Abstract
Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system’s topological organization and to better understand its function. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work indicates a framework for studying spontaneous activity measured by two-photon calcium imaging using computational methods and graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.
Highlights
Distributed and often redundant coding is a hallmark of neural systems [1], providing robustness to single-neuron variability [2] and supporting complexity in the system’s potential behavioral repertoire [3]
We sought to partially address this gap in knowledge by using recently developed techniques in network science to examine the network architecture of correlations in spontaneous activity in mouse auditory cortex as measured by two-photon microscopy and calcium imaging over the course of several weeks
We found significant temporal rearrangement of modular architecture, as Stability of spontaneous, correlated activity in mouse auditory cortex indicated by the fact that the similarity in modules decreased monotonically as a function of the time interval between recording sessions, even when only considering those units that were present in both sessions
Summary
Distributed and often redundant coding is a hallmark of neural systems [1], providing robustness to single-neuron variability [2] and supporting complexity in the system’s potential behavioral repertoire [3]. An undirected binary graph is composed of nodes, which represent the units of the system, and edges, which link pairs of nodes according to some physical connection, functional relation, or shared feature [20]. This simplest version of a network can be expanded to include weights on edges, weights on nodes, dynamics on edges, dynamics on nodes, or multiple types of nodes or edges forming a multilayer or multiplex structure [21, 22]. The network modeling approach is flexible in the sense that its components can be redefined at different spatial scales, and is applicable to cellular data at the microscale as it is to regional data at the large scale [24]
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