Researchers Unveil Scalable Human Brain Model for Rhythm Studies

Researchers at Sanford Burnham Prebys Medical Discovery Institute, in collaboration with the University of California San Diego (UCSD) and BioMarin Pharmaceutical, developed a simplified, scalable human cell model to study how coordinated brain rhythms emerge and respond to chemical compounds. This study was published on January 24, 2026, in Neurobiology of Disease.

EEG (electroencephalogram) measures the brain's electrical activity, revealing patterns linked to sleep, seizures, and other brain function changes.

While EEGs track developmental changes in these rhythms, they do not explain the cellular mechanisms causing altered electrical activity.

Methodology: Building a Model Brain

The team grew two-dimensional (2D) networks of human neurons derived from induced pluripotent stem cells (iPSCs). Neuronal activity was recorded over time using multi-electrode arrays (MEAs). iPSCs allow for the generation of large numbers of human neurons from both healthy individuals and patients.

As these 2D networks matured, researchers observed the emergence of "nested oscillations," which are slow waves containing faster rhythmic structures, across delta, theta, and alpha frequency ranges.

Key Experimental Findings

Experiments showed that blocking GABA signaling, a neurotransmitter that calms network activity, reduced nested rhythms. Increasing the proportion of GABAergic neurons in the network caused these rhythms to emerge earlier. These findings align with existing evidence on GABA's role in shaping oscillations.

The team also investigated the effects of drugs targeting potassium channels, which regulate neuronal excitability. Results indicated that different potassium channel perturbations influence rhythmic organization distinctively, suggesting that excitability involves specific mechanisms with unique network-level signatures.

Analysis methods developed at UCSD, which separate rhythmic oscillations from a broadband background signal, were utilized. In some experiments, the broadband component shifted alongside oscillatory measures, suggesting it carries biologically meaningful information beyond mere background noise.

Finally, the study evaluated a faster neuron-production method using NEUROG2 (NGN2) induction in iPSCs. NGN2-induced networks exhibited only rudimentary nested rhythms, indicating that rapid differentiation methods may require further optimization to reliably capture rhythmic features.

Conclusion and Future Implications

This approach, combining scalable 2D neuronal networks with detailed signal analysis, provides a practical platform for studying the emergence of coordinated activity and testing how specific pathways or drug interventions reshape network dynamics.

This platform can assist in building reference benchmarks for comparing genetic backgrounds, disease models, and candidate treatments.

Deborah Pré, PhD, and Christian Cazares, PhD, are shared first authors of the study.