We present here the co-expressed protein-protein interactions algorithm. In addition to minimizing correlation-causality imbalance and contextualizing protein-protein interactions to the investigated systems, it combines protein-protein interactions and protein co-expression networks to identify differentially correlated functional modules. To test the algorithm, we processed a set of proteomic profiles from different brain regions of controls and subjects affected by idiopathic Parkinson's disease or carrying a GBA1 mutation. Its robustness was supported by the extraction of functional modules, related to translation and mitochondria, whose involvement in Parkinson's disease pathogenesis is well documented. Furthermore, the selection of hubs and bottlenecks from the weightedprotein-protein interactions networks provided molecular clues consistent with the Parkinson pathophysiology. Of note, like quantification, the algorithm revealed less variations when comparing disease groups than when comparing diseased and controls. However, correlation and quantification results showed low overlap, suggesting the complementarity of these measures. An observation that opens the way to a new investigation strategy that takes into account not only protein expression, but also the level of coordination among proteins that cooperate to perform a given function.