Network Neuroscience最新文献_第7页

Predictability of cortico-cortical connections in the mammalian brain 哺乳动物大脑皮质-皮质连接的可预测性

3区医学

Network Neuroscience Pub Date : 2023-11-01 DOI: 10.1162/netn_a_00345

Ferenc Molnár, Szabolcs Horvát, Ana R. Ribeiro Gomes, Jorge Martinez Armas, Botond Molnár, Mária Ercsey-Ravasz, Kenneth Knoblauch, Henry Kennedy, Zoltan Toroczkai

{"title":"Predictability of cortico-cortical connections in the mammalian brain","authors":"Ferenc Molnár, Szabolcs Horvát, Ana R. Ribeiro Gomes, Jorge Martinez Armas, Botond Molnár, Mária Ercsey-Ravasz, Kenneth Knoblauch, Henry Kennedy, Zoltan Toroczkai","doi":"10.1162/netn_a_00345","DOIUrl":"https://doi.org/10.1162/netn_a_00345","url":null,"abstract":"Abstract Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a non-human primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an Area Under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85–90% accuracy (mouse) and 70–80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"145 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weighting the Structural Connectome: Exploring its Impact on Network Properties and Predicting Cognitive Performance in the Human Brain 加权结构连接体:探索其对网络特性的影响和预测人类大脑的认知表现

3区医学

Network Neuroscience Pub Date : 2023-11-01 DOI: 10.1162/netn_a_00342

Hila Gast, Yaniv Assaf

{"title":"Weighting the Structural Connectome: Exploring its Impact on Network Properties and Predicting Cognitive Performance in the Human Brain","authors":"Hila Gast, Yaniv Assaf","doi":"10.1162/netn_a_00342","DOIUrl":"https://doi.org/10.1162/netn_a_00342","url":null,"abstract":"Abstract Brain function does not emerge from isolated activity, but rather from the interactions and exchanges between neural elements which form a network known as the connectome. The human connectome consists of structural and functional aspects. The structural connectome (SC) represents the anatomical connections and the functional connectome represents the resulting dynamics which emerge from this arrangement of structures. As there are different ways of weighting these connections, it is important to consider how such different approaches impact study conclusions. Here, we propose that different weighted connectomes result in varied network properties and while neither superior the other, selection might affect interpretation and conclusions in different study cases. We present three different weighting models, namely, Number of Streamlines (NOS), Fractional Anisotropy (FA), and Axon-Diameter Distribution (ADD), to demonstrate these differences. The later, is extracted using recently published AxSI method, and is first compared to commonly used weighting methods. Moreover, we explore the functional relevance of each weighted SC, using the HCP database. By analyzing intelligencerelated data, we develop a predictive model for cognitive performance based on graph properties and the NIH toolbox. Results demonstrate that the ADD SC, combined with a functional subnetwork model, outperforms other models in estimating cognitive performance.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"144 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135271087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Circuits in the Motor Cortex Explain Oscillatory Responses to Transcranial Magnetic Stimulation 运动皮层中的电路解释了经颅磁刺激的振荡反应

