Network Neuroscience最新文献

Interdependence patterns of multifrequency oscillations predict visuomotor behavior. 多频振荡的相互依赖模式预测视觉运动行为。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-05-08 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00440

Jyotika Bahuguna, Antoine Schwey, Demian Battaglia, Nicole Malfait

{"title":"Interdependence patterns of multifrequency oscillations predict visuomotor behavior.","authors":"Jyotika Bahuguna, Antoine Schwey, Demian Battaglia, Nicole Malfait","doi":"10.1162/netn_a_00440","DOIUrl":"10.1162/netn_a_00440","url":null,"abstract":"We show that sensorimotor behavior can be reliably predicted from single-trial EEG oscillations fluctuating in a coordinated manner across brain regions, frequency bands, and movement time epochs. We define high-dimensional oscillatory portraits to capture the interdependence between basic oscillatory elements, quantifying oscillations occurring in single trials at specific frequencies, locations, and time epochs. We find that the general structure of the element interdependence networks (effective connectivity) remains stable across task conditions, reflecting an intrinsic coordination architecture and responds to changes in task constraints by subtle but consistently distinct topological reorganizations. Trial categories are reliably and significantly better separated using oscillatory portraits than from the information contained in individual oscillatory elements, suggesting an interelement coordination-based encoding. Furthermore, single-trial oscillatory portrait fluctuations are predictive of fine trial-to-trial variations in movement kinematics. Remarkably, movement accuracy appears to be reflected in the capacity of the oscillatory coordination architecture to flexibly update as an effect of movement-error integration.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"712-742"},"PeriodicalIF":3.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Modelling low-dimensional interacting brain networks reveals organising principle in human cognition. 对低维相互作用的大脑网络进行建模，揭示了人类认知的组织原理。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-05-08 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00434

Yonatan Sanz Perl, Sebastian Geli, Eider Pérez-Ordoyo, Lou Zonca, Sebastian Idesis, Jakub Vohryzek, Viktor K Jirsa, Morten L Kringelbach, Enzo Tagliazucchi, Gustavo Deco

{"title":"Modelling low-dimensional interacting brain networks reveals organising principle in human cognition.","authors":"Yonatan Sanz Perl, Sebastian Geli, Eider Pérez-Ordoyo, Lou Zonca, Sebastian Idesis, Jakub Vohryzek, Viktor K Jirsa, Morten L Kringelbach, Enzo Tagliazucchi, Gustavo Deco","doi":"10.1162/netn_a_00434","DOIUrl":"10.1162/netn_a_00434","url":null,"abstract":"The discovery of resting-state networks shifted the focus from the role of local regions in cognitive tasks to the ongoing spontaneous dynamics in global networks. Recently, efforts have been invested to reduce the complexity of brain activity recordings through the application of nonlinear dimensionality reduction algorithms. Here, we investigate how the interaction between these networks emerges as an organising principle in human cognition. We combine deep variational autoencoders with computational modelling to construct a dynamical model of brain networks fitted to the whole-brain dynamics measured with functional magnetic resonance imaging (fMRI). Crucially, this allows us to infer the interaction between these networks in resting state and seven different cognitive tasks by determining the effective functional connectivity between networks. We found a high flexible reconfiguration of task-driven network interaction patterns and we demonstrate that this reconfiguration can be used to classify different cognitive tasks. Importantly, compared with using all the nodes in a parcellation, we obtain better results by modelling the dynamics of interacting networks in both model and classification performance. These findings show the key causal role of manifolds as a fundamental organising principle of brain function, providing evidence that interacting networks are the computational engines' brain during cognitive tasks.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"661-681"},"PeriodicalIF":3.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Whole-brain modular dynamics at rest predict sensorimotor learning performance. 休息时全脑模块化动态预测感觉运动学习表现。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-05-08 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00420

Dominic I Standage, Daniel J Gale, Joseph Y Nashed, J Randall Flanagan, Jason P Gallivan

