Cognitive NeurodynamicsPub Date : 2024-12-01Epub Date: 2024-10-03DOI: 10.1007/s11571-024-10174-1
Xiaoyu Wang, Li Lin, Lei Zhan, Xianghong Sun, Zheng Huang, Liang Zhang
{"title":"Resting state EEG delta-beta amplitude-amplitude coupling: a neural predictor of cortisol response under stress.","authors":"Xiaoyu Wang, Li Lin, Lei Zhan, Xianghong Sun, Zheng Huang, Liang Zhang","doi":"10.1007/s11571-024-10174-1","DOIUrl":"10.1007/s11571-024-10174-1","url":null,"abstract":"<p><p>Stress is ubiquitous in daily life. Subcortical and cortical regions closely interact to respond to stress. Delta-beta cross-frequency coupling (CFC), believed to signify communication between different brain areas, can serve as a neural signature underlying the heterogeneity in stress responses. Nevertheless, the role of cross-frequency coupling in stress prediction has not received sufficient attention. To examine the predictive role of resting state delta-beta CFC across the whole scalp, we obtained amplitude-amplitude coupling (AAC) and phase-amplitude coupling (PAC) from 4-minute resting state EEG of seventy-three healthy participants. The Trier Social Stress Test (TSST) was administered on a separate day to induce stress. Salivary cortisol and heart rate were recorded to measure stress responses. Utilizing cluster-based permutation analysis, the results showed that delta-beta AAC was positively correlated with cortisol increase magnitude (cluster <i>t</i> = 26.012, <i>p</i> = .020) and cortisol AUCi (cluster <i>t</i> = 23.039, <i>p</i> = .022) over parietal-occipital areas, which means that individuals with a stronger within-subject AAC demonstrated a greater cortisol response. These results suggest that AAC could be a valuable biomarker for predicting neuroendocrine activity under stress. However, no association between PAC and stress responses was found. Additionally, we did not detect the predictive effect of power in the delta or beta frequency bands on stress responses, suggesting that delta-beta AAC provides unique insights beyond single-band power. These findings enhance our understanding of the neurophysiological mechanism underpinning individual differences in stress responses and offer promising biomarkers for stress assessment and the detection of stress-related disorders.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10174-1.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"3995-4007"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876522","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 : 2024-12-01Epub Date: 2024-09-24DOI: 10.1007/s11571-024-10144-7
Zhe Zhang, Yanxiao Chen, Xu Zhao, Wang Fan, Ding Peng, Tianwen Li, Lei Zhao, Yunfa Fu
{"title":"A review of ethical considerations for the medical applications of brain-computer interfaces.","authors":"Zhe Zhang, Yanxiao Chen, Xu Zhao, Wang Fan, Ding Peng, Tianwen Li, Lei Zhao, Yunfa Fu","doi":"10.1007/s11571-024-10144-7","DOIUrl":"10.1007/s11571-024-10144-7","url":null,"abstract":"<p><p>The development and potential applications of brain-computer interfaces (BCIs) are directly related to the human brain and may have adverse effects on the users' physical and mental health. Ethical issues, particularly those associated with BCIs, including both non-medical and medical applications, have captured societal attention. This article initially reviews the application of three ethical frameworks in BCI technology: consequentialism, deontology, and virtue ethics. Subsequently, it introduces the ethical standards under consideration within the medical objective framework for BCI medical applications. Finally, the paper discusses and forecasts the ethical standards for BCI medical applications. The paper emphasizes the necessity to differentiate between the ethical issues of implantable and non-implantable BCIs, to approach the research on BCI-based \"controlling the brain\" with caution, and to establish standardized operational procedures and efficacy evaluation methods for BCI medical applications. This paper aims to provide ideas for the establishment of ethical standards in BCI medical applications.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"3603-3614"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655950/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876422","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 : 2024-12-01Epub Date: 2024-10-21DOI: 10.1007/s11571-024-10181-2
Yilin Li, Werner Sommer, Liang Tian, Changsong Zhou
