System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning
这里是标题一h1占位文字
12.10/
2024
A molecularly defined amygdalaindependent tetra-synaptic forebrain-tohindbrain pathway for odor-driven innate fear and anxiety
Zichen Wang, Jing Yu, Muyue Zhai, Zehua Wang, Kaiwen Sheng, Yu Zhu, Tianyu Wang, Mianzhi Liu, Lu Wang, Miao Yan, Jue Zhang, Ying Xu, Xianhua Wang, Lei Ma, Wei Hu and Heping Cheng
Publication: Cell Research
Read more: System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning | Cell Research (nature.com)
Abstract
The suprachiasmatic nucleus (SCN) is the mammalian central circadian pacemaker with heterogeneous neurons acting in concert while each neuron harbors a self-sustained molecular clockwork. Nevertheless, how system-level SCN signals encode time of the day remains enigmatic. Here we show that population-level Ca2+ signals predict hourly time, via a group decision-making mechanism coupled with a spatially modular time feature representation in the SCN. Specifically, we developed a high-speed dual-view two-photon microscope for volumetric Ca2+ imaging of up to 9000 GABAergic neurons in adult SCN slices, and leveraged machine learning methods to capture emergent properties from multiscale Ca2+ signals as a whole. We achieved hourly time prediction by polling random cohorts of SCN neurons, reaching 99.0% accuracy at a cohort size of 900. Further, we revealed that functional neuron subtypes identified by contrastive learning tend to aggregate separately in the SCN space, giving rise to bilaterally symmetrical ripple-like modular patterns. Individual modules represent distinctive time features, such that a module-specifically learned time predictor can also accurately decode hourly time from random polling of the same module. These findings open a new paradigm in deciphering the design principle of the biological clock at the system level.
Latest
B2,Building 16,Treehouse,73 Tanmi Road,
Jiang Bei Xin Qu District, Nanjing, Jiangsu,
P. R. CHINA
