Brain Connectivity Toolbox

The Brain Connectivity Toolbox ( is a MATLAB toolbox for complex-network (graph) analysis of structural and functional brain-connectivity data sets. Several people have contributed to the toolbox and users are welcome to contribute new functions with due acknowledgement.

References in zbMATH (referenced in 70 articles )

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  1. Cheng, Hong; Cai, David; Zhou, Douglas: The extended Granger causality analysis for Hodgkin-Huxley neuronal models (2020)
  2. Chen, Shuo; Bowman, F. DuBois; Xing, Yishi: Detecting and testing altered brain connectivity networks with k-partite network topology (2020)
  3. Chen, Ximing; Ogura, Masaki; Preciado, Victor M.: Bounds on the spectral radius of digraphs from subgraph counts (2020)
  4. Demšar, Jure; Forsyth, Rob: Synaptic scaling improves the stability of neural mass models capable of simulating brain plasticity (2020)
  5. Ghaderi, Amir Hossein; Baltaretu, Bianca R.; Andevari, Masood Nemati; Bharmauria, Vishal; Balci, Fuat: Synchrony and complexity in state-related EEG networks: an application of spectral graph theory (2020)
  6. Li, Yuzhe; Shi, Dawei; Chen, Tongwen: Secure analysis of dynamic networks under pinning attacks against synchronization (2020)
  7. Lovato, Ilenia; Pini, Alessia; Stamm, Aymeric; Vantini, Simone: Model-free two-sample test for network-valued data (2020)
  8. Paul, Subhadeep; Chen, Yuguo: A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging (2020)
  9. Xu, Min; Jog, Varun; Loh, Po-Ling: Optimal rates for community estimation in the weighted stochastic block model (2020)
  10. Aliverti, Emanuele; Durante, Daniele: Spatial modeling of brain connectivity data via latent distance models with nodes clustering (2019)
  11. Bianchi, Daniele; Billio, Monica; Casarin, Roberto; Guidolin, Massimo: Modeling systemic risk with Markov switching graphical SUR models (2019)
  12. Das, Anup; Sexton, Daniel; Lainscsek, Claudia; Cash, Sydney S.; Sejnowski, Terrence J.: Characterizing brain connectivity from human electrocorticography recordings with unobserved inputs during epileptic seizures (2019)
  13. Duan, Zhen; Zou, Haodong; Min, Xing; Zhao, Shu; Chen, Jie; Zhang, Yanping: An adaptive granulation algorithm for community detection based on improved label propagation (2019)
  14. Kong, Wanzeng; Jiang, Bei; Fan, Qiaonan; Zhu, Li; Wei, Xuehui: Personal identification based on brain networks of EEG signals (2019)
  15. Nunes, Ronaldo V.; Reyes, Marcelo B.; de Camargo, Raphael Y.: Evaluation of connectivity estimates using spiking neuronal network models (2019)
  16. Wang, Daohua; Xue, Yumei; Zhang, Qian; Niu, Min: Scale-free and small-world properties of a special hierarchical network (2019)
  17. Chowdhury, Samir; Mémoli, Facundo: A functorial Dowker theorem and persistent homology of asymmetric networks (2018)
  18. Durante, Daniele; Dunson, David B.: Bayesian inference and testing of group differences in brain networks (2018)
  19. Lea Waller; Anastasia Brovkin; Lena Dorfschmidt; Danilo Bzdok; Henrik Walter; Johann Daniel Kruschwitz: GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures (2018) arXiv
  20. Le, Can M.; Levin, Keith; Levina, Elizaveta: Estimating a network from multiple noisy realizations (2018)

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