The aim of our research group is to construct mathematical models of the neuronal network dynamics underlying motor actions in healthy humans and stroke patients, using recordings of individual brain activity (EEG and fMRI).
The vast majority of motor actions is the result of a complex interplay of various brain regions. In the past decades, brain regions involved in movement generation have been intensively investigated in both animal models as well as in humans.
Likewise,experimental and clinical data pinpoint to the crucial role of certain modulatory systems,e.g., the dopaminergic system. Recent techniques in computational neuroscience allow us to assess interregional interactions from times series acquired using in-vivo
techniques like electroencephalography (EEG) or functional magnetic resonance imaging (fMRI).
These techniques have provided first insights as to how areas assemble into
functional networks depending on the motor task. However, our knowledge on the neural processes that encode individual movements and how they are changed during stroke is relatively poor.
Here, generative models and network simulations have the great advantage to decompose the complexity of neural mass activity into a set of equations that explain most of the behavioural variance.
Unveiling the network dynamics underlying specific motor actions further advances our understanding of the neurobiological mechanisms that
enable the brain to interact with the environment. The aim of our research group is to construct mathematical models of the
neuronal network dynamics underlying motor actions in healthy humans and stroke
patients, using recordings of individual brain activity (EEG and fMRI). We intend to use mathematical models of neural oscillators, which are coupled through dynamic synapses. These synapses are affected by the activity of certain neuromodulators. Numerical Simulations of such oscillatory networks and the analysis of their complex dynamics will allow us to explain the neuronal dynamics underlying motor behaviour in healthy and pathological conditions and to formulate hypotheses of how dysfunctional network dynamics and hence motor deficits can be remedied.Such information is crucial for developing novel treatment strategies of neurological diseases by uncovering ways of restoring disrupted network activity to nearly normal one.
Toth, T.I., Schmidt, J., Büschges, A., Daun-Gruhn, S. 2013. A neuro-mechanical model of a single leg joint
highlighting the fundamental physiological role of fast and slow muscle fibres of an insect muscle system.
PLOS ONE, DOI: 10.1371/journal.pone.0078247
Toth, T.I., Grabowska, M., Schmidt, J., Büschges, A., Daun-Gruhn, S., 2013. A neuro-mechanical model
explaining the physiological role of fast and slow muscle fibres at stop and start of stepping of an insect leg.
PLOS ONE, DOI: 10.1371/journal.pone.0078246
Knops, S., Toth, T.I., Guschlbauer, C., Gruhn, M., Daun-Gruhn, S., 2012. A neuromechanical model for
curve walking in the stick insect. Journal of Neurophysiology. J Neurophysiol, 109(3), 679-691
Toth, T.I., Knops, S., Daun-Gruhn, S., 2012. A neuro-mechanical model explaining forward and backward
stepping in the stick insect. Journal of Neurophysiology, 107(12), 3267-80.
Daun-Gruhn, S., Toth, T.I., Borgmann, A., 2011. Dominance of local load signals over inter-segmental
effects in a motor system. II. Simulation studies, Biological Cybernetics, 105(5-6), 413-426.
Daun-Gruhn, S. and Toth, T.I., 2011. An inter-segmental network model and its use in elucidating gait- switches in the stick insect, Journal of Computational Neuroscience, 31(1), 43-60.
Daun-Gruhn, S., 2011. A mathematical modeling study of inter-segmental coordination during stick insect
walking, Journal of Computational Neuroscience, 30(2), 255-278.
Daun, S. Rubin, J., Rybak, I., 2009. Control of oscillation periods and phase durations in half-center central
pattern generators: a comparative mechanistic analysis, Journal of Computational Neuroscience, 27(1), 3-