PT  - JOURNAL ARTICLE
AU  - Tomoko Yoshikawa
AU  - Scott Pauls
AU  - Nicholas Foley
AU  - Alana Taub
AU  - Joseph LeSauter
AU  - Duncan Foley
AU  - Ken-Ichi Honma
AU  - Sato Honma
AU  - Rae Silver
TI  - Phase Gradients and Anisotropy of the Suprachiasmatic Network
AID  - 10.1101/2021.02.01.429173
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.02.01.429173
4099  - http://biorxiv.org/content/early/2021/02/02/2021.02.01.429173.short
4100  - http://biorxiv.org/content/early/2021/02/02/2021.02.01.429173.full
AB  - 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’s 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’s 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.Significance statement Oscillation is a fundamental property of information sensing and encoding in the brain. Using real time imaging and modeling, we explore encoding of time by examining circadian oscillation in single neurons, small groups of neurons, and the entire nucleus, in the brain’s master: the suprachiasmatic nucleus (SCN). New insights into the network organization underlying circadian rhythmicity include the discovery of intermediate structures, termed ‘phaseomes’, characterized by neurons which are stably out of phase with their neighbors. Modeling indicates that the pattern of phaseomes across the tissue encompasses a gradient in connectivity strength from the rostral to caudal aspects of the nucleus. Anisotropy in network organization emerges from comparisons of phaseomes and connectivity gradients in sagittal, horizontal and coronal slices.Competing Interest StatementThe authors have declared no competing interest.