Refined measure of functional connectomes for improved identifiability and prediction.

ABSTRACT: Brain functional connectome analysis is commonly based on population-wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population-wise contributions from connectivity patterns

SUBMITTER: Cai B 

PROVIDER: S-EPMC6865523 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature