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Computational Neurology

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.

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.

 

computational neurology

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). Numerical simulations of 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.


Selected Publications:

  • Rosjat, N., Wang, B.A., Liu, L., Fink, G.R., & Daun, S. (2020). Stimulus transformation into motor action: Dynamic graph analysis reveals aging effects on brain network communication. Human Brain Mapping, doi: 10.1002/hbm.25313.
  • Viswanathan, S., Abdollahi, R.O., Wang, B.A., Grefkes, C., Fink, G.R., & Daun, S. (2020). A response-locking protocol to boost sensitivity for fMRI-based neurochronometry. Human Brain Mapping, 41(12):3420-3438.
  • Yeldesbay, A. & Daun, S. (2020). Intra- and intersegmental neural network architectures determining rhythmic motor activity in insect locomotion. Communications in Nonlinear Science and Numerical Simulation, 82:105078.
  • Yeldesbay, A., Fink, G.R., & Daun, S. (2019). Reconstruction of effective connectivity in the case of asymmetric phase distributions. Journal of Neuroscience Methods, 31:94–107.
  • Rosjat, N., Liu, L., Wang, B.A., Popovych, S., Toth, T.I., Viswanathan, S., Grefkes, C., Fink, G.R., & Daun, S. (2018). Aging-associated changes of movement-related functional connectivity in the human brain. Neuropsychologia, 117:520-529.
  • Viswanathan, S., Wang, B.A., Abdollahi, R.O., Daun, S., Grefkes, G., & Fink, G.R. (2018). Freely chosen and instructed actions are terminated by different neural mechanisms revealed by kinematics-informed EEG. Neuroimage, 188:26-42.
  • Yeldesbay, A., Toth, T.I., & Daun, S. (2018). The role of phase shifts of sensory inputs in walking revealed by means of phase reduction. Journal of Computational Neuroscience, 44(1):313-339.
  • Liu, L., Rosjat, N., Popovych, S., Wang, B.A., Yeldesbay, A., Toth, T.I., Viswanathan, S., Grefkes, C., Fink, G.R., & Daun, S. (2017). Age-related changes in oscillatory power affect motor action. PLoS One, 12(11):e0187911.
  • Wang, B.A., Viswanathan, S., Abdollahi, R.O., Rosjat, N., Popovych, S., Daun, S., Grefkes, C., & Fink, G.R. (2017). Frequency-specific modulation of connectivity in the ipsilateral sensorimotor cortex by different forms of movement initiation. NeuroImage, 159:248-260.
  • Popovych, S., Rosjat, N., Toth, T.I., Wang, B.A., Liu, L., Abdollahi, R.O., Viswanathan, S., Grefkes, C., Fink, G.R., & Daun, S. (2016). Movement-related phase locking in the delta-theta frequency band. NeuroImage, 139:439-449.
  • Rosjat, N., Popovych, S., & Daun-Gruhn, S. (2014). A mathematical model of dysfunction of the thalamo-cortical loop in schizophrenia. Theoretical Biology and Medical Modelling, 11(1): 45.
  • 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):3-36.