Several studies highlight motivation's crucial role in motor decision-making for maximizing potential rewards. However, how explicit rewards impact the neural dynamics of decision-making processes and their effects on neural organization at the network level, is still unexplored. To address this gap, we analyzed neuronal activity recorded from the premotor cortex (PMd) of three monkeys while performing a modified version of the stop signal task. In this task, the motivation to either act or inhibit action was manipulated using Cues that informed about different reward contexts. We conducted an exploratory analysis to define when at least 40% of neurons encoded the Cue valence(permutation test, cluster-corrected p<0.01). This analysis showed that Cue encoding occurs 500 ms after CueOnset. Therefore, we hypothesized that the dynamics and network organization would vary across different reward contexts following the Cue presentation. We analyzed neuronal information patterns through mutual information and graph theory. Our results show that there are differences in network structure complexity among reward contexts, with a negative correlation observed between complexity and the presence of Hubs(i.e. nodes that are highly connected to other nodes in the network). Specifically, we observed greater network complexity and lower network efficiency in conditions where inhibition was more rewarded than action. These conditions were found to be positively correlated with reaction times and negatively correlated with error rates. Our study shows that the expectation of reward influences the PMd by modulating the network organization and neural dynamics, which are correlated to the process of motor control.