Volume 27, Issue 1 (2024)
mjms 2024, 27(1): 0-0 |
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Naghdabadi Z, Mirsadeghi S, kiani S. Topological Causal Analysis Reveals Two Stages of In Vitro neural Network Maturation: Network Expansion and Network Reinforcement. mjms 2024; 27 (1)
URL: http://mjms.modares.ac.ir/article-30-77651-en.html
URL: http://mjms.modares.ac.ir/article-30-77651-en.html
1- Department of Electrical Engineering, Sharif University of Technology, Tehran 1458889694, Iran.
2- Department of Stem Cell and Developmental Biology, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.Department of Brain and Cognitive Sciences, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.
3- Department of Stem Cell and Developmental Biology, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.Department of Brain and Cognitive Sciences, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.Center for Cognitive Science, Institute for Convergence Science & Technology, Sharif University of Technology, Tehran 14588-89694, Iran. ,skiani2536@gmail.com
2- Department of Stem Cell and Developmental Biology, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.Department of Brain and Cognitive Sciences, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.
3- Department of Stem Cell and Developmental Biology, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.Department of Brain and Cognitive Sciences, Cell Science Research Center, ROYAN Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran.Center for Cognitive Science, Institute for Convergence Science & Technology, Sharif University of Technology, Tehran 14588-89694, Iran. ,
Abstract: (1004 Views)
Background: topological data analysis (TDA) in neural network and its advantages over traditional graph theory methods by capturing higher-order relationships and complex structures within the brain examined in this research. TDA provides insights into cognitive processes by analyzing multi-scale interactions among neural activities and is increasingly utilized in both brain science and psychological research.
Methods: The methodology explores neural data from various sources, including multi-electrode arrays (MEAs), to study neural ensemble behaviors and connectivity. Additionally, it critiques existing methods like Granger causality analysis (GCA) for their limitations in interpreting neural data.
Results: According to our findings, the number of spiking activities and active channels rise from the 10th to the 60th day in vitro (DIV). Burst activities peaked between 30 and 50 DIV, while the firing rate in active channels continued to increase until 30 DIV. Furthermore, the average burst length exhibited a consistent rise until 50 DIV. However, the percentage of spikes involved in burst activities displayed a non-monotonic pattern, initially declining until 30 DIV and rising again in subsequent days. The fluctuations in average spike amplitudes can be attributed to factors such as the distance between neurons and electrodes, as well as the ongoing neuronal plasticity and migration.
Conclusion: In summary, TCA provides qualitative insights into network status based on quantitative metrics and established thresholds. While we focused on primary neuronal cells derived from rat cortices, MEA technology is versatile enough to monitor the developmental stages of any neuronal type, including those derived from human sources
Methods: The methodology explores neural data from various sources, including multi-electrode arrays (MEAs), to study neural ensemble behaviors and connectivity. Additionally, it critiques existing methods like Granger causality analysis (GCA) for their limitations in interpreting neural data.
Results: According to our findings, the number of spiking activities and active channels rise from the 10th to the 60th day in vitro (DIV). Burst activities peaked between 30 and 50 DIV, while the firing rate in active channels continued to increase until 30 DIV. Furthermore, the average burst length exhibited a consistent rise until 50 DIV. However, the percentage of spikes involved in burst activities displayed a non-monotonic pattern, initially declining until 30 DIV and rising again in subsequent days. The fluctuations in average spike amplitudes can be attributed to factors such as the distance between neurons and electrodes, as well as the ongoing neuronal plasticity and migration.
Conclusion: In summary, TCA provides qualitative insights into network status based on quantitative metrics and established thresholds. While we focused on primary neuronal cells derived from rat cortices, MEA technology is versatile enough to monitor the developmental stages of any neuronal type, including those derived from human sources
Article Type: Original Research |
Subject:
Cellular and Molecular Neuroscience
Received: 2024/10/24 | Accepted: 2024/10/30
Received: 2024/10/24 | Accepted: 2024/10/30
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