Organoid Brain‐Machine‐Interface Devices for Central Nervous System Repair
Central nervous system (CNS) repair and regeneration suffer from tremendous clinical challenges due to current limitations in replacing lost neural tissues and restoring long-term neural circuits. Neural organoids, 3D lab-cultured neural tissues derived from stem cells, can recapitulate key cellular, structural, and physiological features of the human CNS, showing promising potential for neural regeneration. Here, we envision organoid brain-machine-interface (Organoid-BMI) devices as a new kind of neuroelectrical interface for CNS repair. The Organoid-BMI devices employ neural organoids and bioelectrodes as biohybrid bidirectional communication pathways to connect the human CNS and the external world. Acting as a biologically compatible intermediate, this approach may facilitate structural incorporation and functional alignment with host neural circuits for addressing persistent challenges of CNS repair including graft-host mismatch and long-term circuit stability. Through implementing adaptive and closed-loop strategies, this approach can modulate interaction and functional communication with the host for promoting CNS circuit remodeling and functional recovery. Together, this innovative technology may open new avenues for personalized regenerative medicine.
Authors
- Ken Mackie (ORCID: https://orcid.org/0000-0001-8501-6199)
- Huiyu Chu
- Jason Tchieu (ORCID: https://orcid.org/0000-0002-9793-9836)
- Yantao Xing (ORCID: https://orcid.org/0000-0002-4951-3451)
- Chunhui Tian (ORCID: https://orcid.org/0000-0001-8795-8892)
- Zichen Hong
- Hongwei Cai (ORCID: https://orcid.org/0000-0002-7067-9050)
- Feng Guo
- Mingxia Gu
- Yang Yang
- Susanne Clara Prokop
Institutions
- Brigham and Women's Hospital (US)
- Cincinnati Children's Hospital Medical Center (US)
- Harvard University (US)
- Indiana University Bloomington (US)
- Broad Center (US)
- University of Cincinnati (US)
Publication Details
- Journal
- Advanced Science
- Published
- 2026-04-27
- DOI
- https://doi.org/10.1002/advs.75444
- Primary Topic
- Neuroscience and Neural Engineering
- Type
- article
- Field-Weighted Citation Impact
- 0.00