Algorithm for the identification of resting state independent networks in fMRI

Abstract

Studies have shown that the brain is constituted by anatomically segregated and functionally specific regions working in synergy as a complex network. In this context, the brain at rest does not passively retrieve environmental information and respond but instead it maintains an active representation modulated by sensory information. Using independent component analysis (ICA) over resting state recordings a discrete set of resting state networks (RSNs) has been found, which proven to be systematically present across individuals and to be modified by the state of consciousness and also in disease. ICA's main drawback is that its output consists of a series of 3D z-score maps where noise and physiological components are randomly mixed. In this work we present a computational method composed by an ICA-based noise filtering preprocessing pipeline and a template-based identification algorithm that combines spatial comparison metrics through a voting system developed to find RSNs in a subject-by-subject basis. To validate it, we use a publicly available dataset consisting of 75 resting state fMRI sessions from 25 participants scanned three different times each one. For most common RSNs the correct candidate won the voting 93% of the times and it was voted at least once in 99%. Then we probe within-subject consistency in detected RSNs by showing augmented correlation in networks from the same subject. Finally, by comparing obtained mean RSNs with the ones from nearly 30,000 participants we show that our method constitutes a personalized-medicine oriented approach to shorten the gap between RSN research and clinical applications.