{"title":"Assessing the influence of latency variability on EEG classifiers - a case study of face repetition priming.","authors":"Yilin Li, Werner Sommer, Liang Tian, Changsong Zhou","doi":"10.1007/s11571-024-10181-2","DOIUrl":"10.1007/s11571-024-10181-2","url":null,"abstract":"<p><p>Data-driven strategies have been widely used to distinguish experimental effects on single-trial EEG signals. However, how latency variability, such as within-condition jitter or latency shifts between conditions, affects the performance of EEG classifiers has not been well investigated. Without explicitly considering and disentangling such attributes of single trials, neural network-based classifiers have limitations in measuring their contributions. Inspired by domain knowledge of subcomponent latency and amplitude from traditional cognitive neuroscience, this study applies a stepwise latency correction method on single trials to control for their contributions to classifier behavior. As a case study demonstrating the value of this method, we measure repetition priming effects of faces, which induce large reaction time differences, latency shifts, and amplitude effects in averaged event-related potentials. The results show that within-condition jitter negatively impacts classifier performance, but between-condition latency shifts improve accuracy, whereas genuine amplitude differences have no significant influence. While demonstrated in the case of priming effects, this methodology can be generalized to experiments involving many kinds of time-varying signals to account for the contributions of latency variability to classifier performance.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1007/s11571-024-10181-2.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4055-4069"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876428","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 : 2024-12-01Epub Date: 2024-10-21DOI: 10.1007/s11571-024-10177-y
Debashis Das Chakladar
{"title":"Cortex level connectivity between ACT-R modules during EEG-based n-back task.","authors":"Debashis Das Chakladar","doi":"10.1007/s11571-024-10177-y","DOIUrl":"10.1007/s11571-024-10177-y","url":null,"abstract":"<p><p>Finding the synchronization between Electroencephalography (EEG) and human cognition is an essential aspect of cognitive neuroscience. Adaptive Control of Thought-Rational (ACT-R) is a widely used cognitive architecture that defines the cognitive and perceptual operations of the human mind. This study combines the ACT-R and EEG-based cortex-level connectivity to highlight the relationship between ACT-R modules during the EEG-based <i>n</i>-back task (for validating working memory performance). Initially, the source localization method is performed on the EEG signal, and the mapping between ACT-R modules and corresponding brain scouts (on the cortex surface) is performed. Once the brain scouts are identified for ACT-R modules, then those scouts are called ACT-R scouts. The linear (Granger Causality: GC) and non-linear effective connectivity (Multivariate Transfer Entropy: MTE) methods are applied over the scouts' time series data. From the GC and MTE analysis, for all <i>n</i>-back tasks, information flow is observed from the visual-to-imaginal ACT-R scout for storing the visual stimuli (i.e., input letter) in short-term memory. For 2 and 3-back tasks, causal flow exists from imaginal to retrieval ACT-R scout and vice-versa. Causal flow from procedural to the imaginal ACT-R scout is also observed for all workload levels to execute the set of productions. Identifying the relationship among ACT-R modules through scout-level connectivity in the cortical surface facilitates the effects of human cognition in terms of brain dynamics.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4033-4045"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655808/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876455","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 : 2024-12-01Epub Date: 2024-10-04DOI: 10.1007/s11571-024-10179-w
Sridevi Srinivasan, Shiny Duela Johnson
{"title":"Correction to: Optimizing feature subset for schizophrenia detection using multichannel EEG signals and rough set theory.","authors":"Sridevi Srinivasan, Shiny Duela Johnson","doi":"10.1007/s11571-024-10179-w","DOIUrl":"10.1007/s11571-024-10179-w","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1007/s11571-023-10011-x.].</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4103"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876439","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 : 2024-12-01Epub Date: 2024-11-12DOI: 10.1007/s11571-024-10178-x
Vinoth Seralan, D Chandrasekhar, Sarasu Pakiriswamy, Karthikeyan Rajagopal
