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All Outputs (11)

Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics (2023)
Journal Article
Mackay, M., Huo, S., & Kaiser, M. (2023). Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics. PLoS Computational Biology, 19(8), Article e1011349. https://doi.org/10.1371/journal.pcbi.1011349

Significant research has investigated synchronisation in brain networks, but the bulk of this work has explored the contribution of brain networks at the macroscale. Here we explore the effects of changing network topology on functional dynamics in s... Read More about Spatial organisation of the mesoscale connectome: A feature influencing synchrony and metastability of network dynamics.

Connectomes: from a sparsity of networks to large-scale databases (2023)
Journal Article
Kaiser, M. (2023). Connectomes: from a sparsity of networks to large-scale databases. Frontiers in Neuroinformatics, 17, Article 1170337. https://doi.org/10.3389/fninf.2023.1170337

The analysis of whole brain networks started in the 1980s when only a handful of connectomes were available. In these early days, information about the human connectome was absent and one could only dream about having information about connectivity i... Read More about Connectomes: from a sparsity of networks to large-scale databases.

Nonoptimal component placement of the human connectome supports variable brain dynamics (2022)
Journal Article
Hayward, C. J., Huo, S., Chen, X., & Kaiser, M. (2023). Nonoptimal component placement of the human connectome supports variable brain dynamics. Network Neuroscience, 7(1), 254-268. https://doi.org/10.1162/netn_a_00282

Neural systems are shaped by multiple constraints, balancing region communication with the cost of establishing and maintaining physical connections. It has been suggested that the lengths of neural projections be minimized, reducing their spatial an... Read More about Nonoptimal component placement of the human connectome supports variable brain dynamics.

Time-limited self-sustaining rhythms and state transitions in brain networks (2022)
Journal Article
Huo, S., Zou, Y., Kaiser, M., & Liu, Z. (2022). Time-limited self-sustaining rhythms and state transitions in brain networks. Physical Review Research, 4(2), Article 023076. https://doi.org/10.1103/PhysRevResearch.4.023076

Resting-state networks usually show time-limited self-sustaining oscillatory patterns (TLSOPs) with the characteristic features of multiscaled rhythms and frequent switching between different rhythms, but the underlying mechanisms remain unclear. To... Read More about Time-limited self-sustaining rhythms and state transitions in brain networks.

Dynamic reconfiguration of macaque brain networks during natural vision (2021)
Journal Article
Ortiz-Rios, M., Balezeau, F., Haag, M., Schmid, M. C., & Kaiser, M. (2021). Dynamic reconfiguration of macaque brain networks during natural vision. NeuroImage, 244, Article 118615. https://doi.org/10.1016/j.neuroimage.2021.118615

Natural vision engages a wide range of higher-level regions that integrate visual information over the large-scale brain network. How interareal connectivity reconfigures during the processing of ongoing natural visual scenes and how these dynamic fu... Read More about Dynamic reconfiguration of macaque brain networks during natural vision.

BioDynaMo: a modular platform for high-performance agent-based simulation (2021)
Journal Article
Breitwieser, L., Hesam, A., de Montigny, J., Vavourakis, V., Iosif, A., Jennings, J., …Al-Ars, Z. (2022). BioDynaMo: a modular platform for high-performance agent-based simulation. Bioinformatics, 38(2), 453-460. https://doi.org/10.1093/bioinformatics/btab649

Motivation Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. Resul... Read More about BioDynaMo: a modular platform for high-performance agent-based simulation.

Connectivity within regions characterizes epilepsy duration and treatment outcome (2021)
Journal Article
Chen, X., Wang, Y., Kopetzky, S. J., Butz‐Ostendorf, M., & Kaiser, M. (2021). Connectivity within regions characterizes epilepsy duration and treatment outcome. Human Brain Mapping, 42(12), 3777-3791. https://doi.org/10.1002/hbm.25464

Finding clear connectome biomarkers for temporal lobe epilepsy (TLE) patients, in particular at early disease stages, remains a challenge. Currently, the whole-brain structural connectomes are analyzed based on coarse parcellations (up to 1,000 nodes... Read More about Connectivity within regions characterizes epilepsy duration and treatment outcome.

Computational modeling of neurostimulation in brain diseases (2015)
Book Chapter
Wang, Y., Hutchings, F., & Kaiser, M. (2015). Computational modeling of neurostimulation in brain diseases. In Computational Neurostimulation (191-228). Elsevier. https://doi.org/10.1016/bs.pbr.2015.06.012

Neurostimulation as a therapeutic tool has been developed and used for a range of different diseases such as Parkinson's disease, epilepsy, and migraine. However, it is not known why the efficacy of the stimulation varies dramatically across patients... Read More about Computational modeling of neurostimulation in brain diseases.

Multiple-Scale Hierarchical Connectivity of Cortical Networks Limits the Spread of Activity (2008)
Book Chapter
Kaiser, M. (2008). Multiple-Scale Hierarchical Connectivity of Cortical Networks Limits the Spread of Activity. In Computational Neuroscience in Epilepsy (132-140). https://doi.org/10.1016/B978-012373649-9.50012-0

The anatomy of the brain that cortical architecture and connections are organized in a hierarchical and modular way, from cellular microcircuits in cortical columns at the lowest level, via cortical areas at the intermediate level, to clusters of hig... Read More about Multiple-Scale Hierarchical Connectivity of Cortical Networks Limits the Spread of Activity.