The human brain is one of the most complex and advanced structures known to science. With its 86 billion neurons and over 100 trillion connections, the brain underpins everything from abstract thinking, language acquisition, and advanced problem-solving, to creativity and social behavior. This remarkable complexity gives rise to individual cognition and behavior, which vary significantly from person to person. For years, neuroscientists have strived to understand how subtle differences in brain signaling and dynamics contribute to these individual variations. While there have been advancements in this field, many mysteries regarding brain function and how they influence cognition and behavior remain largely unexplained.
A promising new development in this realm comes from a collaboration between neuroscientists and engineers at Washington University in St. Louis. Their groundbreaking research offers an innovative approach to mapping and understanding these individual differences in brain function. Led by ShiNung Ching, an associate professor in the Preston M. Green Department of Electrical & Systems Engineering in the McKelvey School of Engineering, and Todd Braver, a professor in the Department of Psychological & Brain Sciences in Arts & Sciences, the study introduces a novel framework for constructing personalized brain models. These models, based on noninvasive, high-temporal resolution brain scans, provide a deeper understanding of neural dynamics and pave the way for more personalized treatment methods, especially in the realms of neuroscience research and clinical interventions.
The research, published on January 17 in Proceedings of the National Academy of Sciences (PNAS), seeks to fill critical gaps in our understanding of how the brain’s neural activity varies across individuals. As highlighted by the researchers, current neuroscience has yet to fully explain the mechanisms that lead to such variation in brain dynamics. However, with their innovative framework, the team has taken an important step toward addressing this by offering a new method for studying personalized brain activity in ways that can both enhance current knowledge and guide future medical advances.
Mapping Brain Dynamics: A New Method
The primary goal of the new study is to develop a better understanding of why different people exhibit different brain dynamics, even when considered healthy. To make headway in this area, the team created a method for generating personalized brain models—models that closely represent an individual’s brain activity and the underlying neural mechanisms. A central challenge in neuroscience has been to establish personalized models that can accurately reflect individual variations, an important step toward understanding how these variations affect cognitive and behavioral outcomes.
Matthew Singh, the first author of the study, conducted the research while a postdoctoral fellow under Braver and Ching at Washington University. Singh is now an assistant professor at the University of Illinois Urbana-Champaign. He explained, “We’re not explaining the full range of biophysical mechanisms at work in the human brain, but we are able to shed light on why healthy individuals have different brain dynamics with our new modeling framework. This gives us insights into brain mechanics and generates testable predictions of brain phenomena.”
One of the key strengths of the new approach lies in its ability to observe how different individuals generate alpha and beta waves in their brains. These are two types of brainwaves—each characterized by distinct electrical frequencies—that play crucial roles in the brain’s functioning. Alpha waves are typically associated with relaxed states, such as deep relaxation or meditation, whereas beta waves are linked to more active cognitive functions, such as decision-making, problem-solving, and active thinking.
Alpha and beta waves have been shown to vary among individuals, and this variation has been a significant area of interest for neuroscientists. The peak frequency of alpha waves, in particular, has often been used as a reliable measure of individual differences in behavior and cognitive functioning. The team’s framework, however, goes beyond these measures by examining how these wave patterns are related to broader brain-wide dynamics.
Exploring the Interplay of Excitatory and Inhibitory Neurons
To deepen their understanding, the researchers examined the relationship between two different types of neurons—excitatory and inhibitory—and their role in brain activity. Excitatory neurons increase the brain’s overall activity by transmitting electrical signals to other neurons, while inhibitory neurons counterbalance this activity by dampening the signals and preventing overactivity. This balance plays a fundamental role in maintaining optimal brain function.
The research team found that individual differences in the generation of alpha and beta waves were linked to the balance between these two types of neurons. Specifically, differences in the strength and timing of the excitation and inhibition processes within the brain seemed to contribute to variations in brain wave frequencies and broader neural dynamics.
These findings open up new possibilities for understanding the intricate balance of activity in the human brain. By using their newly developed brain models, the team demonstrated that they could effectively capture individual variations in alpha and beta waves and make predictions about brain activity, accurately forecasting how specific patterns of brain dynamics would evolve over time.
Braver emphasized the importance of this discovery: “Our innovative approach has the potential to provide new insights into how individual variation in brain dynamics results in differences in cognitive functioning. We hope this framework may also eventually inform new ways of enhancing cognitive functioning, such as with neurostimulation.” Neurostimulation refers to the use of electrical or magnetic stimulation to modulate brain activity and is one potential avenue for future research and clinical application.
Practical Applications: Personalizing Neuroscience and Medicine
The broader implications of this research extend far beyond basic science. Personalized brain models are a significant step toward understanding the variation in brain activity, which can be particularly important in both medical and research settings. For example, in clinical environments, such models could lead to more effective and individualized treatments for conditions such as epilepsy, Parkinson’s disease, depression, and even neurological disorders caused by trauma or degeneration.
As Ching noted, “This new technique provides a powerful tool for exploring the mechanisms underlying individual brain dynamics based on noninvasive measurements of brain activity. This will allow us to advance high-level neuroscience, create precision brain models for individuals that can forecast future brain activity, and use that knowledge to inform personalized medical interventions.”
In the future, researchers and clinicians may be able to use this model not only to better understand the inherent variability in brain dynamics but also to develop methods to predict and influence cognitive outcomes. This could include devising personalized neurostimulation therapies, exploring tailored treatments to mitigate cognitive decline, or optimizing training programs that aim to enhance brain functioning in specific individuals.
Future Directions: Expansion and Refinement of the Model
This work is still in its early stages, and the team intends to continue refining and expanding the model in the coming years. According to Singh, the research represents an important starting point for a broader initiative to understand the mechanisms that underlie person-to-person differences in brain dynamics.
Braver added that a key next step in the research would involve applying the model to larger populations and exploring its potential to predict brain states and cognitive behaviors in real-world contexts. Collaborations with experts in neuroimaging, neuroscience, and psychiatry will also be essential for further development of the model’s clinical applications. The hope is that this work will not only deepen our understanding of the brain but also guide future interventions to support cognitive health and treat neurological disorders.
Ultimately, this new personalized brain model framework has the potential to revolutionize neuroscience. With its ability to explain individual differences in neural dynamics, this research brings us closer to a future where cognitive functioning is more personalized, and treatments can be better tailored to each individual’s brain.
Conclusion
The latest developments from researchers at Washington University in St. Louis offer groundbreaking insights into the individual variation of human brain dynamics. Through the creation of personalized brain models, the team has provided a powerful tool for understanding the mechanisms that drive cognitive and behavioral differences among individuals. This new approach has the potential to further the understanding of fundamental brain processes, impact both basic neuroscience and clinical practices, and guide the development of personalized treatments and therapies. As the model continues to be refined and applied in various contexts, it holds promise for transforming neuroscience research and improving individualized healthcare in the future.
Reference: 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