Case-control discrimination through effective brain connectivity

Abstract

Functional and structural connectivity convey different information about the brain. The integration of these different approaches is receiving growing attention from the research community, as it can shed new light on brain functions. This manuscript proposes a constrained autoregressive model with different lag-orders generating an “effective” connectivity matrix which models the structural connectivity integrating the functional activity. Multiple orders are investigated to observe how different time dependencies influence the effective connectivity. The proposed approach alters an initial structural connectivity representation according to functional data, by minimizing the reconstruction error of an autoregressive model constrained by the structural prior. The model is further validated in a case-control experiment, which aims at differentiating healthy subject and young patients affected by autism spectrum disorder.