An EEG (electroencephalogram) is a non-invasive test that uses small sensors placed on the scalp to measure the brain’s electrical activity. It provides a real-time readout of brain “waves,” which are rhythms generated by large groups of neurons working together. Clinicians use EEGs to identify patterns linked to sleep, seizures, and other changes in brain function. However, while EEGs can track these changing rhythms, they cannot reveal the cellular events causing altered electrical activity.
To explore what governs the emergence of these rhythmic patterns in humans, researchers need more advanced tools. In a study published on January 24, 2026, in Neurobiology of Disease, 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. This model allows the study of how coordinated rhythms emerge and how they react when neurons are perturbed with chemical compounds.
Innovative Approaches to Brain Rhythm Research
The research team employed a novel approach by growing two-dimensional (2D) networks of human neurons derived from induced pluripotent stem cells (iPSCs). They recorded the neurons’ activity over time using multi-electrode arrays (MEAs), which are plates embedded with tiny sensors capable of monitoring multiple independent networks simultaneously. Because iPSCs can be generated in the lab from accessible donor cells, such as skin or blood samples, they allow for the production 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 with faster rhythmic structures layered within them. These oscillations were detected across frequency ranges commonly seen in brain recordings, such as delta, theta, and alpha. The team then tested how specific biological mechanisms influence these rhythms.
“The results of these and other experiments show that this simplified 2D neuronal network model captures key features of network maturation, and gives us the scale and control needed for systematic testing,” said Anne Bang, PhD, the study’s senior and co-corresponding author, associate professor at the Center for Therapeutics Discovery at Sanford Burnham Prebys, and director of Cell Biology at the Conrad Prebys Center for Chemical Genomics.
Complementary Models and Experimental Insights
This new work positions 2D networks as complementary to three-dimensional brain organoids. Organoids, also produced from iPSCs, can replicate aspects of tissue architecture, cellular diversity, and network activity that are challenging to reproduce in 2D formats. However, the complexity of organoids can make certain experiments difficult to conduct at scale, especially those requiring large numbers of replicates or extensive dose-response testing across various conditions.
“Organoids are invaluable for modeling aspects of brain organization,” said Bang. “What we add here is a complementary 2D platform that emphasizes experimental control and throughput, capabilities that can be especially useful for benchmarking and systematic testing in disease modeling and early-stage therapeutic evaluation.”
A significant focus of the team’s study was inhibitory signaling mediated by GABA, a neurotransmitter produced by GABAergic neurons that helps stabilize and calm network activity. This inhibitory system is crucial for promoting sleep and preventing seizures. The team found that nested rhythms were reduced when GABA signaling was blocked with a GABA-A receptor antagonist drug, and that increasing the proportion of GABAergic neurons in the network caused these rhythms to emerge earlier. Their findings align with prior evidence in the field and support follow-up studies on how GABA-mediated inhibition shapes the emergence and maturation of oscillations in iPSC-based models of neurodevelopmental and psychiatric diseases.
Exploring Neuronal Excitability and Drug Effects
The researchers also tested drugs affecting potassium channels, which are proteins that help set a neuron’s ability to generate and transmit electrical signals, known as neuronal excitability. “We wanted to explore potassium channels because certain mutant forms have been implicated in neurological disorders such as epilepsy and neurodevelopmental syndromes,” said Bang.
The results suggested that different potassium channel perturbations could influence rhythmic organization in distinct ways, highlighting that excitability is not a simple dial that can be easily adjusted, and that specific mechanisms may have specific network-level signatures.
To enhance the interpretation of these recordings, the team used analysis methods developed in the laboratory of Bradley Voytek, PhD, co-corresponding author and professor and chair of Cognitive Sciences at UCSD. This framework separates two components of neural signals: oscillations (rhythmic peaks) and a broadband background signal often treated as random “noise” lacking useful data.
In some experiments in the new study, the broadband component shifted alongside oscillatory measures, suggesting it is not just background noise but can carry biologically meaningful information about the network. Measuring both components helps determine whether a drug is changing a specific rhythm, shifting the overall network state, or both.
Future Directions and Implications
The study also evaluated a faster neuron-production method based on inducing the expression of the transcription factor NEUROG2 (NGN2) in iPSCs. These NGN2-induced networks showed only rudimentary nested rhythms, indicating that rapid differentiation approaches may require further optimization to reliably capture rhythmic features.
“Improving these faster methods to generate neurons could broaden what is possible in higher-throughput settings,” said Bang.
By combining scalable human 2D neuronal networks with analysis that distinguishes rhythmic peaks from the broadband background, the research team’s approach provides a practical way to study how coordinated activity emerges and test how specific pathways or drug-mediated perturbations reshape network dynamics. Over time, this kind of controlled, reproducible platform can help build reference benchmarks for comparing genetic backgrounds, disease models, and candidate treatments.
Deborah Pré, PhD, a scientist II in the Bang lab at Sanford Burnham Prebys, shares first authorship of the study with Christian Cazares, PhD, a postdoctoral research fellow at UCSD. Additional authors include Alexander T. Wooten, Haowen Zhou, Isabel Onofre, and Ashley Neil at Sanford Burnham Prebys, and Todd Logan, Ruilong Hu, and Jan H. Lui at BioMarin Pharmaceutical. The study was supported by the National Institutes of Health, National Institute of Mental Health, National Institute of General Medical Sciences, BioMarin Pharmaceutical, Viterbi Family Foundation of the Jewish Community Foundation San Diego, and Burroughs Wellcome Fund.
The study’s DOI is 10.1016/j.nbd.2026.107281.
