A computational modelling approach to investigate different targets in deep brain stimulation for Parkinson’s disease

Abstract

We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson's disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). A cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211-235, 2004), was developed. The original Rubin and Terman model was able to reproduce both the physiological and pathological activities of STN, GPi, GPe and thalamo-cortical (TC) relay cells. In the present study, we introduced a representation of the direct pathway of the BGS, allowing a more complete framework to simulate DBS and to interpret its network effects in the BGS. Our results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively. Our results are consistent with the experimental and the clinical evidences on the network effects of DBS.

The effects of DBS patterns on basal ganglia activity and thalamic relay

“…The relay reliability increases or decreases based on the action selection in the striatum. Direct, indirect and hyperdirect pathways in the BG work together to modulate thalamic relay via the GPi (Pirini et al 2009). In PD, the balance between direct, indirect and hyper-direct pathway is disturbed, which disrupts GPi activity and hence compromises thalamic relay.…”

Do gap junctions regulate synchrony in the Parkinsonian basal ganglia?

“…In contrast, entrainment of thalamic cells by basal ganglia input seems to be common in birds . Computational models, however, have assumed that GPi output has prominent cell-to-cell effects on thalamus, at least under parkinsonian conditions and in particular after the occurrence of bursts in GPi [Rubin and Terman, 2004, Guo et al, 2008, Guo and Rubin, 2011, Reitsma et al, 2011, Cagnan et al, 2009, Pirini et al, 2009, Meijer et al, 2011, Cleary et al, 2013. Although we found high LFP correlations between GPi and VLa, it is not clear which physiological properties are actually reflected in these correlations, and whether they describe a transmission of information.…”

Neural network dynamics in Parkinson's disease

“…By using a structured sparse architecture we mimic a basal ganglia input to the PPN cell that represents a normal (uncorrelated spiking) or PD (correlated rhythmic activity) situation, see top and middle panels of Figure 3.2. Following the approach of Pirini et al [168], Rubin and Terman [181], only two parameters are used to switch between the normal and the PD state: the indirect striatal current to GPe cells (I striatum→GPe ) and the intra-GPe inhibitory synaptic conductance (g GPe→GPe ).…”