Network Neuroscience最新文献

{"title":"Directionality of neural activity in and out of the seizure onset zone in focal epilepsy.","authors":"Hamid Karimi-Rouzbahani, Aileen McGonigal","doi":"10.1162/netn_a_00454","DOIUrl":"10.1162/netn_a_00454","url":null,"abstract":"<p><p>Epilepsy affects over 50 million people worldwide, with approximately 30% experiencing drug-resistant forms that may require surgical intervention. Accurate localisation of the epileptogenic zone (EZ) is crucial for effective treatment, but how best to use intracranial EEG data to delineate the EZ remains unclear. Previous studies have used the directionality of neural activities across the brain to investigate seizure dynamics and localise the EZ. However, the different connectivity measures used across studies have often provided inconsistent insights about the direction and the localisation power of signal flow as a biomarker for EZ localisation. In a data-driven approach, this study employs a large set of 13 distinct directed connectivity measures to evaluate neural activity flow in and out the seizure onset zone (SOZ) during interictal and ictal periods. These measures test the hypotheses of \"sink SOZ\" (SOZ dominantly receiving neural activities during interictal periods) and \"source SOZ\" (SOZ dominantly transmitting activities during ictal periods). While the results were different across connectivity measures, several measures consistently supported higher connectivity directed towards the SOZ in interictal periods and higher connectivity directed away during ictal periods. Comparing six distinct metrics of node behaviour in the network, we found that SOZ separates itself from the rest of the network, allowing for the metric of \"<i>eccentricity</i>\" to localise the SOZ more accurately than any other metrics including \"<i>in strength</i>\" and \"<i>out strength</i>.\" This introduced a novel biomarker for localising the SOZ, leveraging the discriminative power of directed connectivity measures in an explainable machine learning pipeline. By using a comprehensive, objective, and data-driven approach, this study addresses previously unresolved questions on the direction of neural activities in seizure organisation and sheds light on dynamics of interictal and ictal activity in focal epilepsy.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"798-823"},"PeriodicalIF":3.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561610","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}

{"title":"Diffusion wavelets on connectome: Localizing the sources of diffusion mediating structure-function mapping using graph diffusion wavelets.","authors":"Chirag Jain, Sravanthi Upadrasta Naga Sita, Avinash Sharma, Raju Surampudi Bapi","doi":"10.1162/netn_a_00456","DOIUrl":"10.1162/netn_a_00456","url":null,"abstract":"<p><p>The intricate link between brain functional connectivity (FC) and structural connectivity (SC) is explored through models performing diffusion on SC to derive FC, using varied methodologies from single to multiple graph diffusion kernels. However, existing studies have not correlated diffusion scales with specific brain regions of interest (RoIs), limiting the applicability of graph diffusion. We propose a novel approach using graph diffusion wavelets to learn the appropriate diffusion scale for each RoI to accurately estimate the SC-FC mapping. Using the open Human Connectome Project dataset, we achieve an average Pearson's correlation value of 0.833, surpassing the state-of-the-art methods for the prediction of FC. It is important to note that the proposed architecture is entirely linear, computationally efficient, and notably demonstrates the power-law distribution of diffusion scales. Our results show that the bilateral frontal pole, by virtue of it having large diffusion scale, forms a large community structure. The finding is in line with the current literature on the role of the frontal pole in resting-state networks. Overall, the results underscore the potential of graph diffusion wavelet framework for understanding how the brain structure leads to FC.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"777-797"},"PeriodicalIF":3.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561595","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}

{"title":"Increased functional network segregation in glioma patients posttherapy: A neurological compensatory response or catastrophe for cognition?","authors":"Laurien De Roeck, Rob Colaes, Patrick Dupont, Stefan Sunaert, Steven De Vleeschouwer, Paul M Clement, Charlotte Sleurs, Maarten Lambrecht","doi":"10.1162/netn_a_00449","DOIUrl":"10.1162/netn_a_00449","url":null,"abstract":"<p><p>The brain operates through networks of interconnected regions, which can be disrupted by glial tumors and their treatment. This study investigates associations between this altered functional network topology and cognition in gliomas. We studied 50 adult glioma survivors (>1-year posttherapy) and 50 healthy controls. Participants underwent cognitive assessments across six domains and an 8-min resting-state functional MRI. Based on the BOLD signal, partial correlations were computed among 78 brain regions. From their absolute values, whole-brain and nodal graph metrics were derived and normalized to random graphs. Group differences in whole-brain and nodal graph metrics were assessed with Mann-Whitney <i>U</i> tests and mixed-design analyses of variance, respectively. Metrics exhibiting significant intergroup differences were correlated with cognitive scores, with <i>p</i> _bonf < 0.050 indicating significance. Among controls, 8 of 78 nodes were identified as hubs. Patients exhibited significantly higher whole-brain clustering, correlating with intelligence (<i>r</i>(98) = -0.409, <i>p</i> _bonf < 0.001) and executive functioning (<i>r</i>(98) = 0.300, <i>p</i> _bonf = 0.014). Lower centrality, higher nodal clustering, and assortativity were also observed in patients, particularly in hubs, correlating with language and executive functioning, respectively (all <i>r</i>(98) > 0.300, <i>p</i> _bonf < 0.050). Glioma patients commonly experience cognitive deficits alongside posttreatment alterations in functional network topology. Alterations in clustering, assortativity, and centrality may specifically act as compensatory mechanisms, significantly influencing cognitive functioning.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"743-760"},"PeriodicalIF":3.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561611","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}

{"title":"A graph neural network approach to investigate brain critical states over neurodevelopment.","authors":"Rodrigo M Cabral-Carvalho, Walter H L Pinaya, João R Sato","doi":"10.1162/netn_a_00451","DOIUrl":"10.1162/netn_a_00451","url":null,"abstract":"<p><p>Recent studies show that functional resting-state dynamics may be modeled by lattice models near criticality, such as the 2D Ising model. The Ising temperature, which is the control parameter dictating the phase transitions of the model, can provide insight into the large-scale dynamics and is being used to better understand different brain states and neurodevelopment. This period is categorized by intricate changes in the microcircuits to consolidate networks. These changes influence the macroscopic brain dynamics and also its functional relations, which can be observed in functional magnetic resonance imaging (fMRI). Therefore, this work investigates neurodevelopment through a novel method to estimate the Ising temperature of the brain from fMRI data using functional connectivity and graph neural networks trained on Ising model networks. The main finding indicates a statistically significant negative correlation between age and temperature for typically developing children (<i>r</i> = -0.48, <i>p</i> < 0.0001) and also children with attention-deficit/hyperactivity disorder (<i>r</i> = -0.49, <i>p</i> < 0.0001). This study suggests that the brain gets distant from criticality as age increases, leading to a more ordered state.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 2","pages":"761-776"},"PeriodicalIF":3.6,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561594","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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 _<i>q</i> ) 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.</p>","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><p>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.</p>","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}