March 7, 2021

Phase Gradients and Anisotropy of the Suprachiasmatic Network

Biological neural networks operate at several levels of granularity, from the individual neuron to local neural circuits to networks of thousands of cells. The daily oscillation of the brain master clock in the suprachiasmatic nucleus (SCN) rests on a yet to be identified network of connectivity among its 20,000 neurons. The SCN provides an accessible model to explore neural organization at several levels of organization. To relate cellular to local and global network behaviors, we explore network topology by examining SCN slices in three orientations using immunochemistry, light and confocal microscopy, real-time imaging, and mathematical modeling. Importantly, the results reveal small local groupings of neurons that form intermediate structures, here termed phaseomes which can be identified through stable local phase differences of varying magnitude among neighboring cells. These local differences in phase are distinct from the global phase relationship, that between individual cells and the mean oscillation of the overall SCN. The magnitude of the phaseomes local phase differences are associated with a global phase gradient observed in the SCN rostral-caudal extent. Modeling results show that a gradient in connectivity strength can explain the observed gradient of phaseome strength, an extremely parsimonious explanation for the heterogeneous oscillatory structure of the SCN.

 bioRxiv Subject Collection: Neuroscience

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