inhibitory interneurons and the inhibitory connection with the self-loop of fast inhibitory interneurons, is also significantly increased during the epileptic seizure transition.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-023-09976-6.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47024005","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
Alternative muscle synergy patterns of upper limb amputees. 上肢截肢者的替代性肌肉协同模式
IF 3.7 3区 工程技术 Q2 Neuroscience Pub Date : 2024-06-01 Epub Date: 2023-04-26 DOI: 10.1007/s11571-023-09969-5
Xiaojun Wang, Junlin Wang, Ningbo Fei, Dehao Duanmu, Beibei Feng, Xiaodong Li, Wing-Yuk Ip, Yong Hu

Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.

肌电假手是上肢截肢者恢复手部功能的有效工具。模式识别算法在识别表面肌电图(sEMG)模式方面取得了很大进展,但很少有人关注截肢者的运动学习过程。许多潜在的肌电假肢使用者无法完全掌握控制方法,或者随着时间的推移,其表现有所下降。学习用残肢产生独特而一致的肌肉激活模式可能有助于截肢者更好地控制肌电假肢。在这项研究中,我们观察了两名截肢者运动技能学习的纵向效果,他们针对同一组目标假肢动作形成了不同的肌肉激活模式。在为期 10 周的训练中,截肢者接受了在实时 sEMG 视觉反馈和不与假肢互动的情况下产生独特而持续的肌肉激活的训练。训练结束后,他们的 sEMG 模式与其他参与者和非截肢者对照组有所不同。对于某些预期的手部运动,观察到均方根方差逐渐减小。此外,还利用专为移动 sEMG 模式识别设计的 CNN-LSTM 混合分类器评估了学习效果。随着时间的推移,分类准确率呈上升趋势,这意味着肌电假肢控制的性能有可能得到改善。该计划实施 6 个月后进行了一次后续训练,结果表明,运动技能学习在 sEMG 模式分类准确性方面产生了持久的影响。结果表明,通过适当的反馈训练,截肢者可以学习到独特的肌肉激活模式,从而触发预期的假肢功能,并更新原有的运动控制方案。这种运动技能学习的效果有助于提高肌电假肢的控制性能。
{"title":"Alternative muscle synergy patterns of upper limb amputees.","authors":"Xiaojun Wang, Junlin Wang, Ningbo Fei, Dehao Duanmu, Beibei Feng, Xiaodong Li, Wing-Yuk Ip, Yong Hu","doi":"10.1007/s11571-023-09969-5","DOIUrl":"10.1007/s11571-023-09969-5","url":null,"abstract":"<p><p>Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand functions. Much progress has been made with pattern recognition algorithms to recognize surface electromyography (sEMG) patterns, but few attentions was placed on the amputees' motor learning process. Many potential myoelectric prostheses users could not fully master the control or had declined performance over time. It is possible that learning to produce distinct and consistent muscle activation patterns with the residual limb could help amputees better control the myoelectric prosthesis. In this study, we observed longitudinal effect of motor skill learning with 2 amputees who have developed alternative muscle activation patterns in response to the same set of target prosthetic actions. During a 10-week program, amputee participants were trained to produce distinct and constant muscle activations with visual feedback of live sEMG and without interaction with prosthesis. At the end, their sEMG patterns were different from each other and from non-amputee control groups. For certain intended hand motion, gradually reducing root mean square (RMS) variance was observed. The learning effect was also assessed with a CNN-LSTM mixture classifier designed for mobile sEMG pattern recognition. The classification accuracy had a rising trend over time, implicating potential performance improvement of myoelectric prosthesis control. A follow-up session took place 6 months after the program and showed lasting effect of the motor skill learning in terms of sEMG pattern classification accuracy. The results indicated that with proper feedback training, amputees could learn unique muscle activation patterns that allow them to trigger intended prosthesis functions, and the original motor control scheme is updated. The effect of such motor skill learning could help to improve myoelectric prosthetic control performance.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143172/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47482132","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
EEG decoding for effects of visual joint attention training on ASD patients with interpretable and lightweight convolutional neural network. 应用可解释和轻量级卷积神经网络对ASD患者视觉联合注意训练的脑电图解码效果
IF 3.7 3区 工程技术 Q2 Neuroscience Pub Date : 2024-06-01 Epub Date: 2023-03-07 DOI: 10.1007/s11571-023-09947-x
Jianling Tan, Yichao Zhan, Yi Tang, Weixin Bao, Yin Tian

