Welcome to the website of the complex networks and brain dynamics group. The workgroup is a part of the Department of Complex Systems of the Institute of Computer Science of the Czech Academy of Sciences.
We organized Workshop on Prediction in Complex Networked Systems: Focus on Epilepsy on December 13-14, 2018
We focus on the development and application of methods of analysis and modelling of real-world complex networked systems, with particular interest in the structure and dynamics of human brain function. Our main research areas are:
- Brain networks
- Neuroimaging data analysis
- Brain dynamics modelling
- Causal interaction inference
- Causality and information flow inference
- Nonlinearity and nonstationarity
- Complex network analysis
- Graph theory
- Machine learning and multivariate statistics
- Application in neuroscience, climate research, economics, ...
Loss of neuronal network resilience precedes seizures and determines the ictogenic nature of interictal synaptic perturbations
WC Chang, J Kudlacek, J Hlinka, J Chvojka, M Hadrava, V Kumpost, ...
Nature neuroscience, 2018, 21 (12), 1742
Small-world bias of correlation networks: From brain to climate
J Hlinka, D Hartman, N Jajcay, D Tomeček, J Tintěra, M Paluš
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2017, 27 (3), 035812
Identifying causal gateways and mediators in complex spatio-temporal systems
J Runge, V Petoukhov, JF Donges, J Hlinka, N Jajcay, M Vejmelka, D Hartman, ...
Nature communications, 2015, 6, 8502
Reliability of inference of directed climate networks using conditional mutual information
J Hlinka, D Hartman, M Vejmelka, J Runge, N Marwan, J Kurths, M Paluš
Entropy, 2013, 15 (6), 2023-2045
Functional connectivity in resting-state fMRI: Is linear correlation sufficient?
J Hlinka, M Paluš, M Vejmelka, D Mantini, M Corbetta
Neuroimage, 2011, 54 (3), 2218-2225
Institute of Computer Science
The Czech Academy of Sciences
Pod Vodárenskou věží 271/2
182 07 Praha 8