Discovering a change point and piecewise linear structure in a time series of organoid networks via the iso-mirror
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916752930111488 |
|---|---|
| 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 |