Large scale collective neural cell dynamics in Alzheimer's Disease and aging

Neural networks in vivo operate on a range of scales, with information thought to be carried by groups of neurons, and often traveling in waves of activity, such as gamma and delta waves. However, gamma waves are known to be disrupted in Alzheimer’s disease (AD). In this work we investigate which aspects of this collective behavior neural cells retain in vitro with a new AD model and healthy controls. This AD model rapidly develops reliable molecular markers of the disease, but has not been measured dynamically. We record the activity of a self-assembled network of thousands of neural cells in one field of view via calcium imaging. These  networks display spontaneous calcium activity across both disease and healthy conditions, at relatively similar rates of activity. Using the time-delay cross-correlation between pairs of cells, we compare the distribution of correlations both across and within experimental conditions. This allows us to measure the impact of Alzheimer's Disease and aging compared to the biological variability within each experimental condition. This functional characterization approach allows us to validate whether this new model of AD and aging captures both the molecular and dynamic signatures of the disease.