Biophysical Modeling of Dopaminergic Denervation Landscapes in the Striatum Reveals New Therapeutic Strategy

Abstract

Parkinson's disease (PD) results from a loss of dopaminergic neurons. What triggers the break-down of neuronal signaling, and how this might be compensated, is not understood. The age of onset, progression and symptoms vary between patients, and our understanding of the clinical variability remains incomplete. In this study, we investigate this, by characterizing the dopaminergic landscape in healthy and denervated striatum, using biophysical modelling. Based on currently proposed mechanisms, we model three distinct denervation patterns, and show how this affect the dopaminergic network. Depending on the denervation pattern, we show how local and global differences arise in the activity of striatal neurons. Finally, we use the mathematical formalism to suggest a cellular strategy for maintaining normal dopamine signaling following neuronal denervation. This strategy is characterized by dual enhancement of both the release and uptake capacity of dopamine in the remaining neurons. Overall, our results derive a new conceptual framework for the impaired dopaminergic signaling related to PD and offers testable predictions for future research directions. Significance StatementsParkinson's disease, caused by a loss of dopaminergic neurons, is the second most common neurodegenerative disorder worldwide. Clinically, the age of onset, disease progression, and symptoms are highly variable between patients. Despite this, an understanding of the underlying mechanisms causing this variability is still missing.We here use biophysical modelling and show that the spatial pattern of dopaminergic denervation profoundly affects the anatomy and signaling of the dopaminergic network. We further show that the pattern of denervation has functional consequences for the activity of the downstream projection neurons, critical for the direct and indirect pathways. Our findings are useful in understanding the clinical variability of Parkinson's disease and offers several experientially testable predictions.

Perspective on investigation of neurodegenerative diseases with neurorobotics approaches

“…In order to further the current research landscape, biologically-plausible computational models must continue to be developed including different levels of abstraction from single-purpose neural circuits to single brain areas and expanding to communication between brain regions. These models can be elaborated by including different types of neurons [21], adding gap junctions [78], including neuromodulators such as dopamine [79], and including non-neuronal glial cells [80] in order to increase the fidelity of simulated network architectures and neuronal activity. The challenge faced when developing increasingly complex models is that it is computationally expensive leading to increased energy needs and long simulation times.…”