Interpretable machine learning reveals hemispheric asymmetry of state switching in the suprachiasmatic nucleus

The suprachiasmatic nucleus (SCN) is the master circadian clock in mammals, comprising ∼20,000 neurons organized into a bilaterally symmetric oval structure. System-level time computations in the SCN depend on coordinated spatiotemporal patterns of neuronal activity, yet most prevailing methods perform time-series analyses while disregarding neural spatiotemporal organization. Here, we developed an interpretable machine learning framework for the integrative analysis of large-scale spatiotemporal calcium signals from SCN neurons, with built-in validation and biological interpretability. Applying this framework, we identified distinct neural spatiotemporal states and subtypes whose spatial mapping across hemispheres revealed hemispheric asymmetry, particularly during the subjective day. Circadian timekeeping ability also displayed side specificity, with each hemisphere encoding a full yet unique time feature representation. Attribution analysis further indicated spectral features of calcium signals as the primary discriminative elements underlying this asymmetry. Overall, we demonstrate that hemispheric asymmetry of state switching is a fundamental property of the brain's circadian clock.