Neuroscientists have developed computer-based systems that can simulate the dynamics of individual brains.
The human brain, consisting of 86 billion neurons and over 100 trillion connections, enables abstract thinking, learning languages, complex reasoning, problem-solving, creativity, and social interactions. A fundamental goal of neuroscience has been to understand how variations in brain signaling and dynamics lead to different thoughts and behaviors in people, though many aspects of this remain unclear.
Researchers from Washington University in St. Louis, combining efforts from neuroscientists and engineers, have created a new approach to generate personalized brain models, offering insights into each person’s unique neural dynamics. This project, spearheaded by ShiNung Ching, an associate professor in the Preston M. Green Department of Electrical & Systems Engineering, and Todd Braver, a professor in the Department of Psychological & Brain Sciences, was published on January 17 in PNAS. Their innovative framework allows for the construction of individualized brain models based on detailed data obtained from noninvasive, high-resolution brain scans. These tailored models will have potential uses in research and clinical practice, possibly leading to advancements in neuroscience and treatments for neurological disorders.
“Our research aims to clarify the variations in brain dynamics from person to person,” explained first author Matthew Singh, who completed this study as a postdoctoral fellow with Braver and Ching at WashU and is currently an assistant professor at the University of Illinois Urbana-Champaign. “While we do not cover every biophysical element at play in the human brain, our new modeling framework helps us understand why healthy people display differing brain dynamics, providing insights into brain functions and allowing us to make testable predictions about brain activity.”
A significant benefit of their method is its capability to identify individual differences in the production of alpha and beta waves while connecting these differences to overall changes in the brain. Alpha and beta brainwaves have distinct electrical frequencies and are associated with various cognitive states. For instance, alpha waves are linked to relaxed conditions, like during meditation, whereas beta waves are connected to alert and engaged states such as problem-solving and decision-making. Historically, variations in the peak frequency of alpha waves have been deemed a reliable indicator of individual brain and behavioral differences.
The research establishes a connection between variations in alpha and beta frequency oscillations with widespread changes in the balance between excitatory neurons (which enhance activity by sending signals to other neurons) and inhibitory neurons (which manage activity by preventing other neurons from firing). The researchers confirmed the efficacy of their personalized models by demonstrating their ability to replicate overall individual patterns of alpha and beta waves and accurately predict future brain activity across the entire brain, reinforcing the framework’s explanatory power.
“Our new method is a powerful resource for exploring the inner workings of individual brain dynamics through noninvasive brain activity measurements,” said Ching. “This will further high-level neuroscience, leading to the creation of precise brain models for individuals that can anticipate future brain behavior, potentially informing customized medical interventions.”
“Collaborative efforts to enhance and refine our model will be crucial as we progress with this project,” Braver stated. “Our groundbreaking approach might unveil new perspectives on how individual differences in brain dynamics can contribute to variations in cognitive performance. Ultimately, we hope this framework could offer fresh strategies for improving cognitive functions, perhaps through neurostimulation.”
