Correspondence of large-scale functional brain network decline across aging mice and humans

Abstract

Human aging is marked by progressive reorganization of large-scale functional brain networks; these brain network changes have been linked to cognitive decline and disease vulnerability. Conversely, while mice have served as powerful models for understanding the molecular and cellular changes that occur over the lifespan, an absence of precise characterization of age-related changes in large-scale functional brain network organization has limited cross-species translational insights. Here, using densely sampled resting-state functional MRI data acquired cross-sectionally and longitudinally in awake mice over a broad range of adulthood (n = 82; 3 to 20 mo), we describe organizational features and age-related alterations of the mouse’s functional connectome. Mouse resting-state functional connectivity recapitulates known functional circuits, demonstrating the organizational validity of these signals. Graph theoretic analysis applied to functional connectivity reveals that mice exhibit modular architectures of functional brain network organization and that increasing age is associated with decreasing system segregation, indicative of network dedifferentiation analogous to observations in humans. Notably, mouse resting-state brain networks are more segregated than those of humans [determined using data from the Human Connectome Project and its developmental- and aging-counterparts (n = 1,179; 18 to 90 y)], attributable to mice exhibiting a diminished contribution of long-range functional relationships that integrate distributed systems. Mice also exhibit slower rates of age-related decline in brain network organization relative to humans, highlighting important species differences in functional brain network organization and trajectories of brain network aging. These findings establish a model of large-scale functional brain network aging in mice and provide a translational bridge across species and spatial scales of analysis.