Network modularity reveals context and state-dependent reorganization of time-varying functional connectivity in single-cell resolved neural activity recordings
An important goal of neuroscience is to understand how biological neural networks organize activity at multiple scales to enable complex information processing and behavioral output. To address this challenge, large-scale neural activity datasets with increased resolution and wider coverage have become more prevalent across many model systems. However, bridging the gap in scale between changes in pairwise functional connectivity between neurons and changes in brain-wide organization of activity remains a key challenge. In this work, we demonstrate application of modularity-based community detection and network modularity to single-cell resolved recordings, for the first time, as a method to summarize complex changes in time-varying functional connectivity, facilitating comparisons across multiple time windows, recordings, and conditions. We apply these methods to both single-cell resolved multi-cell and whole-brain activity recordings. In the multi-cell recordings, we find that food odor changes functional connectivity between existing network modules in a C. elegans locomotory interneuron network, rather than reorganizing them. In spontaneous whole-brain activity, we identify several key hub neurons and combinations that significantly destabilize module assignments when silenced. Together, these results demonstrate community detection and modularity as a method for detecting context and network state-dependent changes in functional connectivity at the intermediate scale of network modules in single-cell resolved neural activity. Results from these analyses facilitate future investigation of mechanisms that mediate organization of neural activity at intermediate scales.