3区医学

Network Neuroscience Pub Date : 2023-11-01 DOI: 10.1162/netn_a_00341

Lysea Haggie, Thor Besier, Angus McMorland

{"title":"Circuits in the Motor Cortex Explain Oscillatory Responses to Transcranial Magnetic Stimulation","authors":"Lysea Haggie, Thor Besier, Angus McMorland","doi":"10.1162/netn_a_00341","DOIUrl":"https://doi.org/10.1162/netn_a_00341","url":null,"abstract":"Abstract Transcranial Magnetic Stimulation (TMS) is a popular method used to investigate brain function. Stimulation over the motor cortex evokes muscle contractions known as motor evoked potentials (MEPs) and also high frequency volleys of electrical activity measured in the cervical spinal cord. The physiological mechanisms of these experimentally derived responses remain unclear, but it is thought that the connections between circuits of excitatory and inhibitory neurons play a vital role. Using a spiking neural network model of the motor cortex, we explained the generation of waves of activity, so called ‘I-waves’, following cortical stimulation. The model reproduces a number of experimentally known responses including direction of TMS, increased inhibition and changes in strength. Using populations of thousands of neurons in a model of cortical circuitry we showed that the cortex generated transient oscillatory responses without any tuning, and that neuron parameters such as refractory period and delays influenced the pattern and timing of those oscillations. By comparing our network with simpler, previously proposed circuits, we explored the contributions of specific connections and found that recurrent inhibitory connections are vital in producing later waves which significantly impact the production of motor evoked potentials in downstream muscles (Thickbroom, 2011). This model builds on previous work to increase our understanding of how complex circuitry of the cortex is involved in the generation of I-waves.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"1 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135325885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

State switching and high-order spatiotemporal organization of dynamic Functional Connectivity are disrupted by Alzheimer’s Disease 阿尔茨海默病破坏了动态功能连接的状态转换和高阶时空组织

3区医学

Network Neuroscience Pub Date : 2023-10-17 DOI: 10.1162/netn_a_00332

Lucas Arbabyazd, Spase Petkoski, Michael Breakspear, Ana Solodkin, Demian Battaglia, Viktor Jirsa

{"title":"State switching and high-order spatiotemporal organization of dynamic Functional Connectivity are disrupted by Alzheimer’s Disease","authors":"Lucas Arbabyazd, Spase Petkoski, Michael Breakspear, Ana Solodkin, Demian Battaglia, Viktor Jirsa","doi":"10.1162/netn_a_00332","DOIUrl":"https://doi.org/10.1162/netn_a_00332","url":null,"abstract":"Abstract Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of interregional correlations, whose structured flexibility relates to cognitive performance. Here, we analyze resting-state dynamic functional connectivity (dFC) in a cohort of older adults, including amnesic mild cognitive impairment (aMCI, N = 34) and Alzheimer’s disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively “supernormal” controls (SNC, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting-state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Hub overload and failure as a final common pathway in neurological brain network disorders 中枢过载和故障是神经性脑网络疾病的最终共同途径

3区医学

Network Neuroscience Pub Date : 2023-10-02 DOI: 10.1162/netn_a_00339

Cornelis Jan Stam

{"title":"Hub overload and failure as a final common pathway in neurological brain network disorders","authors":"Cornelis Jan Stam","doi":"10.1162/netn_a_00339","DOIUrl":"https://doi.org/10.1162/netn_a_00339","url":null,"abstract":"Abstract Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer’s disease hyperactive hub nodes are directly associated with amyloid beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer’s disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Controversies and progress on standardization of large-scale brain network nomenclature. 大规模脑网络命名标准化的争议和进展。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-10-01 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00323

Lucina Q Uddin, Richard F Betzel, Jessica R Cohen, Jessica S Damoiseaux, Felipe De Brigard, Simon B Eickhoff, Alex Fornito, Caterina Gratton, Evan M Gordon, Angela R Laird, Linda Larson-Prior, A Randal McIntosh, Lisa D Nickerson, Luiz Pessoa, Ana Luísa Pinho, Russell A Poldrack, Adeel Razi, Sepideh Sadaghiani, James M Shine, Anastasia Yendiki, B T Thomas Yeo, R Nathan Spreng

{"title":"Controversies and progress on standardization of large-scale brain network nomenclature.","authors":"Lucina Q Uddin, Richard F Betzel, Jessica R Cohen, Jessica S Damoiseaux, Felipe De Brigard, Simon B Eickhoff, Alex Fornito, Caterina Gratton, Evan M Gordon, Angela R Laird, Linda Larson-Prior, A Randal McIntosh, Lisa D Nickerson, Luiz Pessoa, Ana Luísa Pinho, Russell A Poldrack, Adeel Razi, Sepideh Sadaghiani, James M Shine, Anastasia Yendiki, B T Thomas Yeo, R Nathan Spreng","doi":"10.1162/netn_a_00323","DOIUrl":"10.1162/netn_a_00323","url":null,"abstract":"Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"864-905"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41148136","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