{"title":"Whole-brain modular dynamics at rest predict sensorimotor learning performance.","authors":"Dominic I Standage, Daniel J Gale, Joseph Y Nashed, J Randall Flanagan, Jason P Gallivan","doi":"10.1162/netn_a_00420","DOIUrl":"10.1162/netn_a_00420","url":null,"abstract":"Neural measures that predict cognitive performance are informative about the mechanisms underlying cognitive phenomena, with diagnostic potential for neuropathologies with cognitive symptoms. Among such markers, the modularity (subnetwork composition) of whole-brain functional networks is especially promising due to its longstanding theoretical foundations and recent success in predicting clinical outcomes. We used functional magnetic resonance imaging to identify whole-brain modules at rest, calculating metrics of their spatiotemporal dynamics before and after a sensorimotor learning task on which fast learning is widely believed to be supported by a cognitive strategy. We found that participants' learning performance was predicted by the degree of coordination of modular reconfiguration and the strength of recruitment and integration of networks derived during the task itself. Our findings identify these whole-brain metrics as promising network-based markers of cognition, with relevance to basic neuroscience and the potential for clinical application.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"631-660"},"PeriodicalIF":3.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Metric structural human connectomes: Localization and multifractality of eigenmodes. 度量结构人类连接体：特征模态的局部化和多重分形。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-05-08 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00439

Anna Bobyleva, Alexander Gorsky, Sergei Nechaev, Olga Valba, Nikita Pospelov

{"title":"Metric structural human connectomes: Localization and multifractality of eigenmodes.","authors":"Anna Bobyleva, Alexander Gorsky, Sergei Nechaev, Olga Valba, Nikita Pospelov","doi":"10.1162/netn_a_00439","DOIUrl":"10.1162/netn_a_00439","url":null,"abstract":"We explore the fundamental principles underlying the architecture of the human brain's structural connectome through the lens of spectral analysis of Laplacian and adjacency matrices. Building on the idea that the brain balances efficient information processing with minimizing wiring costs, our goal is to understand how the metric properties of the connectome relate to the presence of an inherent scale. We demonstrate that a simple generative model combining nonlinear preferential attachment with an exponential penalty for spatial distance between nodes can effectively reproduce several key features of the human connectome. These include spectral density, edge length distribution, eigenmode localization, local clustering, and topological properties. Additionally, we examine the finer spectral characteristics of human structural connectomes by evaluating the inverse participation ratios (IPR q ) across various parts of the spectrum. Our analysis shows that the level statistics in the soft cluster region of the Laplacian spectrum (where eigenvalues are small) deviate from a purely Poisson distribution due to interactions between clusters. Furthermore, we identify localized modes with large IPR values in the continuous spectrum. Multiple fractal eigenmodes are found across different parts of the spectrum, and we evaluate their fractal dimensions. We also find a power-law behavior in the return probability-a hallmark of critical behavior-and conclude by discussing how our findings are related to previous conjectures that the brain operates in an extended critical phase that supports multifractality.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"682-711"},"PeriodicalIF":3.6,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140581/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multilayer network analysis across cortical depths in 7-T resting-state fMRI. 7-T静息状态fMRI皮层深度多层网络分析。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-04-30 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00436

Parker Kotlarz, Kaisu Lankinen, Maria Hakonen, Tori Turpin, Jonathan R Polimeni, Jyrki Ahveninen

{"title":"Multilayer network analysis across cortical depths in 7-T resting-state fMRI.","authors":"Parker Kotlarz, Kaisu Lankinen, Maria Hakonen, Tori Turpin, Jonathan R Polimeni, Jyrki Ahveninen","doi":"10.1162/netn_a_00436","DOIUrl":"10.1162/netn_a_00436","url":null,"abstract":"In graph theory, \"multilayer networks\" represent systems involving several interconnected topological levels. One example in neuroscience is the stratification of connections between different cortical depths or \"laminae,\" which is becoming noninvasively accessible in humans using ultrahigh-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7-T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We compared networks where the interregional connections were limited to a single cortical depth only (\"layer-by-layer matrices\") with those considering all possible connections between areas and cortical depths (\"multilayer matrix\"). We utilized global and local graph theory features that quantitatively characterize network attributes including network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared with the layer-by-layer versions. Superficial depths of the cortex dominated information transfer, and deeper depths drove clustering. These differences were largest in frontotemporal and limbic regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information; thus, multilayer connectomics could provide a methodological framework for studies on how information flows across this stratification.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"475-503"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12151305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144267716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiplex connectomics reveal altered networks in frontotemporal dementia: A multisite study. 多重连接组学揭示额颞叶痴呆的网络改变：一项多位点研究。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-04-30 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00448

Sunil Kumar Khokhar, Manoj Kumar, Faheem Arshad, Sheetal Goyal, Megha Tiwari, Nithin Thanissery, Subasree Ramakrishnan, Chandana Nagaraj, Rajan Kashyap, Sandhya Mangalore, Tapan K Gandhi, Suvarna Alladi, Rose Dawn Bharath

