Increasing Serotonin to Reduce Parkinsonian Tremor

Abstract

While current dopamine-based drugs seem to be effective for most Parkinson's disease (PD) motor dysfunctions, they produce variable responsiveness for resting tremor. This lack of consistency could be explained by considering recent evidence suggesting that PD resting tremor can be divided into different partially overlapping phenotypes based on the dopamine response. These phenotypes may be associated with different pathophysiological mechanisms produced by a cortical-subcortical network involving even non-dopaminergic areas traditionally not directly related to PD. In this study, we propose a bio-constrained computational model to study the neural mechanisms underlying a possible type of PD tremor: the one mainly involving the serotoninergic system. The simulations run with the model demonstrate that a physiological serotonin increase can partially recover dopamine levels at the early stages of the disease before the manifestation of overt tremor. This result suggests that monitoring serotonin concentration changes could be critical for early diagnosis. The simulations also show the effectiveness of a new pharmacological treatment for tremor that acts on serotonin to recover dopamine levels. This latter result has been validated by reproducing existing data collected with human patients.

Keywords: Parkinson's disease early diagnosis; computational neuroscience; different parkinsonian tremor types; differential equations brain modeling; patient digital twin; serotonin and dopamine interplay; system neuroscience; tremor alternative treatment.

Figure 1

Model architecture including primary motor cortex (M1), thalamus (Thal), dorsal raphe nucleus (DRN), substantia nigra pars compacta (SNc); basal ganglia direct pathway (DP); basal ganglia indirect pathway (IP). The connections linking different components can be excitatory (arrows) or inhibitory (lines ending with dot). The dashed lines indicate the effects of serotonergic projections from DRN to SNc, DP, and IP. Through these connections, serotonin modulates the dopamine release in the system.

Figure 2

Representation of the dynamic arm used to test the model motor behavior. The task space is a horizontal plane in which the arm is free to move according to the inputs received from M1.

Figure 3

Relationship between activities of model components and changes in DA and 5-HT concentrations. The dotted vertical line represents the timing of the lesion in the dopaminergic circuits (τ_SNc + 25%, α_IP + 11%). The four graphs represent: (A) the concentration levels of 5-HT (brown line) and DA (pink line); (B) the spike frequency of DRN (light red line) and SNc (light blue line); (C) the spike frequency of Thal (orange line) and M1 (purple line); (D) the spike frequency of IP (light green line) and DP (dark green line).

Figure 4

Dopamine concentration vs. 5-HT release impairments. DAMAGE1 = τ_DRN + 40%; DAMAGE2 = τ_DRN + 60%; DAMAGE3 = τ_DRN + 80%.

Figure 5

Oscillation induced by 5-HT release impairments. DAMAGE1 = τ_DRN + 40%; DAMAGE2 = τ_DRN + 60%; DAMAGE3 = τ_DRN + 80%.

Figure 6

Relationship between activities of model components and changes in DA and 5-HT concentrations after SNc damage and selective serotonin inhibitors (SSRIs) simulated treatment. The first dotted vertical line (black) represents the timing of the lesion in the dopaminergic circuits (τ_SNc + 70%, α_IP + 333%). The other three dotted vertical lines (gray scale) indicate the beginning of different serotonin-based drug (SSRIs) treatment periods with different doses of the treatment (τ_5−HT − 20%, τ_5−HT − 30%, τ_5−HT − 40%). Color code and graphs displacement as Figure 3.

Figure 7

Oscillation amplitude of the bio-mimetic arm before and after the DA impairment (τ_SNc + 70%, α_IP + 333%) and following SSRIs treatment (τ_5−HT − 20%, τ_5−HT − 30%, τ_5−HT − 40%).