Comparison between a pure functional connectivity and a mixed functional-topological model in functional connectivity. An application on parahippocampal gyrus-anterior division data

Abstract

The investigation of brain functional connectivity (FC) by means of rsfMRI techniques is on-going challenge in the neuroimaging field. In the present investigation we compare two mathematical models for the computation of Resting-State Networks: The first one is based on the pure analysis of time courses (pure Functional Connectivity, pFC), the second one, the FD model, includes both the time courses, the anatomical information between brain nodes and topological metrics of the network which models the brain. The two approaches have been evaluated by comparing the maximal weights of the links representing the neural network obtained by applying the two models to a dataset of rsfMRI images from 133 healthy subjects. FC analyses were performed using the two methods by focusing on the functional connections between the anterior part of the parahippocampal gyrus (PHGA), a core area for cognitive and emotive processes, and the rest of the brain. Based on the literature, we expect to collect evidences of the role of PHGA as connector hub between the temporal pole and the nodes of the Default Model Network. As expected, the majority of the significant links involved were highlighted by both methods. However, the FD model highlighted a greater number of links compared with pFC. These links are in line with the known neuroanatomy. Hence, our results invite to consider the FD approach as an effective approach of analysis, since due to its characteristics it could provide a more complete description of the brain network.