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January 19, 2025 — The complexity of the human brain, with its 86 billion neurons and over 100 trillion synaptic connections, enables remarkable cognitive abilities such as abstract thinking, problem-solving, creativity, and social interaction. However, understanding how variations in brain signaling contribute to individual differences in cognition and behavior has remained one of neuroscience’s greatest challenges. A breakthrough study from Washington University in St. Louis is now shedding light on this mystery by offering a new framework for creating personalized brain models that capture the unique neural dynamics of individuals.
The study, led by ShiNung Ching, associate professor in the Preston M. Green Department of Electrical & Systems Engineering, and Todd Braver, professor in the Department of Psychological & Brain Sciences, introduces a novel method for generating individualized brain models. The work, published in Proceedings of the National Academy of Sciences (PNAS) on January 17, outlines a technique that uses non-invasive, high-temporal resolution brain scans to reveal the personalized workings of the human brain.
“We’re not explaining all the biophysical mechanisms at play, but we are uncovering why healthy individuals have different brain dynamics, which can lead to new insights into brain mechanics and generate testable predictions for brain phenomena,” said first author Matthew Singh, a postdoctoral fellow in the research team, now an assistant professor at the University of Illinois Urbana-Champaign.
A major highlight of this new technique is its ability to identify individual variations in the generation of alpha and beta brainwaves—two distinct types of electrical oscillations that reflect different cognitive states. Alpha waves are typically associated with relaxed states, such as meditation, while beta waves are linked to heightened alertness, such as during decision-making or problem-solving. By mapping these variations, the researchers revealed that they are not only linked to brain-wide fluctuations but also to differences in the balance between excitatory and inhibitory neurons.
Excitatory neurons promote activity by sending signals to other neurons, while inhibitory neurons regulate brain activity by suppressing certain signals. By analyzing how these neural types interact, the team was able to validate the personalized models, showing that they could accurately predict both alpha and beta patterns and anticipate future brain activity.
“This framework offers an invaluable tool for exploring the mechanisms that shape individual brain dynamics, all based on non-invasive measurements,” said Ching. “Our research will pave the way for precision brain models that could inform personalized medical treatments and predict future brain activity, offering powerful new approaches to both neuroscience and clinical intervention.”
The next phase of the research will involve refining and expanding these personalized models, with the goal of better understanding how individual brain dynamics impact cognitive function. There is also potential for this research to lead to new methods of enhancing cognitive functioning, possibly through interventions like neurostimulation.
“Our framework could one day provide insights into cognitive enhancement strategies and other applications in personalized medicine,” said Braver. “By deepening our understanding of individual brain variations, we hope to uncover new ways to optimize brain health and mental performance.”
This innovative research highlights the potential of personalized neuroscience in both research and clinical settings, opening doors to more precise treatments for neurological conditions and cognitive enhancement strategies.
More Information:
Matthew F. Singh et al, “Precision data-driven modeling of cortical dynamics reveals person-specific mechanisms underpinning brain electrophysiology,” Proceedings of the National Academy of Sciences (2025). DOI: 10.1073/pnas.2409577121.
Journal: Proceedings of the National Academy of Sciences
