Multiple brain scans merging into a clear image.

Unlock Your Brain's Potential: How to Get Clearer Insights from Brain Scans

Beau Callahan in Health & Wellness December 2025 • 5 min read.

"Tired of confusing brain scan results? Discover a new statistical method that combines multiple scans for a more stable and reliable picture of your brain's activity."

Understanding how our brains are organized is key to understanding cognition. However, measuring brain activity is tricky, and the tools we use often give slightly different results each time. This is especially true for methods like functional connectivity (FC) estimations, which try to map how different brain regions communicate with each other.

Imagine you're trying to take a picture of a fast-moving object. Each photo might be a little blurry, but if you combine many photos, you can get a clearer image. Similarly, in brain imaging, we can acquire multiple 'snapshots' of brain activity, but need a way to combine them effectively.

This article explores a new way to deal with this challenge. We'll dive into a statistical technique that combines multiple FC estimations to create a more stable and reliable picture of brain function. This approach can help researchers uncover meaningful relationships between brain activity and behavior, even when individual brain scans are a bit noisy.

The Power of Combining Brain Scans: Unveiling the Underlying Truth

Multiple brain scans merging into a clear image.

The central idea is to not rely on just one brain scan, or 'replication,' but to combine many of them. This is especially useful when the process of estimating brain activity involves some randomness, such as when using complex computer algorithms with different starting points. Each run of the algorithm might give a slightly different answer, but they all contain valuable information.

The researchers use a method called Non-Parametric Combination (NPC) to combine the results from these multiple scans. Here’s how it works:

Collect Multiple Scans: Obtain several estimates of functional connectivity for each person. These estimates might come from different runs of a brain imaging analysis or from different data sets acquired at different times.
Test for Association: For each scan, test whether there's a relationship between the estimated brain activity and some behavior or trait you're interested in (e.g., memory performance, personality score).
Combine the Results: Instead of picking the “best” scan, combine the results from all the scans using the NPC algorithm. This algorithm cleverly combines the p-values (measures of statistical significance) from each individual test.
Permutation Testing: NPC uses permutation testing to determine whether the combined result is statistically significant. This involves randomly shuffling the data and repeating the analysis many times to see how often you get a result as strong as the one you observed.

By combining the information from multiple scans, this approach can boost the statistical power to detect real relationships between brain activity and behavior. It's like combining multiple blurry photos to create a sharper image – the noise cancels out, and the underlying signal becomes clearer.

The Future of Brain Imaging: Towards More Reliable Insights

This new approach has the potential to improve the reliability and sensitivity of brain imaging studies. By combining multiple estimations of brain activity, researchers can gain a more stable and accurate picture of how the brain works and how it relates to behavior.

One of the exciting aspects of this method is that it can be applied to various types of brain imaging data and analysis techniques. Whether you're using fMRI, EEG, or MEG, and whether you're interested in static or dynamic connectivity, the NPC algorithm can help you extract more reliable insights from your data.

As the field of neuroscience moves towards larger and more complex datasets, techniques like this will become increasingly important for dealing with noisy data and extracting meaningful signals. By embracing the variability in brain imaging data, we can unlock new discoveries about the human brain and its connection to our thoughts, feelings, and behaviors.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1002/hbm.24442, Alternate LINK

Title: Stable Between‐Subject Statistical Inference From Unstable Within‐Subject Functional Connectivity Estimates

Subject: Neurology (clinical)

Journal: Human Brain Mapping

Publisher: Wiley

Authors: Diego Vidaurre, Mark W. Woolrich, Anderson M. Winkler, Theodoros Karapanagiotidis, Jonathan Smallwood, Thomas E. Nichols

Published: 2018-10-25

Everything You Need To Know

What is the fundamental idea behind using multiple brain scans to understand brain function?

The core concept involves acquiring multiple functional connectivity estimations for each subject instead of relying on a single brain scan. These estimations might arise from diverse runs of a brain imaging analysis or datasets gathered at varying times. This collection of scans is then synthesized using a method like Non-Parametric Combination to enhance the reliability of observed brain activity patterns.

How does the Non-Parametric Combination (NPC) algorithm work to combine the results from multiple brain scans?

The Non-Parametric Combination (NPC) algorithm cleverly synthesizes p-values from individual tests conducted on multiple brain scans. It employs permutation testing to ascertain the statistical significance of the combined outcome. This method shuffles the data randomly and replicates the analysis numerous times, assessing how frequently a result as robust as the observed one occurs. If this happens rarely, we can conclude that the observed association is unlikely to be due to chance, and therefore statistically significant.

How does this statistical method improve the reliability of brain imaging compared to using a single brain scan?

The statistical method addresses the inherent variability in brain imaging by combining multiple estimations of functional connectivity. By leveraging Non-Parametric Combination (NPC), the approach reduces the impact of noise and random variations present in individual scans. This leads to a more robust and reliable assessment of how brain regions communicate with each other, increasing the likelihood of identifying meaningful relationships between brain activity and behavior.

What are the practical implications of using this method for understanding the relationship between brain activity and behavior?

This approach boosts the statistical power to detect actual relationships between brain activity and behavior. The Non-Parametric Combination (NPC) method effectively cancels out noise and enhances the underlying signal by integrating information from multiple scans. For instance, this is beneficial when studying the connection between brain activity and cognitive functions, like memory performance or personality traits. This method provides a clearer and more reliable insight into the brain's functional connectivity.

What aspects of brain imaging are not directly addressed by combining multiple functional connectivity estimations, and what future improvements can build upon this approach?

While functional connectivity estimations, when combined with Non-Parametric Combination (NPC), offer enhanced reliability, it's important to note that this method primarily focuses on statistical analysis and data combination. It doesn't fundamentally alter the spatial resolution or correct for artifacts within the original brain scans themselves. Thus, limitations in the initial data acquisition (e.g., scanner limitations, participant movement) can still influence the final results. Future advancements might integrate artifact correction techniques alongside the Non-Parametric Combination to achieve even greater accuracy in brain imaging studies.