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

Applied Network Science

Research

Ali Saad-Eldin¹  Youngser Park¹  Carey E. Priebe¹  Tianyi Chen¹  Zachary Lubberts¹  Benjamin D. Pedigo¹  Joshua T. Vogelstein¹  Avanti Athreya¹  Gabriel A. Silva²  Alysson R. Muotri³  Francesca Puppo³  Christopher M. White⁴  Weiwei Yang⁴ 

[1] Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, USA;Department of Neurosciences, University of California, San Diego, USA;Department of Pediatrics, University of California, San Diego, USA;Microsoft Research, Seattle, USA;

关键词: Organoid;    Time series network analysis;    Multidimensional scaling;    Manifold learning;    Change point detection;   

DOI  :  10.1007/s41109-023-00564-5

 received in 2023-03-09, accepted in 2023-06-17,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

Fig. 3

Fig. 2

Fig. 6

Fig. 3

Fig. 17

Fig. 2

Fig. 9

Fig. 1

Fig. 1

Fig. 2

Fig. 8

Fig. 16

Fig. 7

Fig. 2

Fig. 15

Fig. 1

Figs. 1

Fig. 3

Fig. 7

Fig. 8

40854_2023_500_Article_IEq6.gif

Fig. 2

Fig. 5

Fig. 1

Fig. 1

Fig. 4

40538_2023_441_Article_IEq3.gif

Fig. 4

Fig. 4

Fig. 3

Fig. 3

Fig. 2

Fig. 2

Fig. 6

Fig. 1

Fig. 2

Fig. 5

Fig. 3

Fig. 3

Fig. 6

Fig. 2

Fig. 3

Fig. 2

Fig. 1

Fig. 1

Fig. 6

Fig. 4

12888_2023_5124_Article_IEq2.gif

12888_2023_5124_Article_IEq1.gif

Fig. 2

Fig. 3

Fig. 3

Fig. 7

Fig. 2

Fig. 4

Fig. 5

Fig. 3

Fig. 4

Fig. 3

13690_2023_1147_Article_IEq4.gif

13690_2023_1147_Article_IEq3.gif

13690_2023_1147_Article_IEq1.gif

Fig. 3

Fig. 1.

Fig. 1

Fig. 6

Fig. 3

Fig. 2

Fig. 3

Fig. 7

Fig. 2

Fig. 5

Fig. 1

Fig. 1

Fig. 3

Fig. 6

Fig. 3