Visual joint attention, the ability to track gaze and recognize intent, plays a key role in the development of social and language skills in health humans, which is performed abnormally hard in autism spectrum disorder (ASD). The traditional convolutional neural network, EEGnet, is an effective model for decoding technology, but few studies have utilized this model to address attentional training in ASD patients. In this study, EEGNet was used to decode the P300 signal elicited by training and the saliency map method was used to visualize the cognitive properties of ASD patients during visual attention. The results showed that in the spatial distribution, the parietal lobe was the main region of classification contribution, especially for Pz electrode. In the temporal information, the time period from 300 to 500 ms produced the greatest contribution to the electroencephalogram (EEG) classification, especially around 300 ms. After training for ASD patients, the gradient contribution was significantly enhanced at 300 ms, which was effective only in social scenarios. Meanwhile, with the increase of joint attention training, the P300 latency of ASD patients gradually shifted forward in social scenarios, but this phenomenon was not obvious in non-social scenarios. Our results indicated that joint attention training could improve the cognitive ability and responsiveness of social characteristics in ASD patients.

视觉联合注意,即跟踪注视和识别意图的能力,在健康人的社交和语言技能发展中起着关键作用,而自闭症谱系障碍(ASD)患者的这种能力表现异常困难。传统的卷积神经网络 EEGnet 是一种有效的解码技术模型,但很少有研究利用这种模型来解决 ASD 患者的注意力训练问题。本研究利用 EEGNet 对训练引起的 P300 信号进行解码,并采用显著性图谱法对 ASD 患者在视觉注意过程中的认知特性进行可视化分析。结果显示,在空间分布上,顶叶是分类贡献的主要区域,尤其是Pz电极。在时间信息方面,300 至 500 毫秒的时间段对脑电图(EEG)分类的贡献最大,尤其是在 300 毫秒左右。对 ASD 患者进行训练后,梯度贡献在 300 毫秒处明显增强,仅在社交场景中有效。同时,随着联合注意训练的增加,ASD患者的P300潜伏期在社交场景中逐渐前移,但这一现象在非社交场景中并不明显。我们的研究结果表明,联合注意训练可以提高ASD患者的认知能力和对社会特征的反应能力。
{"title":"EEG decoding for effects of visual joint attention training on ASD patients with interpretable and lightweight convolutional neural network.","authors":"Jianling Tan, Yichao Zhan, Yi Tang, Weixin Bao, Yin Tian","doi":"10.1007/s11571-023-09947-x","DOIUrl":"10.1007/s11571-023-09947-x","url":null,"abstract":"<p><p>Visual joint attention, the ability to track gaze and recognize intent, plays a key role in the development of social and language skills in health humans, which is performed abnormally hard in autism spectrum disorder (ASD). The traditional convolutional neural network, EEGnet, is an effective model for decoding technology, but few studies have utilized this model to address attentional training in ASD patients. In this study, EEGNet was used to decode the P300 signal elicited by training and the saliency map method was used to visualize the cognitive properties of ASD patients during visual attention. The results showed that in the spatial distribution, the parietal lobe was the main region of classification contribution, especially for Pz electrode. In the temporal information, the time period from 300 to 500 ms produced the greatest contribution to the electroencephalogram (EEG) classification, especially around 300 ms. After training for ASD patients, the gradient contribution was significantly enhanced at 300 ms, which was effective only in social scenarios. Meanwhile, with the increase of joint attention training, the P300 latency of ASD patients gradually shifted forward in social scenarios, but this phenomenon was not obvious in non-social scenarios. Our results indicated that joint attention training could improve the cognitive ability and responsiveness of social characteristics in ASD patients.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43572885","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
Temporal-spatial deciphering mental subtraction in the human brain. 人脑的时空解读心理减法
IF 3.7 3区 工程技术 Q2 Neuroscience Pub Date : 2024-06-01 Epub Date: 2023-02-27 DOI: 10.1007/s11571-023-09937-z
Na Clara Pan, Chengtian Zhao, Jialin Du, Qilin Zhou, Cuiping Xu, Chunyan Liu, Tao Yu, Dan Zhang, Yuping Wang

