Network Neuroscience最新文献

{"title":"Idiosyncrasy and generalizability of contraceptive- and hormone-related functional connectomes across the menstrual cycle.","authors":"Katherine L Bottenhorn, Taylor Salo, Emily G Jacobs, Laura Pritschet, Caitlin Taylor, Megan M Herting, Angela R Laird","doi":"10.1162/netn.a.20","DOIUrl":"10.1162/netn.a.20","url":null,"abstract":"<p><p>Neuroendocrinology has received little attention in human neuroscience research, resulting in a dearth of knowledge surrounding potent and dynamic modulators of cognition and behavior, as well as brain structure and function. This work addresses one such phenomenon by studying functional connectomics related to ovarian hormone fluctuations throughout the adult menstrual cycle. To do so, we used fMRI and hormone assessments from two dense, longitudinal datasets to assess variations in functional connectivity with respect to endogenous and exogenous endocrine factors throughout the menstrual cycle. First, we replicated prior findings that common, group-level, and individual-specific factors have similar relative contributions to functional brain network organization. Second, we found widespread connectivity related to hormonal contraceptive (HC) use, in addition to sparser estradiol- and progesterone-related connectivity. Differential generalizability of these connectivity patterns suggests progestin-specific impacts on functional brain organization in HC users. These results provide novel insight into within-individual changes in brain organization across the menstrual cycle and the extent to which these changes are shared between individuals, illuminating understudied phenomena in reproductive health and important information for all neuroimaging studies that include participants who menstruate.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"990-1012"},"PeriodicalIF":3.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12543306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356527","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":"Jointly estimating individual and group networks from fMRI data.","authors":"Don van den Bergh, Linda Douw, Zarah van der Pal, Tessa F Blanken, Anouk Schrantee, Maarten Marsman","doi":"10.1162/netn_a_00457","DOIUrl":"10.1162/netn_a_00457","url":null,"abstract":"<p><p>In fMRI research, graphical models are used to uncover complex patterns of relationships between brain regions. Connectivity-based fMRI studies typically analyze nested data; raw observations, for example, BOLD responses, are nested within participants, which are nested within populations, for example, healthy controls. Often, studies ignore the nested structure and analyze participants either individually or in aggregate. This overlooks the distinction between within-participant and between-participant variance, which can lead to poor generalizability of results because group-level effects do not necessarily reflect effects for each member of the group and, at worst, risk paradoxical results where group-level effects are opposite to individual-level effects (e.g., Kievit, Frankenhuis, Waldorp, & Borsboom, 2013; Robinson, 2009; Simpson, 1951). To address these concerns, we propose a multilevel approach to model the fMRI networks, using a Gaussian graphical model at the individual level and a Curie-Weiss graphical model at the group level. Simulations show that our method outperforms individual or aggregate analysis in edge retrieval. We apply the proposed multilevel approach to resting-state fMRI data of 724 healthy participants, examining both their commonalities and individual differences. We not only recover the seven previously found resting-state networks at the group level but also observe considerable heterogeneity in the individual-level networks. Finally, we discuss the necessity of a multilevel approach, additional challenges, and possible future extensions.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"896-912"},"PeriodicalIF":3.1,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12543299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356602","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":"Structure-function coupling using fixel-based analysis and functional magnetic resonance imaging in Alzheimer's disease and mild cognitive impairment.","authors":"Charly Hugo Alexandre Billaud, Junhong Yu","doi":"10.1162/netn_a_00461","DOIUrl":"10.1162/netn_a_00461","url":null,"abstract":"<p><p>Functional MRI (fMRI) and diffusion-weighted imaging (DWI) help explore correlations between structural connectivity (SC) and functional connectivity (FC; SC-FC coupling). Studies on mild cognitive impairment (MCI) and Alzheimer's disease (AD) observed coupling disruptions, co-occurring with cognitive decline. Advanced \"fixel-based\" analyses improved DWI's accuracy in assessing microstructural and macrostructural features of white matter (WM), but previous aging coupling studies commonly defined SC via tensor-based tractography and streamline counts, thereby missing fiber-specific information. We investigated different types of fixel-FC coupling and their relation to cognition in 392 participants (Age_mean = 73; 207 females) from the