Back

Brainwaves Monitoring via Human Midbrain Organoids Microphysiological Analysis Platform: MAP

Hong, S.; Song, M.; Yang, W.; Park, I.-H.; Lee, L. P.

2024-09-26 bioengineering
10.1101/2024.09.24.613225 bioRxiv
Show abstract

Understanding the development and pathogenesis of the human midbrain is critical for developing diagnostics and therapeutics for incurable neurological disorders including Parkinsons disease (PD)1-3. While organoid models are introduced to delineate midbrain-related pathogenesis based on experimental flexibility4-6, there is currently a lack of tools with high fidelity for tracing the long-term dynamics of intact brain networks-- an essential portrait of physiological states7,8. Here, we report a brain organoid microphysiological analysis platform (MAP) designed for long-term physiological development and in-situ real-time monitoring, akin to electroencephalogram (EEG), of midbrain organoids. We successfully achieved the on-chip homogeneous organogenesis of midbrain organoids and in-situ, non-disturbing electrophysiological tracking of the midbrain network activities. Throughout our long-term EEG monitoring via MAP, we captured the early-stage electrophysiological evolution of midbrain development, transitioning from discontinuous brief brainwave bursts to complex broadband activities. Furthermore, our midbrain organoid MAP facilitated the modeling and monitoring of neurotoxin-induced Parkinsonism, replicating the pathological dynamics of midbrain circuitry and exhibiting PD-like alterations in beta oscillation. We envision that the modeling and monitoring of brain organoid MAP will significantly enhance our understanding of human neurophysiology, neuropathogenesis, and drug discovery of neurodegenerative diseases.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.