Complexity analysis in human brain organoids

• Grant number: 22/14124-2
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: March 15, 2023
• End date: March 14, 2024
• Field of knowledge: Biological Sciences - Physiology - Physiology of Organs and Systems
• Principal Investigator: Alexandre Hiroaki Kihara
• Grantee: Mariana Sacrini Ayres Ferraz
• Supervisor: Alysson Renato Muotri
• Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Institution abroad: University of California, San Diego (UC San Diego), United States
• Associated to the scholarship: 19/15024-9 - Information flow in neuronal networks of networks: oscillations, criticality and electrical synapses, BP.PD

Abstract

Complexity is a concept that permeates several knowledge areas, as social, biological, or physical. The concept has been employed to describe physiological processes, in which researchers claim that it is the phenomenon responsible for allowing living beings to adapt to disturbances and preserve their homeostasis. The human brain is a prominent example of a complex system, which is highly adaptative, in which the interactions of a large number of neurons lead to the emergent phenomenon of consciousness. It is known that signals generated by the biological systems may carry information on the system's complexity. This information is useful in characterizing physiological states, monitoring health conditions, and predicting pathological events. In the context of neuroscience, much effort has been applied to the development of methodologies and posterior use of the complex systems analysis to investigate topology, dynamics, physiological states, and diseases of the brain. Parameters such as the Hurst exponent H have been used for pattern recognition, classification and identification of diseases. A recent hot topic in neuroscience is the human cerebral organoids. They have shown potential for modeling in vitro our brain, turning possible to study brain development, diseases and drug testing. However, there is still few research related to analysis of complexity in brain organoids. In this sense, how the complexity evolves with the development of the organoids? More specifically, how does neuronal activity change over time, and how is it temporally structured? In this project, we intend to analyze the complexity of developing human organoids by calculating the $H$ with time. The experimental data will be acquired via multi-electrode array (MEA), with which trains of neuronal firings will be obtained for each electrode. Like this one, many experiments present data as binary time series, which can present memory and H divergent from 0.5. These types of data can be analyzed using entropies and entropy rates, but there is still a lack of methodologies that relate these measures and parameters such as H. In this way, we also intend to develop a methodology to find the parameter H directly from binary series. (AU)