Revolutionary high-resolution human 3D brain organoid platform integrating AI-based analytics

The long-term vision of the 3D-BrAIn consortium is to revolutionize personalized precision medicine for central nervous system (CNS)

disorders, by developing an innovative bio-digital twin model of the human brain that is personalized, precise, and predictive.

In this pathfinder project we bring together three breakthrough technologies: 1) a novel, highly reproducible human brain modelling

technology using robust adherent iPSC-derived 3D cortical organoid cultures, 2) a unique, state-of-the-art 3D multi-electrode array

(MEA) technology for non-invasive high-resolution electrophysiological recordings and 3) a novel approach to analyse and interpret

the large quantities of functional data using tailored automated machine learning (ML)-based algorithms.

With this breakthrough approach we overcome significant hurdles that made it thus far impossible to create a truly representative

and functional model of the CNS for personalized medicine, drug screening and neurotoxicity testing. The revolutionary 3D-BrAIn

high-precision CNS platform will allow robust and accurate modelling of the CNS for a broad range of neuropsychiatric diseases.

Ultimately, the 3D-BrAIn technology will be translatable to multiple other organ systems (cardiomyocytes, pancreatic islets, retina), to

non-invasively obtain longitudinal 3D high-resolution electrophysiological recordings and effectively interpret them.

In this project a prototype of the 3D-BrAIn platform will be developed by growing functional 3D organoids that faithfully resemble

the human cortex on 3D MEA micropillar electrodes, enabling continuous functional monitoring and by developing ML-based

algorithms that can process and interpret the large spatiotemporal data sets. Once all individual components are optimized and

integrated, proof-of-concept will be obtained by validating the platform for two of the envisaged applications: CNS drug

development and neurotoxicity screening.