A dynamical model for the basal ganglia-thalamo-cortical oscillatory activity and its implications in Parkinson's disease

Navarro-Lopez E. M., Celikok U., Şengör N. S.

COGNITIVE NEURODYNAMICS, vol.15, no.4, pp.693-720, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 15 Issue: 4
  • Publication Date: 2021
  • Doi Number: 10.1007/s11571-020-09653-y
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, Psycinfo
  • Page Numbers: pp.693-720
  • Keywords: Adaptive dynamical evolution, Basal ganglia, Brain oscillations, Collective behaviour, Computational and mathematical models, Neuroplasticity, Parkinson’s disease, Self-organisation, Spiking neural networks
  • Istanbul Technical University Affiliated: Yes


We propose to investigate brain electrophysiological alterations associated with Parkinson's disease through a novel adaptive dynamical model of the network of the basal ganglia, the cortex and the thalamus. The model uniquely unifies the influence of dopamine in the regulation of the activity of all basal ganglia nuclei, the self-organised neuronal interdependent activity of basal ganglia-thalamo-cortical circuits and the generation of subcortical background oscillations. Variations in the amount of dopamine produced in the neurons of the substantia nigra pars compacta are key both in the onset of Parkinson's disease and in the basal ganglia action selection. We model these dopamine-induced relationships, and Parkinsonian states are interpreted as spontaneous emergent behaviours associated with different rhythms of oscillatory activity patterns of the basal ganglia-thalamo-cortical network. These results are significant because: (1) the neural populations are built upon single-neuron models that have been robustly designed to have eletrophysiologically-realistic responses, and (2) our model distinctively links changes in the oscillatory activity in subcortical structures, dopamine levels in the basal ganglia and pathological synchronisation neuronal patterns compatible with Parkinsonian states, this still remains an open problem and is crucial to better understand the progression of the disease.