title: A Quantitative Study of Network Robustness in Resting-State fMRI in Young and Elder Adults.
abstract: Brain connectivity analysis has shown great promise in understanding how aging affects functional connectivity; however, an explanatory framework to study healthy aging in terms of network efficiency is still missing. Here, we study network robustness, i.e., resilience to perturbations, in resting-state functional connectivity networks (rs-fMRI) in young and elder subjects. We apply analytic measures of network communication efficiency in the human brain to investigate the compensatory mechanisms elicited in aging. Specifically, we quantify the effect of "lesioning" (node canceling) of either single regions of interest (ROI) or whole networks on global connectivity metrics (i.e., efficiency). We find that young individuals are more resilient than old ones to random "lesioning" of brain areas; global network efficiency is over 3 times lower in older subjects relative to younger subjects. On the other hand, the "lesioning" of central and limbic structures in young subjects yield a larger efficiency loss than in older individuals. Overall, our study shows a more idiosyncratic response to specific brain network "lesioning" in elder compared to young subjects, and that young adults are more resilient to random deletion of single nodes compared to old adults.
authors: Department of Neuroscience and Mental Health, The Hospital for Sick Children, University of Toronto , Toronto, ON , Canada.
authors: Jaime
authors: J
authors: Gomez-Ramirez
authors: Key Laboratory of Adolescent Cyberpsychology and Behavior (CCNU), Ministry of Education, Wuhan, China; School of Psychology, Central China Normal University, Wuhan, China.
authors: Yujie
authors: Y
authors: Li
authors: Biomedical Engineering Laboratory, Okayama University , Okayama , Japan.
authors: Qiong
authors: Q
authors: Wu
authors: Biomedical Engineering Laboratory, Okayama University, Okayama, Japan; Intelligent Robotics Institute, School of Mechatronics Engineering, Beijing Institute of Technology, Beijing, China.
authors: Jinglong
authors: J
authors: Wu
keywords: network degeneration hypothesis; network efficiency; network robustness; normal aging; resting-state fMRI