Resting-state functional magnetic resonance imaging (rsfMRI) is increasingly used to unravel the functional neuronal networks in health and disease. In particular, this technique of simultaneously probing the whole brain has found high interest in monitoring brain wide effects of cerebral disease and in evaluating therapeutic strategies. Such studies, applied in preclinical experimental mouse models, often require long-term observations. In particular during regeneration studies, easily several months of continuous monitoring are required to detect functional improvements. These long periods of following the functional deficits during disease evolution as well as the functional recoveries during therapeutic interventions represent a substantial fraction of the life span of the experimental animals. We have therefore aimed to decipher the role of healthy aging alone for changes in functional neuronal networks in mice, from developmental adolescence via adulthood to progressing aging. For this purpose, four different groups of C57Bl6 mice of varying age between 2 and 13 months were studied twice with 4 weeks separation using resting state fMRI at 9.4T. Dedicated data analysis including both Independent Component Analysis (ICA) followed by seed-based connectivity matrix compilation resulted in an inverse U-shape curve of functional connectivity (FC) strength in both the sensorimotor and default mode network (DMN). This inverse U-shape pattern presented a distinct maximum of FC strength at 8-9 months of age, followed by a continuous decrease during later aging phases. At progressed aging at 12-13 months, the reduction of connectivity strength varied between 25% and 70% with most connectivities showing a reduction in strength by approximately 50%. We recommend that these substantial age-dependent changes in FC strength must be considered in future longitudinal studies to discriminate focused disease-based functional deficits and therapy-related functional improvements from underlying independent age effects.
Keywords: aging; brain connectivity; dependence on aging; functional neuronal networks; mice; resting state fMRI; seed correlation analysis.
Copyright © 2019 Egimendia, Minassian, Diedenhofen, Wiedermann, Ramos-Cabrer and Hoehn.