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

Daniel A Llano,Umberto Di Fabrizio,Georgiy Yudintsev,Kevin A Stebbings,Robert V Kenyon,Gang Xiao,Aynaz Taheri,Chihua Ma,Tanya Y Berger-Wolf

doi:10.1162/netn_a_00191

Abstract

Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices by using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.

Highlights

Normal aging is associated with a gradual loss of cognitive function [1,2,3,4,5]
Method development: Two datasets were examined in this study: brain slice imaging data from the auditory cortex during paroxysmal depolarizations and in vivo imaging data from the awake mouse motor cortex (See Figure 1)
We provide proof-of-concept data that dynamic network analysis, community dynamic inference method (CommDy), a network analytical technique initially developed for the inference of communities in dynamic social networks, can quantitatively describe network-level dynamics in brain imaging experiments

Summary

Introduction

Normal aging is associated with a gradual loss of cognitive function [1,2,3,4,5]. The mechanisms responsible for this cognitive loss are not yet known, but given the increasing prevalence of aged individuals worldwide [6], it will be important to more fully understand the patterns of how brain networks fail with aging. Structural changes in the aging brain have been investigated and are characterized by changes in cortical thickness [7, 8], synaptic density [9,10,11] and selective loss of inhibitory interneurons [12, 13]. Less well characterized are functional changes in cortical physiology with aging, such as changes in functional connectivity. Cortical networks appear vulnerable to aging, and demonstrate diminished network-level functional connectivity over the lifespan [20, 21]. Such aging-related disruptions in functional associations correlate with declines in cognitive performance [22]

Methods

Results

Conclusion