Deciphering Complexity in Human Brain Organoids via a Novel Hurst Exponent Estimation

Area of Science:

• Neuroscience
• Developmental Biology
• Computational Biology

Background:

• The human brain's complexity arises from intricate neuronal interactions.
• The Hurst exponent (H) is a metric for temporal correlations in neuronal activity.
• Quantifying neural complexity in developing brain models remains challenging.

Purpose of the Study:

• To introduce a novel method for estimating the Hurst exponent (H) from binary spike trains.
• To propose the coefficient of variation (CV) of H as a superior metric for neural complexity.
• To analyze neural complexity dynamics during human cortical organoid maturation.

Main Methods:

• Developed a method to estimate H directly from binary spike train data.
• Validated the H estimation method using simulated data with controlled correlations.
• Applied the method to multielectrode array recordings from human induced pluripotent stem cell-derived cortical organoids over 250 days.

Main Results:

• The CV of H, reflecting neural complexity, peaked around day 100 of organoid development.
• Organoids at peak complexity exhibited diverse activity patterns, from isolated spikes to synchronized events.
• The study demonstrates a structured evolution of complexity in developing organoids.

Conclusions:

• The coefficient of variation (CV) of the Hurst exponent (H) is a robust metric for assessing neural complexity.
• This method provides a scalable tool for quantifying developmental transitions in neural network activity.
• Findings offer insights into the maturation process of human brain organoids.