Mental subtraction, involving numerical processing and operation, requires a complex interplay among several brain regions. Diverse studies have utilized scalp electroencephalograph, electrocorticogram, or functional magnetic resonance imaging to resolve the structure pattern and functional activity during subtraction operation. However, a high resolution of the spatial-temporal understanding of the neural mechanisms involved in mental subtraction is unavailable. Thus, this study obtained intracranial stereoelectroencephalography recordings from 20 patients with pharmacologically resistant epilepsy. Specifically, two sample-delayed mismatch paradigms of numeric comparison and subtracting results comparison were used to help reveal the time frame of mental subtraction. The brain sub-regions were chronologically screened using the stereoelectroencephalography recording for mental subtraction. The results indicated that the anterior cortex, containing the frontal, insular, and parahippocampous, worked for preparing for mental subtraction; moreover, the posterior cortex, such as parietal, occipital, limbic, and temporal regions, cooperated during subtraction. Especially, the gamma band activities in core regions within the parietal-cingulate-temporal cortices mediated the critical mental subtraction. Overall, this research is the first to describe the spatiotemporal activities underlying mental subtraction in the human brain. It provides a comprehensive insight into the cognitive control activity underlying mental arithmetic.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-023-09937-z.

心理减法涉及数字处理和运算,需要多个脑区之间复杂的相互作用。许多研究利用头皮脑电图、皮层电图或功能磁共振成像来解析减法操作过程中的结构模式和功能活动。然而,对心理减法所涉及的神经机制的时空理解却缺乏高分辨率。因此,本研究获得了 20 名药物抵抗性癫痫患者的颅内立体脑电图记录。具体而言,本研究采用了数字比较和减法结果比较两种样本延迟错配范式,以帮助揭示心理减法的时间框架。研究人员利用立体脑电图记录按时间顺序对大脑亚区进行了心理减法筛选。结果表明,大脑前部皮层,包括额叶、岛叶和海马旁,为心理减法做准备;此外,大脑后部皮层,如顶叶、枕叶、边缘和颞叶区,在减法过程中相互配合。尤其是顶叶-扣带回-颞叶皮层核心区域的伽马带活动介导了临界心理减法。总之,这项研究首次描述了人脑中心理减法的时空活动。该研究全面揭示了心算背后的认知控制活动:在线版本包含补充材料,可查阅 10.1007/s11571-023-09937-z。
{"title":"Temporal-spatial deciphering mental subtraction in the human brain.","authors":"Na Clara Pan, Chengtian Zhao, Jialin Du, Qilin Zhou, Cuiping Xu, Chunyan Liu, Tao Yu, Dan Zhang, Yuping Wang","doi":"10.1007/s11571-023-09937-z","DOIUrl":"10.1007/s11571-023-09937-z","url":null,"abstract":"<p><p>Mental subtraction, involving numerical processing and operation, requires a complex interplay among several brain regions. Diverse studies have utilized scalp electroencephalograph, electrocorticogram, or functional magnetic resonance imaging to resolve the structure pattern and functional activity during subtraction operation. However, a high resolution of the spatial-temporal understanding of the neural mechanisms involved in mental subtraction is unavailable. Thus, this study obtained intracranial stereoelectroencephalography recordings from 20 patients with pharmacologically resistant epilepsy. Specifically, two sample-delayed mismatch paradigms of numeric comparison and subtracting results comparison were used to help reveal the time frame of mental subtraction. The brain sub-regions were chronologically screened using the stereoelectroencephalography recording for mental subtraction. The results indicated that the anterior cortex, containing the frontal, insular, and parahippocampous, worked for preparing for mental subtraction; moreover, the posterior cortex, such as parietal, occipital, limbic, and temporal regions, cooperated during subtraction. Especially, the gamma band activities in core regions within the parietal-cingulate-temporal cortices mediated the critical mental subtraction. Overall, this research is the first to describe the spatiotemporal activities underlying mental subtraction in the human brain. It provides a comprehensive insight into the cognitive control activity underlying mental arithmetic.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-023-09937-z.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11143099/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45598691","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
期刊
Cognitive Neurodynamics
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1