Effective brain connectivity through a constrained autoregressive model

Abstract

Integration of functional and structural brain connectivity is a topic receiving growing attention in the research community. Their fusion can,in fact,shed new light on brain functions. Targeting this issue,the manuscript proposes a constrained autoregressive model allowing to generate an “effective” connectivity matrix that model the structural connectivity integrating the functional activity. In practice,an initial structural connectivity representation is altered according to functional data,by minimizing the reconstruction error of an autoregressive model constrained by the structural prior. The proposed model has been tested in a community detection framework,where the brain is partitioned using the effective network across multiple subjects. Results showed that using the effective connectivity the resulting clusters better describe the functional interactions of different regions while maintaining the structural organization.