@article{chen2023discovering,
author = {Chen, Tianyi and Yang, Weiwei and White, Chris and Priebe, Carey E.},
title = {Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror},
year = {2023},
month = {July},
abstract = {Recent advancements have been made in the development of cell-based in-vitro neuronal networks, or organoids. In order to better understand the network structure of these organoids, a super-selective algorithm has been proposed for inferring the effective connectivity networks from multi-electrode array data. In this paper, we apply a novel statistical method called spectral mirror estimation to the time series of inferred effective connectivity organoid networks. This method produces a one-dimensional iso-mirror representation of the dynamics of the time series of the networks which exhibits a piecewise linear structure. A classical change point algorithm is then applied to this representation, which successfully detects a change point coinciding with the neuroscientifically significant time inhibitory neurons start appearing and the percentage of astrocytes increases dramatically. This finding demonstrates the potential utility of applying the iso-mirror dynamic structure discovery method to inferred effective connectivity time series of organoid networks.},
url = {http://approjects.co.za/?big=en-us/research/publication/discovering-a-change-point-and-piecewise-linear-structure-in-a-time-series-of-organoid-networks-via-the-iso-mirror/},
journal = {Applied Network Science},
}