The Virtual Brain (TVB) is a computational framework with specialized data processing workflows that aims to simulate and model the dynamics of the human brain. Personalized brain network modelling in epilepsy has demonstrated its predictive power [1] and is being systematically evaluated in the prospective large-scale clinical trial EPINOV for epilepsy surgery. Here we aim to adopt and translate the approach for studying Parkinson's disease. In this context, we aim to create a model within the simulated environment of The Virtual Brain (TVB) that reflects how neuromodulators influence the electrical activity patterns in the human brain. To achieve this goal, we have modified a neural mass model based on a mean-field approach to capture dopamine dynamics, associated with Parkinson's disease, characterized by the excessive occurrence of beta-burst activity in the basal ganglia. The model we've selected is called a neural-mass model, based on a mean-field approach [2]. It's built upon the adaptive quadratic integrate-and-fire (aQIF) model of individual neurons. This model allows us to consider different types of synaptic inputs. Now, we're aiming to incorporate dopamine concentration as a variable in our model. We're using the Michaelis-Menten equation to describe how the concentration of this neuromodulator evolves. We're introducing a factor, denoted as $M$, to represent dopamine's influence on the excitatory synaptic conductance. We present here results from whole-brain simulation based on human connectome. We show how the dopamine dynamics impairment in the basal ganglia affects whole-brain dynamics.