{"title":"Multiplex connectomics reveal altered networks in frontotemporal dementia: A multisite study.","authors":"Sunil Kumar Khokhar, Manoj Kumar, Faheem Arshad, Sheetal Goyal, Megha Tiwari, Nithin Thanissery, Subasree Ramakrishnan, Chandana Nagaraj, Rajan Kashyap, Sandhya Mangalore, Tapan K Gandhi, Suvarna Alladi, Rose Dawn Bharath","doi":"10.1162/netn_a_00448","DOIUrl":"10.1162/netn_a_00448","url":null,"abstract":"A network neuroscience perspective can significantly advance the understanding of neurodegenerative disorders, particularly frontotemporal dementia (FTD). This study employed an innovative multiplex connectomics approach, integrating cortical thickness (CTH) and fluorodeoxyglucose-positron emission tomography (FDG-PET) in a dual-layer model to investigate network alterations in FTD subtypes across two geographically distinct sites. The cohort included groups of behavioral variant FTD (bvFTD), primary progressive aphasia (PPA), mild cognitive impairment (MCI), and cognitively normal (CN) individuals who were analyzed from two separate sites. Site 1 included 28 bvFTD, 20 PPA, and 27 MCI participants, whereas Site 2 included 26 bvFTD, 43 PPA, and 43 CN individuals, respectively. Utilizing CTH and FDG-PET data after standard preprocessing, a multiplex network pipeline in BRAPH2 toolbox was used to derive multiplex participation coefficient (MPC) between the groups. The analysis revealed an increase in global MPC as an indicator of disease in PPA at both sites. Additionally, nodal MPC alterations in the anterior cingulate, frontal, and temporal lobes in PPA were compared with bvFTD. Comparisons with the CN showed that nodal MPC alterations were more extensive in PPA when compared with bvFTD. These findings underscore the potential utility of multiplex connectomes for identifying network disruptions in neurodegenerative disorders, offering promising implications for future research and clinical applications.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"615-630"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140567/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficacy of functional connectome fingerprinting using tangent-space brain networks. 切线空间脑网络功能连接体指纹识别的有效性。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-04-30 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00445

Davor Curic, Sudhanva Kalasapura Venugopal Krishna, Jörn Davidsen

{"title":"Efficacy of functional connectome fingerprinting using tangent-space brain networks.","authors":"Davor Curic, Sudhanva Kalasapura Venugopal Krishna, Jörn Davidsen","doi":"10.1162/netn_a_00445","DOIUrl":"10.1162/netn_a_00445","url":null,"abstract":"Functional connectomes (FCs) are estimations of brain region interaction derived from brain activity, often obtained from functional magnetic resonance imaging recordings. Quantifying the distance between FCs is important for understanding the relation between behavior, disorders, disease, and changes in connectivity. Recently, tangent space projections, which account for the curvature of the mathematical space of FCs, have been proposed for calculating FC distances. We compare the efficacy of this approach relative to the traditional method in the context of subject identification using the Midnight Scan Club dataset in order to study resting-state and task-based subject discriminability. The tangent space method is found to universally outperform the traditional method. We also focus on the subject identification efficacy of subnetworks. Certain subnetworks are found to outperform others, a dichotomy that largely follows the \"control\" and \"processing\" categorization of resting-state networks, and relates subnetwork flexibility with subject discriminability. Identification efficacy is also modulated by tasks, though certain subnetworks appear task independent. The uniquely long recordings of the dataset also allow for explorations of resource requirements for effective subject identification. The tangent space method is found to universally require less data, making it well suited when only short recordings are available.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"549-568"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140576/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250362","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantifying the influence of biophysical factors in shaping brain communication through remnant functional networks. 量化生物物理因素对通过残余功能网络形成大脑通讯的影响。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-04-30 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00444

