Imagine if we could decode the intricate symphony of brain waves, uncovering the secrets behind disorders like epilepsy and autism. That's exactly what researchers are striving for, and a groundbreaking study published in Neurobiology of Disease on January 24, 2026, brings us a step closer. But here's where it gets fascinating: instead of relying solely on traditional EEG readings, scientists at Sanford Burnham Prebys Medical Discovery Institute, alongside collaborators at UC San Diego and BioMarin Pharmaceutical, have developed a revolutionary approach using scalable human neuron networks.
EEGs, those painless tests with sensors on your scalp, have long been our window into the brain's electrical activity. They reveal rhythms—think of them as brain waves—generated by neurons firing in harmony. These rhythms are linked to everything from sleep cycles to seizures. However, EEGs only show the surface; they can't explain why these rhythms change or what happens inside brain cells to cause disruptions.
And this is the part most people miss: to truly understand these rhythms, we need tools that go beyond surface-level observations. Enter the researchers' innovative solution: a simplified, scalable model of human neurons grown in two-dimensional (2D) networks. Derived from induced pluripotent stem cells (iPSCs), these networks allow scientists to study how rhythms emerge and respond to disruptions, like those caused by chemical compounds.
Here’s how it works: iPSCs, created from easily accessible cells like skin or blood samples, are transformed into neurons. These neurons are then grown in 2D networks and monitored using multi-electrode arrays (MEAs), tiny sensors that track their activity over time. As these networks mature, researchers observe “nested oscillations”—slow waves with faster rhythms layered within them, mirroring patterns seen in real brain recordings (delta, theta, and alpha waves).
But here's the controversial part: while 3D brain organoids have been the go-to for modeling brain complexity, this 2D approach offers something unique. Is simplicity the key to scalability? Anne Bang, PhD, the study's lead author, argues that 2D networks provide the control and throughput needed for systematic testing, making them ideal for disease modeling and early-stage drug evaluation. Organoids, while invaluable for replicating tissue architecture, can be complex and harder to scale for large experiments.
One major focus of the study was GABA, a neurotransmitter that acts like the brain’s “calm button,” stabilizing network activity. When GABA signaling was blocked, nested rhythms diminished, and increasing GABAergic neurons caused rhythms to emerge earlier. These findings align with previous research but also raise questions: How does GABA-mediated inhibition shape neurodevelopmental and psychiatric disorders?
The team also explored potassium channels, proteins critical for neuronal excitability. Their results suggest that disruptions in these channels influence rhythmic organization in distinct ways, challenging the idea that excitability is a simple dial to turn up or down. Could this mean that neurological disorders have more nuanced network-level signatures than we thought?
To deepen their analysis, the researchers used a framework developed by Bradley Voytek, PhD, which separates neural signals into oscillations and a broadband background signal often dismissed as noise. Surprisingly, the broadband component shifted alongside oscillatory measures, suggesting it carries meaningful biological information. This dual analysis helps determine whether a drug alters a specific rhythm, shifts the entire network state, or both.
Finally, the study tested a faster neuron-production method using the transcription factor NEUROG2 (NGN2). While these networks showed rudimentary rhythms, they highlight the need for optimization to reliably capture complex features. Can we strike a balance between speed and accuracy in neuron production?
By combining scalable 2D networks with advanced analysis techniques, this research offers a practical way to study coordinated brain activity and test how disruptions reshape network dynamics. Over time, this platform could establish benchmarks for comparing genetic backgrounds, disease models, and treatments.
The study, co-authored by Deborah Pré, PhD, and Christian Cazares, PhD, alongside a multidisciplinary team, was supported by the NIH, BioMarin Pharmaceutical, and other leading institutions.
So, what do you think? Is the future of brain research in simplicity, or do we need the complexity of organoids to truly understand the brain? Share your thoughts in the comments—let’s spark a conversation!
