Publikationen von Thomas R. Knösche

Wang, P.; He, Y.; Maess, B.; Yue, J.; Chen, L.; Brauer, J.; Friederici, A. D.; Knösche, T. R.: Alpha power during task performance predicts individual language comprehension. NeuroImage, 119449 (2022)

Gong, R.; Mühlberg, C.; Wegscheider, M.; Fricke, C.; Rumpf, J.-J.; Knösche, T. R.; Classen, J.: Cross-frequency phase-amplitude coupling in repetitive movements in patients with Parkinson’s disease. Journal of Neurophysiology 127 (6), S. 1606 - 1621 (2022)

Weise, K.; Müller, E.; Poßner, L.; Knösche, T. R.: Comparison of the performance and reliability between improved sampling strategies for polynomial chaos expansion. Mathematical Biosciences and Engineering 19 (8), S. 7425 - 7480 (2022)

Schmidt, H.; Knösche, T. R.: Modelling the effect of ephaptic coupling on spike propagation in peripheral nerve fibres. Biological Cybernetics (2022)

Weise, K.; Wartman, W. A.; Knösche, T. R.; Nummenmaa, A. R.; Makarov, S. N.: The effect of meninges on the electric fields in TES and TMS: Numerical modeling with adaptive mesh refinement. Brain Stimulation 15 (3), S. 654 - 663 (2022)

Numssen, O.; Zier, A.; Thielscher, A.; Hartwigsen, G.; Knösche, T. R.; Weise, K.: Efficient high-resolution TMS mapping of the human motor cortex by nonlinear regression. NeuroImage 245, 118654 (2021)

Feng, Z.-J.; Deng, X.-P.; Zhao, N.; Jin, J.; Yue, J.; Hu, Y.-S.; Jing, Y.; Wang, H.-X.; Knösche, T. R.; Zang, Y.-F. et al.; Wang, J.: Resting-state fMRI functional connectivity strength predicts local activity change in the dorsal cingulate cortex: A multi-target focused rTMS study. Cerebral Cortex (2021)

Gast, R.; Knösche, T. R.; Schmidt, H.: Mean-field approximations of networks of spiking neurons with short-term synaptic plasticity. Physical Review E 104, 044310 (2021)

Wang, P.; Knösche, T. R.; Chen, L.; Brauer, J.; Friederici, A. D.; Maess, B.: Functional brain plasticity during L1 training on complex sentences: Changes in gamma‐band oscillatory activity. Human Brain Mapping 42 (12), S. 3858 - 3870 (2021)

Gast, R.; Gong, R.; Schmidt, H.; Meijer, H. G.E.; Knösche, T. R.: On the role of arkypallidal and prototypical neurons for phase transitions in the external pallidum. The Journal of Neuroscience 41 (31), S. 6673 - 6683 (2021)

Gong, R.; Wegscheider, M.; Mühlberg, C.; Gast, R.; Fricke, C.; Rumpf, J.-J.; Nikulin, V. V.; Knösche, T. R.; Classen, J.: Spatiotemporal features of β-γ phase-amplitude coupling in Parkinson’s disease derived from scalp EEG. Brain 144 (2), S. 487 - 503 (2021)

Gast, R.; Schmidt, H.; Knösche, T. R.: A mean-field description of bursting dynamics in spiking neural networks with short-term adaptation. Neural Computation 32 (9), S. 1615 - 1634 (2020)

Weise, K.; Numssen, O.; Thielscher, A.; Hartwigsen, G.; Knösche, T. R.: A novel approach to localize cortical TMS effects. NeuroImage 209, 116486 (2020)

Weise, K.; Poßner, L.; Müller, E.; Gast, R.; Knösche, T. R.: Pygpc: A sensitivity and uncertainty analysis toolbox for Python. SoftwareX 11, 100450 (2020)

Gast, R.; Rose, D.; Möller, H. E.; Weiskopf, N.; Knösche, T. R.: PyRates - A Python framework for rate-based neural simulations. PLOS One 14 (12), e0225900 (2019)

Chien, V. S. C.; Maess, B.; Knösche, T. R.: A generic deviance detection principle for cortical on/off responses, omission response, and mismatch negativity. Biological Cybernetics 113 (5-6), S. 475 - 494 (2019)

Schmidt, H.; Knösche, T. R.: Action potential propagation and synchronisation in myelinated axons. PLoS Computational Biology 15 (10), e1007004 (2019)

Saturnino, G. B.; Thielscher, A.; Madsen, K. H.; Knösche, T. R.; Weise, K.: A principled approach to conductivity uncertainty analysis in electric field calculations. NeuroImage 188, S. 821 - 834 (2019)

Kunze, T.; Haueisen, J.; Knösche, T. R.: Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models. Biological Cybernetics 113 (3), S. 273 - 291 (2019)

Tittgemeyer, M.; Rigoux, L.; Knösche, T. R.: Cortical parcellation based on structural connectivity: A case for generative models. NeuroImage 173, S. 592 - 603 (2018)