Johan Nakuci, Javier Garcia, Kanika Bansal

{"title":"Quantifying the influence of biophysical factors in shaping brain communication through remnant functional networks.","authors":"Johan Nakuci, Javier Garcia, Kanika Bansal","doi":"10.1162/netn_a_00444","DOIUrl":"10.1162/netn_a_00444","url":null,"abstract":"Functional connectivity (FC) reflects brain-wide communication essential for cognition, yet the role of underlying biophysical factors in shaping FC remains unclear. We quantify the influence of physical factors-structural connectivity (SC) and Euclidean distance (DC), which capture anatomical wiring and regional distance-and molecular factors-gene expression similarity (GC), and neuroreceptor congruence (RC), representing neurobiological similarity-on resting-state FC. We assess how these factors impact graph-theoretic and gradient features, capturing pairwise and higher-order interactions. By generating remnant functional networks after selectively removing connections tied to specific factors, we show that molecular factors, particularly RC, dominate graph-theoretic features, while gradient features are shaped by a mix of molecular and physical factors, especially GC and DC. SC has a surprisingly minor role. We also link FC alterations to biophysical factors in schizophrenia, bipolar disorder, and attention deficit/hyperactivity disorder (ADHD), with physical factors differentiating these groups. These insights are key for understanding FC across various applications, including task performance, development, and clinical conditions.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"522-548"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting response speed and age from task-evoked effective connectivity. 从任务诱发的有效连通性预测反应速度和年龄。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-04-30 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00447

Shufei Zhang, Kyesam Jung, Robert Langner, Esther Florin, Simon B Eickhoff, Oleksandr V Popovych

{"title":"Predicting response speed and age from task-evoked effective connectivity.","authors":"Shufei Zhang, Kyesam Jung, Robert Langner, Esther Florin, Simon B Eickhoff, Oleksandr V Popovych","doi":"10.1162/netn_a_00447","DOIUrl":"10.1162/netn_a_00447","url":null,"abstract":"Recent neuroimaging studies demonstrated that task-evoked functional connectivity (FC) may better predict individual traits than resting-state FC. However, the prediction properties of task-evoked effective connectivity (EC) remain unexplored. We investigated this by predicting individual reaction time (RT) performance in the stimulus-response compatibility task and age, using intrinsic EC (I-EC; calculated at baseline) and task-modulated EC (M-EC; induced by experimental conditions) with dynamic causal modeling (DCM) across various data processing conditions, including different general linear model (GLM) designs, Bayesian model reduction, and different cross-validation schemes and prediction models. We report evident differences in predicting RT and age between I-EC and M-EC, as well as between event-related and block-based GLM and DCM designs. M-EC outperformed both I-EC and task-evoked FC in RT prediction, while all types of connectivity performed similarly for age. Event-related GLM and DCM designs performed better than block-based designs. Our findings suggest that task-evoked I-EC and M-EC may capture different phenotypic attributes, with performance influenced by data processing and modeling choices, particularly the GLM-DCM design. This evaluation of methods for behavior prediction from brain EC may contribute to a meta-scientific understanding of how data processing and modeling frameworks influence neuroimaging-based predictions, offering insights for improving their robustness and efficacy.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"591-614"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","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 impact of transcranial random noise stimulation (tRNS) on alpha coherence and verbal divergent thinking. 经颅随机噪声刺激对脑连贯性和言语发散性思维的影响。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2025-04-30 eCollection Date: 2025-01-01 DOI: 10.1162/netn_a_00446

Magdalena Camenzind, Rahel A Steuri, Branislav Savic, Fred W Mast, René M Müri, Aleksandra K Eberhard-Moscicka

{"title":"The impact of transcranial random noise stimulation (tRNS) on alpha coherence and verbal divergent thinking.","authors":"Magdalena Camenzind, Rahel A Steuri, Branislav Savic, Fred W Mast, René M Müri, Aleksandra K Eberhard-Moscicka","doi":"10.1162/netn_a_00446","DOIUrl":"10.1162/netn_a_00446","url":null,"abstract":"Random noise stimulation (tRNS) applied to the dorsolateral prefrontal cortex (DLPFC) enhances fluency and originality in verbal divergent thinking tasks. However, the underlying neural mechanisms of this behavioral change remain unclear. Given that the DLPFC is a key node of the executive control network (ECN) and that creativity is a two-stage process in which the ECN is primarily involved in the final idea selection stage, application of tRNS to this region shall not only result in an increase of originality and flexibility but also in a modulation of EEG activity. To test these assumptions, we collected 256-channel EEG of 40 participants before and after tRNS/sham applied to the DLPFC, during which participants performed two verbal creativity tasks. To assess stimulation-induced connectivity changes and to capture large-scale cortical communication, a source space alpha (8-12 Hz) imaginary coherence was calculated. We found that the tRNS-induced improvements in originality and flexibility were associated with bilateral DLPFC alpha coherence changes. From a large-scale networks perspective, these results suggest that tRNS-induced ECN activity is associated with increased originality and flexibility, potentially by enhancing selectivity in the idea evaluation phase. This study, for the first time, indicates a link between neurophysiological activity and tRNS-induced changes in verbal creativity.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"569-590"},"PeriodicalIF":3.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12140578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0