{"title":"Collective behavior of an adapting synapse-based neuronal network with memristive effect and randomness.","authors":"Vinoth Seralan, D Chandrasekhar, Sarasu Pakiriswamy, Karthikeyan Rajagopal","doi":"10.1007/s11571-024-10178-x","DOIUrl":"10.1007/s11571-024-10178-x","url":null,"abstract":"<p><p>This study delves into the examination of a network of adaptive synapse neurons characterized by a small-world network topology connected through electromagnetic flux and infused with randomness. First, this research extensively explores the existence of the global multi-stability of a single adaptive synapse-based neuron model with magnetic flux. The non-autonomous neuron model exhibits periodically switchable equilibrium states that are strongly related to the transitions between stable and unstable points in every whole periodic cycle, leading to the creation of global multi-stability. Various numerical measures, including bifurcation plots, phase plots, and basin of attraction, illustrate the intricate dynamics of diverse coexisting global firing activities. Moreover, the model is extended by coupling two neurons with a memristive synapse. The dynamics of the coupled neurons model are showcased with the help of largest Lyapunov exponents, and synchronized dynamics are viewed with the help of mean average error. Next, we consider a regular network of neurons connected to their nearest neighbors through the memristive synapse. We then reconstruct it into a small-world network by increasing the randomness in the rewiring links. Consequently, we observed collective behavior influenced by the number of neighborhood connections, coupling strength, and rewiring probability. We used spatio-temporal patterns, recurrence plots, as well as global-order parameters to verify the reported results.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4071-4087"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876431","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 : 2024-12-01Epub Date: 2023-07-11DOI: 10.1007/s11571-023-09987-3
Mikhail Rabinovich, Christian Bick, Pablo Varona
{"title":"Beyond neurons and spikes: <i>cognon</i>, the hierarchical dynamical unit of thought.","authors":"Mikhail Rabinovich, Christian Bick, Pablo Varona","doi":"10.1007/s11571-023-09987-3","DOIUrl":"10.1007/s11571-023-09987-3","url":null,"abstract":"<p><p>From the dynamical point of view, most cognitive phenomena are hierarchical, transient and sequential. Such cognitive spatio-temporal processes can be represented by a set of sequential metastable dynamical states together with their associated transitions: The state is quasi-stationary close to one metastable state before a rapid transition to another state. Hence, we postulate that metastable states are the central players in cognitive information processing. Based on the analogy of quasiparticles as elementary units in physics, we introduce here the quantum of cognitive information dynamics, which we term \"cognon\". A cognon, or dynamical unit of thought, is represented by a robust finite chain of metastable neural states. Cognons can be organized at multiple hierarchical levels and coordinate complex cognitive information representations. Since a cognon is an abstract conceptualization, we link this abstraction to brain sequential dynamics that can be measured using common modalities and argue that cognons and brain rhythms form binding spatiotemporal complexes to keep simultaneous dynamical information which relate the 'what', 'where' and 'when'.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3327-3335"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44622024","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 : 2024-12-01Epub Date: 2023-06-20DOI: 10.1007/s11571-023-09983-7
Christoph Aurnhammer, Matthew W Crocker, Harm Brouwer
{"title":"Single-trial neurodynamics reveal N400 and P600 coupling in language comprehension.","authors":"Christoph Aurnhammer, Matthew W Crocker, Harm Brouwer","doi":"10.1007/s11571-023-09983-7","DOIUrl":"10.1007/s11571-023-09983-7","url":null,"abstract":"<p><p>Theories of the electrophysiology of language comprehension are mostly informed by event-related potential effects observed between condition averages. We here argue that a dissociation between competing effect-level explanations of event-related potentials can be achieved by turning to predictions and analyses at the single-trial level. Specifically, we examine the single-trial dynamics in event-related potential data that exhibited a biphasic N400-P600 effect pattern. A group of multi-stream models can explain biphasic effects by positing that each individual trial should induce either an N400 increase or a P600 increase, but not both. An alternative, single-stream account, Retrieval-Integration theory, explicitly predicts that N400 amplitude and P600 amplitude should be correlated at the single-trial level. In order to investigate the single-trial dynamics of the N400 and the P600, we apply a regression-based technique in which we quantify the extent to which N400 amplitudes are predictive of the electroencephalogram in the P600 time window. Our findings suggest that, indeed, N400 amplitudes and P600 amplitudes are inversely correlated within-trial and, hence, the N400 effect and the P600 effect in biphasic data are driven by the same trials. Critically, we demonstrate that this finding also extends to data which exhibited only monophasic effects between conditions. In sum, the observation that the N400 is inversely correlated with the P600 on a by-trial basis supports a single stream view, such as Retrieval-Integration theory, and is difficult to reconcile with the processing mechanisms proposed by multi-stream models.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3309-3325"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49617519","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 : 2024-12-01Epub Date: 2023-02-02DOI: 10.1007/s11571-023-09932-4
