Periodic Reporting for period 1 - BeyondMapping (Beyond mapping of the human brain: causal deconstruction of brain mechanisms underlying complex social behaviors)
Reporting period: 2023-06-01 to 2025-11-30
Understanding how the human brain processes social information is one of the most important and least understood challenges in neuroscience. Our ability to read emotions, follow someone’s gaze, understand a joke, or sense discomfort in a room are examples of social cognition, complex abilities that help us navigate the world. Yet science still struggles to explain how these processes work at the level of brain networks, and why different people vary so much in these abilities.
At the same time, difficulties in social processing are central to many psychiatric and developmental conditions, including autism and social anxiety. If we could identify the brain networks that support social cognition and determine how they differ between individuals, we could move closer to creating personalized tools for diagnosis and intervention.
The BeyondMapping project sets out to tackle this challenge using cutting-edge tools in neuroscience. Its goal is to go beyond simply identifying which brain regions are involved in social processing (“mapping”), and instead understand how different brain networks interact, how these interactions within different regions of the brain vary across individuals, and how these differences in the networks causally affect behavior.
To do this, the project follows a three-pronged approach:
1. Develop behavioral tasks that isolate specific elements of social behavior, like detecting emotions or predicting actions.
2. Use ultra-high field MRI to examine how individual brain networks relate to performance on these tasks, giving us greater power and resolution to understand this interaction.
3. Apply real-time feedback on brain activity (neurofeedback) to test whether changing brain activity in a specific network leads to a change in behavior, allowing us to test for causality.
Together, these steps will help answer fundamental questions about how the brain supports social abilities and why people differ in these skills. The findings could lead to better tools for assessing social functioning and new, non-invasive treatments for people with social processing difficulties.
At the same time, difficulties in social processing are central to many psychiatric and developmental conditions, including autism and social anxiety. If we could identify the brain networks that support social cognition and determine how they differ between individuals, we could move closer to creating personalized tools for diagnosis and intervention.
The BeyondMapping project sets out to tackle this challenge using cutting-edge tools in neuroscience. Its goal is to go beyond simply identifying which brain regions are involved in social processing (“mapping”), and instead understand how different brain networks interact, how these interactions within different regions of the brain vary across individuals, and how these differences in the networks causally affect behavior.
To do this, the project follows a three-pronged approach:
1. Develop behavioral tasks that isolate specific elements of social behavior, like detecting emotions or predicting actions.
2. Use ultra-high field MRI to examine how individual brain networks relate to performance on these tasks, giving us greater power and resolution to understand this interaction.
3. Apply real-time feedback on brain activity (neurofeedback) to test whether changing brain activity in a specific network leads to a change in behavior, allowing us to test for causality.
Together, these steps will help answer fundamental questions about how the brain supports social abilities and why people differ in these skills. The findings could lead to better tools for assessing social functioning and new, non-invasive treatments for people with social processing difficulties.
Since the start of the project, we have made strong progress in each of the core areas of work.
First, we developed a reliable and flexible battery of behavioral tasks that measure different types of social and non-social processing, such as gaze-following, biological motion prediction, and understanding emotions. These tasks are specifically designed to separate out the “social” aspects of behavior from more general cognitive factors, allowing us to better identify what makes social information processing unique.
Second, we used ultra-high field (7 Tesla) MRI scanning to study how brain networks of individuals relate to these social behaviors. We found that people who behave similarly on certain social tasks tend to show similar brain activity in specific networks, particularly in regions already known to support social understanding (like the superior temporal sulcus and medial prefrontal cortex). However, we also discovered that these relationships are often domain-specific, meaning there may not be a single “social brain,” but multiple systems involved depending on the context.
Third, we developed an innovative virtual reality (VR) system to study how people respond to anxiety-inducing situations. In this environment, we measure both physiological reactions (like heart rate and sweating) and subjective feelings of fear. Using this approach, we have been able to separate the brain pathways involved in automatic responses to fear (e.g. increased heart rate) from those involved in more cognitive, subjective experiences of anxiety, i.e. the story we tell ourselves about what we are experiencing, offering a more detailed picture of how social and emotional processes interact.
In addition, we published a methodological breakthrough: a new mathematical model to assess the reliability of behavioral tasks when used to measure individual differences. This model helps researchers determine how much data is needed to get meaningful results and has already been adopted by other scientists worldwide through an open-access web tool.
First, we developed a reliable and flexible battery of behavioral tasks that measure different types of social and non-social processing, such as gaze-following, biological motion prediction, and understanding emotions. These tasks are specifically designed to separate out the “social” aspects of behavior from more general cognitive factors, allowing us to better identify what makes social information processing unique.
