Stability of spontaneous, correlated activity in mouse auditory cortex
arXiv:1812.03796 · doi:10.1371/journal.pcbi.1007360
Abstract
Neural systems can be modeled as networks of functionally connected neural elements. 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. While the network-based approach is common in the analysis of large-scale neural systems probed by non-invasive neuroimaging, few studies have used network science to study the organization of networks reconstructed at the cellular level, and thus many very basic and fundamental questions remain unanswered. 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 provides a careful methodological blueprint for future studies of spontaneous activity measured by two-photon calcium imaging using cutting-edge computational methods and machine learning algorithms informed by explicit graphical models from network science. The methods are 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.
arXiv comment15 pages, 3 figures
References in corpus (24)
- Finding and evaluating community structure in networks
- Community detection in graphs
- Multilayer Networks
- Temporal Networks
- Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
- Statistical Mechanics of Community Detection
- Dynamic reconfiguration of human brain networks during learning
- Community detection in networks: A user guide
- Controllability of Brain Networks
- Weight-conserving characterization of complex functional brain networks
- Hierarchical modularity in human brain functional networks
- Robust Detection of Dynamic Community Structure in Networks
- The multilayer connectome of Caenorhabditis elegans
- Model-free reconstruction of neuronal network connectivity from calcium imaging signals
- Multilayer motif analysis of brain networks
- Multilayer Brain Networks
- Diversity of meso-scale architecture in human and non-human connectomes
- Topological Principles of Control in Dynamical Network Systems
- Topological analysis of the connectome of digital reconstructions of neural microcircuits
- The evolutionary origins of hierarchy
- Revealing cell assemblies at multiple levels of granularity
- Modeling correlated noise is necessary to decode uncertainty
- Two's company, three (or more) is a simplex: Algebraic-topological tools for understanding higher-order structure in neural data
- Functional brain modules reconfigure at multiple scales across the human lifespan