Brain organoids, high-density MEAs, and deep learning: A new paradigm for functional neurobiomarker discovery

Biomarker discovery, essential in neuroscience research, has traditionally been based on static molecular omics, which frequently reveal established pathogenesis at the network level when irreversible structural damage occurs. In this review, the paradigm shift of functional "digital phenotyping" is discussed, along with its implementation by combining human induced pluripotent stem cell (hiPSC)-derived brain organoids with high-density multielectrode arrays (HD-MEAs). Researchers can use advanced architecture deep learning (DL) algorithms, such as Convolutional and Graph Neural Networks, to identify predictive signatures of diseases at the network level by separating complex spatiotemporal dynamics in firing patterns. These digital functional biomarkers can their ability to detect abnormal burst kinetics, abnormal oscillatory coupling, and abnormal functional connectomes before the onset of structural cell loss. Moreover, we discuss the underlying methods of artificial intelligence (AI) analysis and advances in phenotypic drug discovery using organoid electrophysiology, changing the paradigm of therapeutic targeting from the clearance of molecular aggregates to the functional recuperation of neural circuit health. In conclusion, a combination of powerful 3D culture technology, state-of-the-art microelectronics, and machine learning offers an extensive novel translational platform for dissecting early neurodevelopmental and neurodegenerative diseases, ultimately driving precision therapeutic approaches for the brain and other organs.