Second, we used ultra-high field (7 Tesla) MRI scanning to study how brain networks of individuals relate to these social behaviors. We found that people who behave similarly on certain social tasks tend to show similar brain activity in specific networks, particularly in regions already known to support social understanding (like the superior temporal sulcus and medial prefrontal cortex). However, we also discovered that these relationships are often domain-specific, meaning there may not be a single “social brain,” but multiple systems involved depending on the context.
Third, we developed an innovative virtual reality (VR) system to study how people respond to anxiety-inducing situations. In this environment, we measure both physiological reactions (like heart rate and sweating) and subjective feelings of fear. Using this approach, we have been able to separate the brain pathways involved in automatic responses to fear (e.g. increased heart rate) from those involved in more cognitive, subjective experiences of anxiety, i.e. the story we tell ourselves about what we are experiencing, offering a more detailed picture of how social and emotional processes interact.
In addition, we published a methodological breakthrough: a new mathematical model to assess the reliability of behavioral tasks when used to measure individual differences. This model helps researchers determine how much data is needed to get meaningful results and has already been adopted by other scientists worldwide through an open-access web tool.
This project goes beyond the current state of the art in cognitive neuroscience in several important ways:
1. New tools for measuring social behavior. One of the greatest needs in the field of social neuroscience at the moment, are better tools to measure social abilities, that capture differences between individuals even within the general population. We developed behavioral tasks that are reliable enough to capture meaningful individual differences, and can separate social from non-social influences on performance. This allows us to narrow down the networks specifically involved in social processing, a crucial step for understanding why there are such large differences between individuals in these abilities.
2. A formal framework for behavioral reliability. Our published model and free online tool give researchers across different fields the ability to plan and validate their studies more effectively. This is important for improving reproducibility in psychology and neuroscience.
3. Real-time neurofeedback as a non-invasive tool for basic science research. Following our previous work which showed that neurofeedback can be used to modulate brain networks, we are planning to use covert neurofeedback to test whether changing specific brain networks associated with social behavior or anxiety can change these behaviors. If successful, this approach will not only give us invaluable insights into the mechanisms involved, but could also lead to personalized therapies.
4. Integration of neuroscience with virtual reality and physiological measurements. We have built VR based environments to probe fear responses in real-world like situations. Our experiments combine subjective reporting of the experience of fear, physiological monitoring, and brain imaging. This interdisciplinary approach is crucial for studying emotional and social experiences as they unfold naturally.
Key next steps include:
• Expanding the study to additional populations, including individuals with autism spectrum disorder.
• Longitudinal tracking to examine how brain and behavior change over time.
• Collaboration with patients and clinicians to best translate neurofeedback and behavioral tools into applied settings.
Together, results from this project will provide us with new insights into the brain’s role in social behavior, with implications both for basic science, as well as potential clinical applications.
1. New tools for measuring social behavior. One of the greatest needs in the field of social neuroscience at the moment, are better tools to measure social abilities, that capture differences between individuals even within the general population. We developed behavioral tasks that are reliable enough to capture meaningful individual differences, and can separate social from non-social influences on performance. This allows us to narrow down the networks specifically involved in social processing, a crucial step for understanding why there are such large differences between individuals in these abilities.
2. A formal framework for behavioral reliability. Our published model and free online tool give researchers across different fields the ability to plan and validate their studies more effectively. This is important for improving reproducibility in psychology and neuroscience.
3. Real-time neurofeedback as a non-invasive tool for basic science research. Following our previous work which showed that neurofeedback can be used to modulate brain networks, we are planning to use covert neurofeedback to test whether changing specific brain networks associated with social behavior or anxiety can change these behaviors. If successful, this approach will not only give us invaluable insights into the mechanisms involved, but could also lead to personalized therapies.
4. Integration of neuroscience with virtual reality and physiological measurements. We have built VR based environments to probe fear responses in real-world like situations. Our experiments combine subjective reporting of the experience of fear, physiological monitoring, and brain imaging. This interdisciplinary approach is crucial for studying emotional and social experiences as they unfold naturally.
Key next steps include:
• Expanding the study to additional populations, including individuals with autism spectrum disorder.
• Longitudinal tracking to examine how brain and behavior change over time.
• Collaboration with patients and clinicians to best translate neurofeedback and behavioral tools into applied settings.
Together, results from this project will provide us with new insights into the brain’s role in social behavior, with implications both for basic science, as well as potential clinical applications.