A novel thresholding method for the analysis of functional connectivity networks of the brain

Functional connectivity is a key concept in the analysis of brain function and it has been successfully applied to the study of the brain and changes caused by diseases such as Alzheimer´s Disease and Schizophrenia but also Asperger´s syndrome. Thresholding is an essential operation in extracting functional connectivity networks and contributes to the determination of their structural characteristics. Several approaches to estimating the threshold value have been applied to functional modality datasets so far, but the choice of threshold value is largely adhoc. In this paper we propose a novel method for thresholding functional connectivity matrices that is universal, iterative and subject specific and evaluate its performance through simulations.