Syntactic parsing is the task of assigning a syntactic structure to a sentence. Recent works have used syntactic embeddings from constituency trees and other word syntactic features to understand how syntax structure is represented in the brain’s language network. However, the effectiveness of dependency parse trees or the relative predictive power of the three syntax parsers is yet unexplored. We explore syntactic structure embeddings obtained from three parsers and use them in an encoding model to predict brain responses. We use a GCN model (SynGCN embeddings) for the dependency parser that accurately encodes the global syntactic information. Constituency trees explain additional variance better than other syntactic parsing methods. This work was done on data related to English stories only. As we do other kinds of models of language processing in various languages, we want to make similar studies for multi-lingual languages