A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse

Abstract

29Network analysis of large-scale neuroimaging data has proven to be a particularly challenging 30 computational problem. In this study, we adapt a novel analytical tool, known as the community 31 dynamic inference method (CommDy), which was inspired by social network theory, for the 32 study of brain imaging data from an aging mouse model. CommDy has been successfully used 33 in other domains in biology; this report represents its first use in neuroscience. We used 34CommDy to investigate aging-related changes in network parameters in the auditory and motor 35 cortices using flavoprotein autofluorescence imaging in brain slices and in vivo. Analysis of 36 spontaneous activations in the auditory cortex of slices taken from young and aged animals 37 demonstrated that cortical networks in aged brains were highly fragmented compared to 38 networks observed in young animals. Specifically, the degree of connectivity of each activated 39 node in the aged brains was significantly lower than those seen in the young brain, and 40 multivariate analyses of all derived network metrics showed distinct clusters of these metrics in 41 young vs. aged brains. CommDy network metrics were then used to build a random-forests 42 classifier based on NMDA-receptor blockade data, which successfully recapitulated the aging 43findings, suggesting that the excitatory synaptic substructure of the auditory cortex may be 44 altered during aging. A similar aging-related decline in network connectivity was also observed 45 in spontaneous activity obtained from the awake motor cortex, suggesting that the findings in 46 the auditory cortex are reflections of general mechanisms that occur during aging. Therefore, 47CommDy therefore provides a new dynamic network analytical tool to study the brain and 48 provides links between network-level and synaptic-level dysfunction in the aging brain. 49 50 Introduction 53Normal aging is associated with a gradual loss of cognitive function [1][2][3][4][5]. The mechanisms 54 responsible for this cognitive loss are not yet known, but given the increasing prevalence of 55 aged individuals worldwide [6], it will be important to more fully understand the patterns of how 56 brain networks fail with aging. Structural changes in the aging brain have been investigated and 57 are characterized by changes in cortical thickness [7, 8], synaptic density [9][10][11] and selective 58 loss of inhibitory interneurons [12, 13]. Less well characterized are functional changes in cortical 59 physiology with aging, such as changes in functional connectivity. Functional connectivity 60 between brain regions can change rapidly over time [14, 15], is not easily predictable from 61 anatomical connectivity [16], and is altered in several different pathological states [17][18][19]. In 62 addition, cortical networks appear particularly vulnerable to aging, and demonstrate diminished 63 network-level functional connectivity over the lifespan [20, 21]. Furthermore, such aging-related 64 disruptions in functional associations correlate with...