Functional connectome fingerprinting across the lifespan. 贯穿整个生命周期的功能性连接体指纹图谱。

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-10-01 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00320

Frédéric St-Onge, Mohammadali Javanray, Alexa Pichet Binette, Cherie Strikwerda-Brown, Jordana Remz, R Nathan Spreng, Golia Shafiei, Bratislav Misic, Étienne Vachon-Presseau, Sylvia Villeneuve

{"title":"Functional connectome fingerprinting across the lifespan.","authors":"Frédéric St-Onge, Mohammadali Javanray, Alexa Pichet Binette, Cherie Strikwerda-Brown, Jordana Remz, R Nathan Spreng, Golia Shafiei, Bratislav Misic, Étienne Vachon-Presseau, Sylvia Villeneuve","doi":"10.1162/netn_a_00320","DOIUrl":"10.1162/netn_a_00320","url":null,"abstract":"Systematic changes have been observed in the functional architecture of the human brain with advancing age. However, functional connectivity (FC) is also a powerful feature to detect unique \"connectome fingerprints,\" allowing identification of individuals among their peers. Although fingerprinting has been robustly observed in samples of young adults, the reliability of this approach has not been demonstrated across the lifespan. We applied the fingerprinting framework to the Cambridge Centre for Ageing and Neuroscience cohort (n = 483 aged 18 to 89 years). We found that individuals are \"fingerprintable\" (i.e., identifiable) across independent functional MRI scans throughout the lifespan. We observed a U-shape distribution in the strength of \"self-identifiability\" (within-individual correlation across modalities), and \"others-identifiability\" (between-individual correlation across modalities), with a decrease from early adulthood into middle age, before improving in older age. FC edges contributing to self-identifiability were not restricted to specific brain networks and were different between individuals across the lifespan sample. Self-identifiability was additionally associated with regional brain volume. These findings indicate that individual participant-level identification is preserved across the lifespan despite the fact that its components are changing nonlinearly.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"1206-1227"},"PeriodicalIF":4.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473304/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152494","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

Improvements in task performance after practice are associated with scale-free dynamics of brain activity. 练习后任务表现的改善与大脑活动的无标度动力学有关。

IF 3.6 3区医学

Network Neuroscience Pub Date : 2023-10-01 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00319

Omid Kardan, Andrew J Stier, Elliot A Layden, Kyoung Whan Choe, Muxuan Lyu, Xihan Zhang, Sian L Beilock, Monica D Rosenberg, Marc G Berman

{"title":"Improvements in task performance after practice are associated with scale-free dynamics of brain activity.","authors":"Omid Kardan, Andrew J Stier, Elliot A Layden, Kyoung Whan Choe, Muxuan Lyu, Xihan Zhang, Sian L Beilock, Monica D Rosenberg, Marc G Berman","doi":"10.1162/netn_a_00319","DOIUrl":"10.1162/netn_a_00319","url":null,"abstract":"Although practicing a task generally benefits later performance on that same task, there are individual differences in practice effects. One avenue to model such differences comes from research showing that brain networks extract functional advantages from operating in the vicinity of criticality, a state in which brain network activity is more scale-free. We hypothesized that higher scale-free signal from fMRI data, measured with the Hurst exponent (H), indicates closer proximity to critical states. We tested whether individuals with higher H during repeated task performance would show greater practice effects. In Study 1, participants performed a dual-n-back task (DNB) twice during MRI (n = 56). In Study 2, we used two runs of n-back task (NBK) data from the Human Connectome Project sample (n = 599). In Study 3, participants performed a word completion task (CAST) across six runs (n = 44). In all three studies, multivariate analysis was used to test whether higher H was related to greater practice-related performance improvement. Supporting our hypothesis, we found patterns of higher H that reliably correlated with greater performance improvement across participants in all three studies. However, the predictive brain regions were distinct, suggesting that the specific spatial H↑ patterns are not task-general.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"1129-1152"},"PeriodicalIF":3.6,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41153024","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

Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states. 静态和动态功能连接组揭示了整个大脑网络在认知状态下的重构特征。

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-10-01 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00314

Heming Zhang, Chun Meng, Xin Di, Xiao Wu, Bharat Biswal

{"title":"Static and dynamic functional connectome reveals reconfiguration profiles of whole-brain network across cognitive states.","authors":"Heming Zhang, Chun Meng, Xin Di, Xiao Wu, Bharat Biswal","doi":"10.1162/netn_a_00314","DOIUrl":"10.1162/netn_a_00314","url":null,"abstract":"Assessment of functional connectivity (FC) has revealed a great deal of knowledge about the macroscale spatiotemporal organization of the brain network. Recent studies found task-versus-rest network reconfigurations were crucial for cognitive functioning. However, brain network reconfiguration remains unclear among different cognitive states, considering both aggregate and time-resolved FC profiles. The current study utilized static FC (sFC, i.e., long timescale aggregate FC) and sliding window-based dynamic FC (dFC, i.e., short timescale time-varying FC) approaches to investigate the similarity and alterations of edge weights and network topology at different cognitive loads, particularly their relationships with specific cognitive process. Both dFC/sFC networks showed subtle but significant reconfigurations that correlated with task performance. At higher cognitive load, brain network reconfiguration displayed increased functional integration in the sFC-based aggregate network, but faster and larger variability of modular reorganization in the dFC-based time-varying network, suggesting difficult tasks require more integrated and flexible network reconfigurations. Moreover, sFC-based network reconfigurations mainly linked with the sensorimotor and low-order cognitive processes, but dFC-based network reconfigurations mainly linked with the high-order cognitive process. Our findings suggest that reconfiguration profiles of sFC/dFC networks provide specific information about cognitive functioning, which could potentially be used to study brain function and disorders.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"1034-1050"},"PeriodicalIF":4.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473282/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41169776","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

Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model. 使用Jansen和Rit神经质量模型比较个体和组水平模拟的神经生理脑连接。

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-10-01 eCollection Date: 2023-01-01 DOI: 10.1162/netn_a_00303

S D Kulik, L Douw, E van Dellen, M D Steenwijk, J J G Geurts, C J Stam, A Hillebrand, M M Schoonheim, P Tewarie

{"title":"Comparing individual and group-level simulated neurophysiological brain connectivity using the Jansen and Rit neural mass model.","authors":"S D Kulik, L Douw, E van Dellen, M D Steenwijk, J J G Geurts, C J Stam, A Hillebrand, M M Schoonheim, P Tewarie","doi":"10.1162/netn_a_00303","DOIUrl":"https://doi.org/10.1162/netn_a_00303","url":null,"abstract":"Computational models are often used to assess how functional connectivity (FC) patterns emerge from neuronal population dynamics and anatomical brain connections. It remains unclear whether the commonly used group-averaged data can predict individual FC patterns. The Jansen and Rit neural mass model was employed, where masses were coupled using individual structural connectivity (SC). Simulated FC was correlated to individual magnetoencephalography-derived empirical FC. FC was estimated using phase-based (phase lag index (PLI), phase locking value (PLV)), and amplitude-based (amplitude envelope correlation (AEC)) metrics to analyze their goodness of fit for individual predictions. Individual FC predictions were compared against group-averaged FC predictions, and we tested whether SC of a different participant could equally well predict participants' FC patterns. The AEC provided a better match between individually simulated and empirical FC than phase-based metrics. Correlations between simulated and empirical FC were higher using individual SC compared to group-averaged SC. Using SC from other participants resulted in similar correlations between simulated and empirical FC compared to using participants' own SC. This work underlines the added value of FC simulations using individual instead of group-averaged SC for this particular computational model and could aid in a better understanding of mechanisms underlying individual functional network trajectories.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 3","pages":"950-965"},"PeriodicalIF":4.7,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473283/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41170715","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