Zusammenfassung
Any discussion of brain repair, rehabilitation, and functional recovery imperatively requires a working definition of “function” (Jirsa et al. 2019). If such definition is not explicitly provided, which is more common than not, then the precedent statement still remains valid and is implied by the choice of methods applied in the investigation. An illustrative and recent example is the use of resting state paradigms in modern neuroscience, in which spatiotemporal brain activity is recorded using neuroimaging techniques such as functional MRI or EEG and then cast into a measure, e.g., functional connectivity, which captures the Pearson correlation of brain activations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Literatur
Aerts, H., Schirner, M., Jeurissen, B., Van Roost, D., Achten, E., Ritter, P., & Marinazzo, D. (2018). Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain. Eneuro, 5(3). https://doi.org/10.1523/eneuro.0083-18.2018.
Beer, R. D., Chiel, H. J., Gallagher, J. C. (1999). Evolution and analysis of model CPGs for walking: ii. general principles and individual variability. Journal of Computational Neuroscience, 7, 119–147. PMID: 10515251.
Changeux, J.-P., Courrege, P., & Danchin, A. (1973). A Theory of the Epigenesis of Neuronal Networks by Selective Stabilization of Synapses. Proceedings of the National Academy of Sciences, 70(10), 2974–2978. https://doi.org/10.1073/pnas.70.10.2974.
Chiel, H. J., Beer, R. D., Gallagher, J. C. (1999). Evolution and analysis of model CPGs for walking: I. dynamical modules. Journal of Computational Neuroscience, 7, 99–118. PMID: 10515250.
Deco, G., Jirsa, V., McIntosh, A. R., Sporns, O., & Kotter, R. (2009). Key role of coupling, delay, and noise in resting brain fluctuations. Proceedings of the National Academy of Sciences, 106(25), 10302–10307. https://doi.org/10.1073/pnas.0901831106.
Deco, G., Jirsa, V. K., & McIntosh, A. R. (2013). Resting brains never rest: computational insights into potential cognitive architectures. Trends in neurosciences, 36(5), 268-274.
Edelman, G. M., & Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences, 98(24), 13763–13768. https://doi.org/10.1073/pnas.231499798.
Falcon, M. I., Jirsa, V., & Solodkin, A. (2016a). A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain. Current opinion in neurology, 29(4), 429.
Falcon, M. I., Riley, J. D., Jirsa, V., McIntosh, A. R., Chen, E. E., & Solodkin, A. (2016b). Functional mechanisms of recovery after chronic stroke: modeling with the Virtual Brain. Eneuro. https://doi.org/10.1523/ENEURO.0158-15.2016.
Frégnac, Y. (2017). Big data and the industrialization of neuroscience: a safe roadmap for understanding the brain? Science, 358, 470–477. https://doi.org/10.1126/science.aan8866-
Fuchs, A., Jirsa, V. K., & Kelso, J. A. (2000a). Issues in the coordination of human brain activity and motor behavior. Neuroimage, 11, 375-377.
Fuchs, A., Jirsa, V. K., & Kelso, J. S. (2000b). Theory of the relation between human brain activity (MEG) and hand movements. Neuroimage, 11(5), 359-369.
Haken, H. (1983). Synergetics, an Introduction: Nonequilibrium Phase Transitions and Self-Organization in Physics, Chemistry, and Biology New York: Springer.
Hansen, E. C., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015). Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage, 105, 525-535.
Huys, R., Perdikis, D., & Jirsa, V. K. (2014). Functional architectures and structured flows on manifolds: A dynamical framework for motor behavior. Psychological review, 121(3), 302.
Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical review, 106(4), 620.
Jirsa, V. K., & Kelso, J. S. (2000). Spatiotemporal pattern formation in neural systems with heterogeneous connection topologies. Physical Review E, 62(6), 8462-8465.
Jirsa, V. K., Jantzen, K. J., Fuchs, A., & Kelso, J. S. (2002). Spatiotemporal forward solution of the EEG and MEG using network modeling. IEEE transactions on medical imaging, 21(5), 493-504.
Jirsa, V. K., McIntosh, A. R., Huys, R. (2019). Grand Unified Theories of the brain need better understanding of behavior: the two-tiered emergence of function. Ecological psychology, 31(3), 152-165.
Levitis, D. A., Lidicker Jr, W. Z., & Freund, G. (2009). Behavioural biologists do not agree on what constitutes behaviour. Animal behaviour, 78(1), 103-110.
Pillai, A. S., & Jirsa, V. K. (2017). Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior. Neuron, 94(5), 1010–1026. https://doi.org/10.1016/j.neuron.2017.05.013.
Prinz, A. A., Bucher, D., Marder, E. (2004). Similar network activity from disparate circuit parameters. NatureNeuroscience, 7, 1345–1352. https://doi.org/10.1038/nn1352.
Proix, T., Bartolomei, F., Guye, M., Jirsa, V. K. (2017). Individual brain structure and modeling predict seizure propagation. Brain, 140(3), 641-654.
Schirner, M., McIntosh, A. R., Jirsa, V., Deco, G., & Ritter, P. (2018). Inferring multi-scale neural mechanisms with brain network modelling. Elife, 7, e28927.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this chapter
Cite this chapter
Jirsa, V. (2020). Structured Flows on Manifolds as guiding concepts in brain science. In: Viol, K., Schöller, H., Aichhorn, W. (eds) Selbstorganisation – ein Paradigma für die Humanwissenschaften. Springer, Wiesbaden. https://doi.org/10.1007/978-3-658-29906-4_6
Download citation
DOI: https://doi.org/10.1007/978-3-658-29906-4_6
Published:
Publisher Name: Springer, Wiesbaden
Print ISBN: 978-3-658-29905-7
Online ISBN: 978-3-658-29906-4
eBook Packages: Psychology (German Language)