Dartmouth researchers create new template of the human brain

Understanding how the brain operates is a monumental task, and scientists have long relied on neuroimaging techniques to capture and analyze brain activity. However, analyzing this data is far from straightforward due to the unique shape of each individual’s brain. A new study from Dartmouth researchers has introduced a groundbreaking tool that promises to make this process more accurate and efficient: the “OpenNeuro Average” (onavg) cortical surface template.

This innovative template was developed using data from over 1,000 brain scans and represents a significant advancement in the field of neuroimaging. By providing a more uniform and less biased map of the brain’s surface, onavg allows researchers to obtain better results with less data. This could be particularly valuable in studies where data collection is challenging, such as research involving rare diseases.

“Our cortical surface template, onavg, is the first to sample different parts of the brain uniformly,” said lead author Feilong Ma, a postdoctoral fellow and member of the Haxby Lab in the Department of Psychological and Brain Sciences at Dartmouth. “It’s a less biased map that is more computationally efficient.”

The findings are published in Nature Methods.

Neuroscientists often face the challenge of comparing brain activity data across different individuals. Since each person’s brain has a slightly different shape, researchers need to align these differences to a common reference, or template, to analyze the data effectively. For over 25 years, scientists have used various cortical surface templates to accomplish this. These templates map brain activity onto a model of the brain’s surface, helping researchers pinpoint where certain functions are located across different individuals.

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However, the most commonly used templates are based on data from only 40 brains and have significant limitations. These older templates often sample different parts of the brain unevenly, leading to biases in data analysis. For example, some areas of the brain might be overrepresented, while others are underrepresented, which can skew research results. Additionally, these templates typically rely on a spherical model of the brain, which distorts the natural shape of the brain and further complicates the analysis.

To address these issues, the Dartmouth team sought to create a new template that would offer more accurate and uniform sampling of the brain’s surface. They aimed to reduce biases in data analysis and improve the efficiency of neuroimaging studies, making it easier to replicate and validate research findings across different studies.

The team began by collecting data from 1,031 brain scans, drawn from 30 different datasets available on OpenNeuro, an open-source platform for sharing neuroimaging data. This large and diverse sample set allowed them to create a more representative model of the human brain than previous templates, which were based on much smaller sample sizes.

To build the onavg template, the researchers focused on accurately mapping the geometric shape of the brain’s surface. Unlike older templates that relied on a spherical approximation, onavg was designed to more closely follow the natural contours of the brain. The team optimized the placement of data points—known as “vertices”—across the brain’s surface to ensure they were distributed evenly. This uniform distribution of vertices means that every part of the brain is sampled with equal precision, reducing the biases present in older models.

The researchers then tested the new template using a variety of methods to ensure its accuracy and efficiency. They compared the performance of onavg with that of older templates in different scenarios, such as analyzing brain activity while participants watched movies. The results consistently showed that onavg provided more accurate and reliable data with less computational effort.

The onavg template significantly outperformed older models in several key areas. First, it provided more accurate mapping of brain activity, particularly in areas that were previously underrepresented in older templates. This means that researchers can now get a clearer and more accurate picture of how different parts of the brain function.

Second, onavg was more efficient. The template required less data to produce reliable results, which is a major advantage in neuroimaging studies. Collecting brain imaging data can be costly and time-consuming, and in some cases, such as studies on rare diseases, it can be difficult to gather enough data. With onavg, researchers can achieve the same level of accuracy with fewer data points, making studies more feasible and cost-effective.

Moreover, the uniform distribution of vertices in onavg reduced the computational time needed to analyze brain data. This is particularly important in large-scale studies where data processing can be a significant bottleneck. By speeding up these processes, onavg can help researchers analyze data more quickly and efficiently, enabling faster scientific discoveries.

Finally, the study demonstrated that onavg could improve the replicability and reproducibility of neuroimaging research. One of the biggest challenges in science today is ensuring that findings can be replicated in different studies. The more efficient data usage and reduced biases offered by onavg increase the likelihood that results from one study can be replicated in another, strengthening the overall reliability of neuroscientific research.

While the onavg template represents a significant advancement, it is not without limitations. One of the main limitations is that the template, like all models, is still an approximation. Although it is based on a large and diverse dataset, there are inherent variations in individual brains that may not be fully captured by any single template. As a result, while onavg reduces bias, it does not eliminate it entirely.

Another limitation is that onavg was tested primarily in specific types of neuroimaging studies, such as those involving movie-watching. While the results were positive, it remains to be seen how well the template performs across a broader range of tasks and in different populations, such as older adults or individuals with neurological disorders. Future research will be needed to validate the template in these contexts and to explore its potential applications in clinical settings.

Additionally, the development of the onavg template relied heavily on data from the OpenNeuro platform, which primarily includes brain scans from healthy individuals. Future research could benefit from including more diverse populations, such as individuals with various neurological conditions, to ensure that the template is as broadly applicable as possible.

In terms of future directions, the researchers are optimistic that the onavg template will have a broad impact across many areas of neuroscience. They suggest that it could be particularly useful in studies of vision, hearing, language, and individual differences in brain function, as well as in research on neurological disorders like autism and neurodegenerative diseases such as Alzheimer’s and Parkinson’s.

The team has made the onavg template freely available to the scientific community, encouraging other researchers to adopt it in their work. By doing so, they hope to promote more accurate, efficient, and replicable neuroimaging research, ultimately leading to a better understanding of the human brain.

“It’s very expensive to obtain data through neuroimaging and for some clinical populations— such as if you’re studying a rare disease—it can be difficult or impossible to acquire a large amount of data, so the ability to access better results with less data is an asset,” explained Feilong. “With more efficient data usage, our template can potentially increase the replicability and reproducibility of results in academic studies.”

“I think that onavg represents a methodological advancement that has broad applications across all aspects of cognitive and clinical neuroscience,” added co-author James Haxby, a professor in the Department of Psychological and Brain Sciences and former director of the Center for Cognitive Neuroscience at Dartmouth.

The study, “A cortical surface template for human neuroscience,” was authored by Ma Feilong, Guo Jiahui, Maria Ida Gobbini, and James V. Haxby.