ADNI. Two hundred twenty-five controls, 142 MCI, and 25 AD with diffusion-weighted and resting-state fMRI scans were analyzed. Structural connectomes were constructed using average fixel metrics (fiber density (FD), fiber-bundle cross-section log, and combined [FDC]) as edges. SC-FC coupling for each SC metric was calculated at overall network, edge, and node levels. Overall DMN, node- and edge-specific coupling differences were found across SC measures and groups. DMN nodal coupling significantly predicted Mini-Mental Status Examination score and verbal memory. In conclusion, different types of fixel-based coupling alterations can be observed across the neurocognitive aging spectrum, in particular, FD-FC and FDC-FC coupling between DMN regions are associated with cognitive functioning.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"969-989"},"PeriodicalIF":3.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12543303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145356557","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":"Joint estimation of source dynamics and interactions from MEG data.","authors":"Narayan Puthanmadam Subramaniyam, Filip Tronarp, Simo Särkkä, Lauri Parkkonen","doi":"10.1162/netn_a_00453","DOIUrl":"10.1162/netn_a_00453","url":null,"abstract":"<p><p>Current techniques to estimate directed functional connectivity from magnetoencephalography (MEG) signals involve two sequential steps: (a) estimation of the sources and their amplitude time series from the MEG data and (b) estimation of directed interactions between the source time series. However, such a sequential approach is not optimal as it leads to spurious connectivity due to spatial leakage. Here, we present an algorithm to jointly estimate the source and connectivity parameters using Bayesian filtering. We refer to this new algorithm as JEDI-MEG (Joint Estimation of source Dynamics and Interactions from MEG data). By formulating a state-space model for the locations and amplitudes of a given number of sources, we show that estimation of their connections can be reduced to a system identification problem. Using simulated MEG data, we show that the joint approach provides a more accurate reconstruction of connectivity parameters than the conventional two-step approach. Using real MEG responses to visually presented faces in 16 subjects, we also demonstrate that our method gives source and connectivity estimates that are both physiologically plausible and largely consistent across subjects. In conclusion, the proposed joint estimation approach outperforms the traditional two-step approach in determining functional connectivity in MEG data.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"842-868"},"PeriodicalIF":3.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700048","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":"Longitudinal changes in MEG-based brain network topology of ALS patients with cognitive/behavioral impairment-An exploratory study.","authors":"Rosanne Govaarts, Elliz P Scheijbeler, Emma Beeldman, Matteo Fraschini, Alessandra Griffa, Marjolein M A Engels, Anneke J van der Kooi, Yolande A L Pijnenburg, Marianne de Visser, Cornelis J Stam, Joost Raaphorst, Arjan Hillebrand","doi":"10.1162/netn_a_00450","DOIUrl":"10.1162/netn_a_00450","url":null,"abstract":"<p><p>Amyotrophic lateral sclerosis (ALS) with only motor impairment (ALS-pure motor) and the behavioral variant of frontotemporal dementia (bvFTD) are hypothesized to represent extreme ends of a disease spectrum, which encompasses ALS with cognitive/behavioral impairment (ALSci/bi). In this longitudinal magnetoencephalography (MEG) study, we investigated changes in brain network topology of ALSci/bi over time as compared with ALS-pure motor and bvFTD patients. Resting-state MEG was recorded in ALS-pure motor (<i>n</i> = 9), ALSci/bi (<i>n</i> = 16), and bvFTD (<i>n</i> = 16) at baseline and 5-month follow-up, projected to source space. The corrected version of the amplitude envelope correlation was applied to compute frequency-band-specific functional connectivity between brain regions, from which the backbone of the functional networks was constructed using the minimum spanning tree (MST) approach. Reference MSTs were computed based on the functional connectivity matrices for ALS-pure motor and bvFTD, against which the networks of ALSci/bi were compared. We showed that, at baseline, networks in the theta band of ALSci/bi patients were more similar to ALS-pure motor than bvFTD. At follow-up, ALSci/bi patients' beta-band network similarity had moved away from ALS-pure motor and resembled bvFTD. In conclusion, our findings suggest that brain networks of ALSci/bi patients move along the ALS-bvFTD spectrum over time, from ALS-pure motor to bvFTD-like topology.</p>","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"9 3","pages":"824-841"},"PeriodicalIF":3.6,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12283152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144700049","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":"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":3.4,"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}