A new dimension in brain research

Lab-grown human cells offer a simplified, flat view of how brain diseases develop, but that same simplicity makes it hard for them to mimic the richer, 3D structure of real brain tissue. How much does this limit what scientists can learn? Parodi et al. explored this question by examining how features like 3D structure, cell diversity, and modular organization capture the complexity of the human brain compared with traditional 2D cultures.

The team’s results demonstrate that 3D organization and modular architecture significantly enhance the

richness and complexity of network activity compared to traditional 2D experimental models.

“Strikingly, the complexity levels in our modular 3D networks approach those measured in animal cortex,” author Giulia Parodi said. “We consider this a milestone result because the experimental model integrates key brain-relevant features with unprecedented control. It also enables human-derived cultures to be an even more reliable model for medical brain research.”

The team’s 3D in vitro system was composed of human-induced, pluripotent stem cells-derived neurons with tunable excitation/inhibition ratios, allowing them to model different brain states and diseases. Using high-density microelectrode arrays, the authors quantified both spontaneous network dynamics and electrically evoked responses within the neurospheroids, offering a detailed view of how the 3D cells integrate and propagate activity.

Future work includes increasing the system’s resolution by enabling recordings from within the spheroids rather than only at their surface, and by more precisely defining and scaling their modular organization and inter-module interactions. These advances would provide a deeper, more physiologically relevant view of network dynamics in 3D neural cultures.

Source: “Functional human-derived neurospheroids capture cortical-like network complexity,” by Giulia Parodi, Giorgia Zanini, Linda Collo, Donatella Di Lisa, Cecilia Beccari, Michela Chiappalone, and Sergio Martinoia, APL Bioengineering (2026). The article can be accessed at https://doi.org/10.1063/5.0335967 .