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 74 articles )

Showing results 1 to 20 of 74.
Sorted by year (citations)

1 2 3 4 next

  1. Gopalakrishnan Meena, Muralikrishnan; Taira, Kunihiko: Identifying vortical network connectors for turbulent flow modification (2021)
  2. Lima, Verusca S.; Madeiro, Francisco; Lima, Juliano B.: Three-dimensional steerable discrete cosine transform with application to 3D image compression (2021)
  3. Cheng, Hong; Cai, David; Zhou, Douglas: The extended Granger causality analysis for Hodgkin-Huxley neuronal models (2020)
  4. Chen, Shuo; Bowman, F. DuBois; Xing, Yishi: Detecting and testing altered brain connectivity networks with k-partite network topology (2020)
  5. Chen, Ximing; Ogura, Masaki; Preciado, Victor M.: Bounds on the spectral radius of digraphs from subgraph counts (2020)
  6. Demšar, Jure; Forsyth, Rob: Synaptic scaling improves the stability of neural mass models capable of simulating brain plasticity (2020)
  7. 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)
  8. Hövel, Philipp; Viol, Aline; Loske, Philipp; Merfort, Leon; Vuksanović, Vesna: Synchronization in functional networks of the human brain (2020)
  9. Li, Yuzhe; Shi, Dawei; Chen, Tongwen: Secure analysis of dynamic networks under pinning attacks against synchronization (2020)
  10. Lovato, Ilenia; Pini, Alessia; Stamm, Aymeric; Vantini, Simone: Model-free two-sample test for network-valued data (2020)
  11. Paul, Subhadeep; Chen, Yuguo: A random effects stochastic block model for joint community detection in multiple networks with applications to neuroimaging (2020)
  12. Schilling, Kurt G.; Rogers, Baxter; Anderson, Adam W.; Landman, Bennett A.: Current challenges and future directions in diffusion MRI: from model- to data-driven analysis (2020)
  13. Xu, Min; Jog, Varun; Loh, Po-Ling: Optimal rates for community estimation in the weighted stochastic block model (2020)
  14. Aliverti, Emanuele; Durante, Daniele: Spatial modeling of brain connectivity data via latent distance models with nodes clustering (2019)
  15. Bianchi, Daniele; Billio, Monica; Casarin, Roberto; Guidolin, Massimo: Modeling systemic risk with Markov switching graphical SUR models (2019)
  16. 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)
  17. 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)
  18. Kong, Wanzeng; Jiang, Bei; Fan, Qiaonan; Zhu, Li; Wei, Xuehui: Personal identification based on brain networks of EEG signals (2019)
  19. Nunes, Ronaldo V.; Reyes, Marcelo B.; de Camargo, Raphael Y.: Evaluation of connectivity estimates using spiking neuronal network models (2019)
  20. Wang, Daohua; Xue, Yumei; Zhang, Qian; Niu, Min: Scale-free and small-world properties of a special hierarchical network (2019)

1 2 3 4 next