• Basal Ganglia
• beta bursts.
• correlation
• cortex
• DBS
• Parkinson’s Disease

Fluctuations in the Beta frequency range (13 – 35Hz) in cortical and basal ganglia (BG) networks are known to be highly synchronized in Parkinson’s disease (PD) patients. However, how these pathological oscillations are generated is still unclear. One popular candidate explanation is the rate-based model. According to this hypothesis, disruption of physiological behavior is due to the abnormal firing rate of neurons in the BG nuclei. Aberrant behavior follows dopamine depletion in the substantia nigra pars compacta and results in excessive feedback to the cortex, via modulation of thalamic activity.
Although the rate-based model accounts for several basal ganglia functions, it cannot explain the whole spectrum of motor and non-motor symptoms that characterizes PD, leading to the need of considering other features, such as neuronal firing patterns and cortico-basal ganglia synchrony. Recently, neurophysiological recordings demonstrated that Beta oscillatory activity occurs in burst. The bursting behavior may reflect changes in synaptic properties or inputs of circuital neurons, indeed patterned activity in the cortical and subcortical circuits seem to be phase locked.
Here, we address the problem of reproducing the pathological over-synchronization between cortex and BG circuit using a spiking neural network that includes plausible synaptic dynamics, connectivity patterns and neuronal behavior. Results have been obtained considering parkinsonian synaptic characteristics and administering several levels of correlation by way of emulated cortical input to striatal medium spiny neurons. Our model predicts that emergence of Beta-band hyperactivity is possible without changing the population firing rate. Finally, we were able to simulate a basic implementation of Deep Brain Stimulation (DBS), the key surgical method to attenuate PD symptomatology and show that, in some conditions, such stimulation is able to suppress over-synchronization.
This paves the way toward in silico testing of DBS parameters that could be used to determine optimal parameters of stimulation offline rather than during surgical implant.