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

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 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.

V‐NeuroStack: Open‐source 3D time stack software for identifying patterns in neuronal data

“…along the Z axis. The community datasets were generated using the CommDy Algorithm (Llano et al, 2021;. Three-dimensional point clouds may also be generated for raw and the first derivative of intensities (Figure 8).…”

“…Data extraction comprised five steps: capturing images from tissue preparation, composed image generation, labeling and outlining of ROIs (individual somata), extraction of centroid coordinates from the ROI, and activity time course extraction (Tanzi, 2017). The time series of spontaneous calcium signals were then processed using either a K ‐means clustering or a dynamic community algorithm discussed in (Llano et al, 2021; Tantipathananandh & Berger‐Wolf, 2011) which identifies dynamically organized clusters of neurons. These clusters are then visualized using the V‐NeuroStack application.…”

“…We then create a network with neurons as nodes and the correlation coefficient between them as edges. This network is then fed to a K ‐means clustering or CommDy algorithm (Llano et al, 2021; Tantipathananandh & Berger‐Wolf, 2011) for the network analysis. This is followed by visualizing the networks using V‐NeuroStack.…”

“…K ‐means clustering, which has been broadly used in the biology to identify networks of interacting biological entities, was applied to the datasets. In addition, a newer algorithm, known as community dynamic analysis (Llano et al, 2021; Tantipathananandh & Berger‐Wolf, 2011) was used to infer dynamic communities. These approaches generated clusters that were then used as inputs to our visualization tool for exploration and analysis.…”

“…Highlights of V‐NeuroStack Application applied to in vitro data. (a) Shows V‐NeuroStack with dataset created using CommDy algorithm (Llano et al, 2021; Tantipathananandh & Berger‐Wolf, 2011) with window size 100 frames and correlation coefficient threshold of 0.9. (b1–b3) Show detailed views of timesteps in A with columnar clusters.…”