Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror

TL;DR

This paper introduces a spectral mirror estimation method to identify change points and piecewise linear structures in the effective connectivity networks of neuronal organoids, revealing significant biological transitions.

Contribution

The novel spectral mirror estimation technique uncovers change points and structural dynamics in organoid network data, advancing analysis of neural network development.

Findings

Detected a change point coinciding with inhibitory neuron appearance

Identified a dramatic increase in astrocyte percentage

Demonstrated utility of iso-mirror method for network dynamics

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.