Deep brain stimulation in the subthalamic nucleus for Parkinson's disease can restore dynamics of striatal networks

Abstract

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is highly effective in alleviating movement disability in patients with Parkinson’s disease (PD). However, its therapeutic mechanism of action is unknown. The healthy striatum exhibits rich dynamics resulting from an interaction of beta, gamma, and theta oscillations. These rhythms are essential to selection and execution of motor programs, and their loss or exaggeration due to dopamine (DA) depletion in PD is a major source of behavioral deficits. Restoring the natural rhythms may then be instrumental in the therapeutic action of DBS. We develop a biophysical networked model of a BG pathway to study how abnormal beta oscillations can emerge throughout the BG in PD and how DBS can restore normal beta, gamma, and theta striatal rhythms. Our model incorporates STN projections to the striatum, long known but understudied, found to preferentially target fast-spiking interneurons (FSI). We find that DBS in STN can normalize striatal medium spiny neuron activity by recruiting FSI dynamics and restoring the inhibitory potency of FSIs observed in normal conditions. We also find that DBS allows the reexpression of gamma and theta rhythms, thought to be dependent on high DA levels and thus lost in PD, through cortical noise control. Our study highlights that DBS effects can go beyond regularizing BG output dynamics to restoring normal internal BG dynamics and the ability to regulate them. It also suggests how gamma and theta oscillations can be leveraged to supplement DBS treatment and enhance its effectiveness.

Keywords: basal ganglia; beta, gamma, and theta rhythms; correlated noise; fast-spiking interneurons; medium spiny neurons.

Fig. 1.

Population dynamics in baseline condition. (A) Schematic illustrating the network structure of the biophysical neuronal model, composed of MSN, FSI, GPe, and STN neurons. (B) Raster plots showing spiking activity of MSN, FSI, GPe, and STN neurons, all in baseline condition (MSN firing rate: mean ± SD = $1.21\pm 0.08 \text{spk}·{\text{s}}^{-1}$, n = 25 simulations). (C) Graphs showing the average (blue) and SD (light blue) of the spectra of the MSN, FSI, GPe, and STN population activity, all in baseline condition (n = 25 simulations).

Fig. 2.

Population dynamics in PD. (A) Schematic illustrating the parametric changes in the biophysical model in PD from parameters in baseline condition. The loss of DA is modeled in multiple ways: 1) an increase of background excitation onto the MSNs expressing D2 receptors (45), 2) a decrease in background excitation for FSIs (51), and 3) changes in effective connectivity among the FSIs [decreased electrical conductance for the gap junctions (52) and increase in their interneuronal GABAa maximal conductance (51)]. The increase in cholinergic tone 1) decreases the maximal MSN M-current conductance (via ACh action on M1 receptors) (45) and 2) decreases the inhibitory maximal conductance from FSIs to MSNs (53). (B) Raster plots showing spiking activity of MSN, FSI, GPe, and STN neurons, all in PD (MSN firing rate: mean ± SD = $4.85\pm 0.13 \text{spk}·{\text{s}}^{-1}$, n = 25 simulations). (C) Graphs showing the average (blue) and SD (light blue) of the spectra of the MSN, FSI, GPe, and STN population activity, all in PD (n = 25 simulations).

Fig. 3.

Population dynamics during DBS in PD. (A) Schematic illustrating the parametric changes in the biophysical model in PD during DBS from parameters in baseline condition. (B) Raster plots showing spiking activity of MSN and FSI neurons, all in PD during DBS (MSN firing rate: mean ± SD = $1.31\pm 0.07 \text{spk}·{\text{s}}^{-1}$, n = 25 simulations). (C) Graphs showing the average (blue) and SD (light blue) of the spectra of the MSN and FSI population activity, all in PD during DBS. (D) Graph showing the average FSI gamma oscillation frequency (bars representing SD) as a function of DBS frequency (10 simulations per simulated frequency). (E) Graph similar to D showing the average FSI firing rate as a function of DBS frequency. (F) Graph similar to D showing the average MSN firing rate as a function of DBS frequency.

Fig. 4.

DBS can restore the DA functionality lost during PD. (A) Raster plots showing spiking activity of FSI neurons, in normal conditions with high level of DA. (B) Graph showing the average (blue) and SD (light blue) of the spectrum of FSI population activity, in normal conditions with high level of DA. (C and D) Graphs displaying population activity of FSI neurons, as in A and B, but for PD with DBS and a synchronized noise regime. (E) Raster plots showing spiking activity of FSI neurons, in PD within a synchronized noise regime. (F) Graph showing the average (blue) and SD (light blue) of the spectrum of FSI population activity, in PD within a correlated noise regime. (G and H) Graphs displaying population activity of FSI neurons, as in E and F, but for PD within a correlated noise regime and added excitation for the FSIs to drive them individually at a theta/gamma oscillation. (I and J) Graphs displaying population activity of MSN neurons, as in A and B, for normal conditions with high level of dopamine. The raster plots show D1 and D2 MSN activity separately. Traces of raw (black) and beta-band filtered (orange) population activities of MSNs are additionally shown. (K and L) Graphs displaying population activity of MSN neurons, as in I and J, but for PD with DBS and a correlated noise regime. (M) Graph showing the average FSI gamma oscillation frequency (bars representing SD) as a function of DBS frequency under correlated noise conditions (10 simulations per simulated frequency). (N) Graph similar to M showing the average FSI theta oscillation frequency as a function of DBS frequency. All average spectra are derived from 25 simulations in each condition.