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

Fuente: arXiv
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Autores principales: Chen, Tianyi, Park, Youngser, Saad-Eldin, Ali, Lubberts, Zachary, Athreya, Avanti, Pedigo, Benjamin D., Vogelstein, Joshua T., Puppo, Francesca, Silva, Gabriel A., Muotri, Alysson R., Yang, Weiwei, White, Christopher M., Priebe, Carey E.
Formato: Preprint
Publicado: 2023
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author Chen, Tianyi
Park, Youngser
Saad-Eldin, Ali
Lubberts, Zachary
Athreya, Avanti
Pedigo, Benjamin D.
Vogelstein, Joshua T.
Puppo, Francesca
Silva, Gabriel A.
Muotri, Alysson R.
Yang, Weiwei
White, Christopher M.
Priebe, Carey E.
author_facet Chen, Tianyi
Park, Youngser
Saad-Eldin, Ali
Lubberts, Zachary
Athreya, Avanti
Pedigo, Benjamin D.
Vogelstein, Joshua T.
Puppo, Francesca
Silva, Gabriel A.
Muotri, Alysson R.
Yang, Weiwei
White, Christopher M.
Priebe, Carey E.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2303_04871
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
Chen, Tianyi
Park, Youngser
Saad-Eldin, Ali
Lubberts, Zachary
Athreya, Avanti
Pedigo, Benjamin D.
Vogelstein, Joshua T.
Puppo, Francesca
Silva, Gabriel A.
Muotri, Alysson R.
Yang, Weiwei
White, Christopher M.
Priebe, Carey E.
Applications
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.
title Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
topic Applications
url https://arxiv.org/abs/2303.04871