Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type

To improve 'bench-to-bedside' translation, it is integral that knowledge flows bidirectionally -from animal mod-els to humans, and vice versa. This requires common analytical frameworks, as well as open software and data sharing practices. We share a new pipeline (and test dataset) for the preprocessing of wide-field optical fluo-rescence imaging data -an emerging mode applicable in animal models -as well as results from a functional connectivity and graph theory analysis inspired by recent work in the human neuroimaging field. The approach is demonstrated using a dataset comprised of two test-cases: (1) data from animals imaged during awake and anesthetized conditions with excitatory neurons labeled, and (2) data from awake animals with different genet-ically encoded fluorescent labels that target either excitatory neurons or inhibitory interneuron subtypes. Both seed-based connectivity and graph theory measures (global efficiency, transitivity, modularity, and characteristic path-length) are shown to be useful in quantifying differences between wakefulness states and cell populations. Wakefulness state and cell type show widespread effects on canonical network connectivity with variable fre-quency band dependence. Differences between excitatory neurons and inhibitory interneurons are observed, with somatostatin expressing inhibitory interneurons emerging as notably dissimilar from parvalbumin and va-soactive polypeptide expressing cells. In sum, we demonstrate that our pipeline can be used to examine brain state and cell-type differences in mesoscale imaging data, aiding translational neuroscience efforts. In line with open science practices, we freely release the pipeline and data to encourage other efforts in the community.

Automated summary

To improve the translation from the laboratory to the clinic, it is essential to have bidirectional knowledge flow between animal models and humans. This study presents a new pipeline for the preprocessing of wide-field optical fluorescence imaging data, along with functional connectivity and graph theory analyses inspired by recent work in human neuroimaging. The results demonstrate the usefulness of these approaches in quantifying differences between wakefulness states and cell populations, aiding translational neuroscience efforts.