Haitao Yu, Quanfa Zhao
{"title":"Brain-inspired multisensory integration neural network for cross-modal recognition through spatiotemporal dynamics and deep learning.","authors":"Haitao Yu, Quanfa Zhao","doi":"10.1007/s11571-023-09932-4","DOIUrl":"10.1007/s11571-023-09932-4","url":null,"abstract":"<p><p>The integration and interaction of cross-modal senses in brain neural networks can facilitate high-level cognitive functionalities. In this work, we proposed a bioinspired multisensory integration neural network (MINN) that integrates visual and audio senses for recognizing multimodal information across different sensory modalities. This deep learning-based model incorporates a cascading framework of parallel convolutional neural networks (CNNs) for extracting intrinsic features from visual and audio inputs, and a recurrent neural network (RNN) for multimodal information integration and interaction. The network was trained using synthetic training data generated for digital recognition tasks. It was revealed that the spatial and temporal features extracted from visual and audio inputs by CNNs were encoded in subspaces orthogonal with each other. In integration epoch, network state evolved along quasi-rotation-symmetric trajectories and a structural manifold with stable attractors was formed in RNN, supporting accurate cross-modal recognition. We further evaluated the robustness of the MINN algorithm with noisy inputs and asynchronous digital inputs. Experimental results demonstrated the superior performance of MINN for flexible integration and accurate recognition of multisensory information with distinct sense properties. The present results provide insights into the computational principles governing multisensory integration and a comprehensive neural network model for brain-inspired intelligence.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":" ","pages":"3615-3628"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49113589","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 : 2024-12-01Epub Date: 2024-11-14DOI: 10.1007/s11571-024-10165-2
Dianavinnarasi Joseph, Suresh Kumarasamy, Sayooj Aby Jose, Karthikeyan Rajagopal
{"title":"Stability of synchronization manifolds and its nonlinear behaviour in memristive coupled discrete neuron model.","authors":"Dianavinnarasi Joseph, Suresh Kumarasamy, Sayooj Aby Jose, Karthikeyan Rajagopal","doi":"10.1007/s11571-024-10165-2","DOIUrl":"10.1007/s11571-024-10165-2","url":null,"abstract":"<p><p>In this study, we investigate the impact of first and second-order coupling strengths on the stability of a synchronization manifold in a Discrete FitzHugh-Nagumo (DFHN) neuron model with memristor coupling. Master Stability Function (MSF) is used to estimate the stability of the synchronized manifold. The MSF of the DFHN model exhibits two zero crossings as we vary the coupling strengths, which is categorized as class <math><msub><mi>Γ</mi> <mn>2</mn></msub> </math> . Interestingly, both zero-crossing points demonstrate a power-law relationship with respect to both the first-order coupling strength and flux coefficient, as well as the second-order coupling strength and flux coefficient. In contrast, the zero crossings follow a linear relationship between first-order and second-order coupling strength. These linear and nonlinear relationships enable us to forecast the zero-crossing point and, consequently, determine the coupling strengths at which the stability of the synchronization manifold changes for any given set of parameters. We further explore the regime of the stable synchronization manifold within a defined parameter space. Lower values of both first and second-order coupling strengths have minimal impact on the transition between stable and unstable synchronization regimes. Conversely, higher coupling strengths lead to a shrinking regime of the stable synchronization manifold. This reduction follows an exponential relationship with the coupling strengths. This study is helpful in brain-inspired computing systems by understanding synchronization stability in neuron models with memristor coupling. It helps to create more efficient neural networks for tasks like pattern recognition and data processing.</p>","PeriodicalId":10500,"journal":{"name":"Cognitive Neurodynamics","volume":"18 6","pages":"4089-4099"},"PeriodicalIF":3.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11655780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876523","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}