Author Summary In this work, we introduce a novel technique (the community dynamic interference method, CommDy) which is based on social network theory, to analyze neural data. We use CommDy to analyze imaging data from the cerebral cortex of awake mice and mouse brain slices. In each case, data were obtained from young and aged mice. Using CommDy, we found that in both types of data, aging is associated with a fragmentation of cortical circuitry. Specifically, the strength of intracortical connectivity diminishes with aging, leaving fragmented cortical networks. Thus, CommDy is a novel tool now available to the neuroscience community, and has revealed aging-related pathology of cortical networks. 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.