Summary: Researchers have developed a groundbreaking 3D brain model that closely mirrors the architecture and function of the human brain. Built using biomimetic 3D printing, the Bioengineered Neural Network (BENN) features distinct gray and white matter regions and responds to electrical stimulation like real brain tissue.
When exposed to low levels of alcohol, BENN exhibited Alzheimer’s-related protein buildup and structural damage, revealing how moderate drinking may harm brain health. This platform offers unprecedented real-time insights into disease processes and paves the way for more accurate preclinical testing.
Key Facts:
- 3D Brain Model: BENN replicates human brain structure with functionally distinct gray and white matter regions.
- Alcohol Impact: Daily exposure to moderate alcohol levels increased Alzheimer’s proteins and neural damage in the model.
- Clinical Potential: BENN allows real-time visualization of neurotoxic effects, aiding early diagnosis and drug testing.
Source: POSTECH
A research team led by Professor Dong-Woo Cho (Department of Mechanical Engineering, POSTECH) and Professor Jinah Jang (Departments of Mechanical Engineering, IT Convergence Engineering, Life Sciences, and Interdisciplinary Graduate Program), in collaboration with Dr. Mihyeon Bae, and Dr. Joeng Ju Kim, has successfully developed a three-dimensional (3D) brain model that closely mimics the structure and function of the human brain.
The study was published in the International Journal of Extreme Manufacturing, a leading journal in the field of manufacturing and materials science.
Neurodegenerative diseases such as Alzheimer’s and Parkinson’s are notoriously difficult to reverse once they onset occurs, making early diagnosis and predictive modeling critically important.
However, the brain is the most complex organ in the human body, with intricately interconnected cells and signaling mechanisms that remain largely unexplored.
Recent studies have suggested that even everyday alcohol consumption may be linked to neural damage, further emphasizing the urgent need for in vitro brain models that can precisely replicate human brain responses in laboratory settings.
Existing two-dimensional cell cultures and stem cell-derived organoids have shown significant limitations in reproducing the complex architecture and function of the brain.
To overcome these limitations, the POSTECH research team developed the Bioengineered Neural Network (BENN)—a novel 3D artificial brain model constructed layer by layer, akin to building a house using a 3D printer.
A central innovation of this model lies in the biomimetic compartmentalization into two distinct regions: gray matter, which contains neuronal cell bodies, and white matter, which consists of aligned axons that act as information highways facilitating signal transmission.
The researchers applied electrical stimulation to guide the axonal growth of neurons in a specific direction, promoting the formation of aligned and interconnected neural pathways.
This led to the establishment of a functional neural network that closely resembles the brain’s native signal transmission architecture. Real-time monitoring of calcium ion flux confirmed that the BENN model exhibited electrophysiological responses analogous to those observed in actual brain tissue.
Furthermore, the team utilized the BENN platform to investigate the effects of alcohol exposure on brain function. The model was treated daily with ethanol at a concentration of 0.03%—representative of moderate social drinking—for three weeks.
In the gray matter region, they observed elevated levels of Alzheimer’s-related proteins, including amyloid-beta and tau. In the white matter, they identified significant morphological changes in neural fibers, including swelling and distortion.
The propagation of neural signals also exhibited marked attenuation. This study is the first to directly visualize and quantify region-specific neurotoxic responses to alcohol in real time using a bioengineered brain model.
Professor Dong-Woo Cho stated, “This model enables high-resolution analysis of neural connectivity and electrophysiological responses that were previously difficult to observe. It holds significant potential for early disease detection and accurate prediction of therapeutic outcomes at the preclinical stage.”
Professor Jinah Jang added, “This research marks an important step forward in our ability to investigate the early pathological events of brain diseases in a laboratory setting.”
Funding: This research was supported by Korean Fund for Regenerative Medicine funded by Ministry of Science and ICT, and Ministry of Health and Welfare (22A0106L1, Republic of Korea) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022M3C1A3081359).
About this neurotech and neuroscience research news
Author: Jinyoung Huh
Source: POSTECH
Contact: Jinyoung Huh – POSTECH
Image: The image is credited to Neuroscience News
Original Research: Open access.
“3D bioprinted unidirectional neural network and its application for alcoholic neurodegeneration” by Dong-Woo Cho et al. International Journal of Extreme Manufacturing
Abstract
3D bioprinted unidirectional neural network and its application for alcoholic neurodegeneration
The brain exhibits complex physiology characterized by unique features such as a brain-specific extracellular matrix, compartmentalized structure (white and grey matter), and an aligned axonal network.
These physiological characteristics underpin brain function and facilitate signal transduction similar to that in an electrical circuit.
Therefore, investigating these features in vitro is crucial for understanding the interactions between neuronal signal transduction processes and the pathology of neurological diseases.
Compared to neurons on patterned substrates, three-dimensional (3D) bioprinting-based neural models provide significant advantages in replicating axonal kinetics without physical limitations.
This study proposes the development of a 3D bioprinted engineered neural network (BENN) model to replicate the physiological features of the brain, suggesting its application as a tool for studying neurodegenerative diseases.
We employed 3D bioprinting to reconstruct the compartmentalized structure of the brain, and controlled the directionality of axonal growth by applying electrical stimuli to the printed neural structure for overcoming spatial constraints.
The reconstructed axonal network demonstrated reliability as a neural analog, including the visualization of mature neuronal features and spontaneous calcium reactions.
Furthermore, these brain-like neural network models have demonstrated usefulness for studying neurodegeneration by enabling the visualization of degenerative pathophysiology in alcohol-exposed neurons.
The BENN facilitates the visualization of region-specific pathological markers in soma or axon populations, including amyloid-beta formation and axonal deformation.
Overall, the BENN closely mimics brain physiology, offers insights into the dynamics of axonal networks, and can be applied to studying neurological diseases.
