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Paper   IPM / Biological Sciences / 17707
School of Biological Sciences
  Title:   Dynamics of parkinsonian oscillations mediated by transmission delays in a mean-field model of the basal ganglia
1.  Atefeh Asadi
2.  Mojtaba Madadi Asl
3.  Alireza Valizadeh
4.  MatjaÃ?¾ Perc
  Status:   Published
  Journal: Frontiers in Cellular Neuroscience
  Vol.:  18
  Year:  2024
  Supported by:  IPM
Introduction: Neural interactions in the brain are affected by transmission delays which may critically alter signal propagation across different brain regions in both normal and pathological conditions. The effect of interaction delays on the dynamics of the generic neural networks has been extensively studied by theoretical and computational models. However, the role of transmission delays in the development of pathological oscillatory dynamics in the basal ganglia (BG) in Parkinson's disease (PD) is overlooked. Methods: Here, we investigate the effect of transmission delays on the discharge rate and oscillatory power of the BG networks in control (normal) and PD states by using a Wilson-Cowan (WC) mean-field firing rate model. We also explore how transmission delays affect the response of the BG to cortical stimuli in control and PD conditions. Results: Our results show that the BG oscillatory response to cortical stimulation in control condition is robust against the changes in the inter-population delays and merely depends on the phase of stimulation with respect to cortical activity. In PD condition, however, transmission delays crucially contribute to the emergence of abnormal alpha and beta band oscillations, suggesting that delays play an important role in abnormal rhythmogenesis in the parkinsonian BG. Discussion: Our findings indicate that in addition to the strength of connections within and between the BG nuclei, oscillatory dynamics of the parkinsonian BG may also be influenced by inter-population transmission delays. Moreover, phase-specificity of the BG response to cortical stimulation may provide further insight into the potential role of delays in the computational optimization of phase-specific brain stimulation therapies.

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