To better understand the brain, take a look at the bigger picture

Summary: By zooming out to image larger areas of the brain while using fMRI technology, researchers can capture additional relevant information, offering a better understanding of neural interaction.

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Researchers have learned a great deal about the human brain through functional magnetic resonance imaging (fMRI), a technique that can shed light on brain function. But typical fMRI methods can miss key information and provide only part of the image, the Yale researchers say.

In a new study, they evaluated various approaches and found that zooming out and taking in a wider field of view captures additional relevant information that a narrow focus misses, offering greater insight into neural interaction.

In addition, these broader results may help address the reproducibility issue of neuroimaging, in which some findings presented in studies cannot be reproduced by other investigators.

The findings were published August 4 in Proceedings of the National Academy of Sciences.

Studies using fMRI generally focus on small areas of the brain. As an example of this approach, the researchers look for brain regions that become more “active” when a particular activity is performed, focusing on small areas with the strongest activation. But a growing body of evidence shows that brain processes, and complex processes in particular, are not limited to small parts of the brain.

“The brain is a network. It’s complex,” said Dustin Scheinost, associate professor of radiology and biomedical imaging and lead author of the study. Oversimplifying, he said, leads to inaccurate conclusions.

“For more sophisticated cognitive processes, it is unlikely that many areas of the brain are not involved,” added Stephanie Noble, a postdoctoral associate in Scheinost’s lab at Yale School of Medicine and lead author of the study.

Focusing on small areas leaves out other regions that may be involved in the behavior or process under study, which may also affect the direction of future research.

“You develop this incorrect picture of what’s really going on in the brain,” he said.

For the study, the researchers tested how well fMRI scans on a variety of scales were able to detect effects or changes in fMRI signals as participants performed different activities, revealing which parts of the brain are involved.

They used data from the Human Connectome Project, which has collected brain scans of individuals as they performed different tasks related to complex processes such as emotion, language, and social interactions.

The research team looked for effects in very small parts of the brain network, such as connections between just two areas, and also in groups of connections, generalized networks, and entire brains.

They found that the larger the scale, the better they could detect the effects. This ability to detect effects is known as “power”.

“We get better power with these methods on a larger scale,” Noble said.

At the smallest scales, the researchers were only able to detect about 10% of the effects. But at the network level, they were able to detect more than 80% of them.

The trade-off for the extra power was that the larger views did not convey spatially accurate information like the smaller-scale analyses. For example, at the smallest scale, the researchers could safely say that the effects they observed occurred throughout the small area.

However, at the network level, they could only say that the effects were happening across much of the network, not pinpoint exactly where in the network.

The goal, Noble says, is to balance the benefits and drawbacks of the various methods.

“Would you rather have a lot of confidence in a small piece of relevant information; in other words, get a very clear picture of just the tip of the iceberg?” she said.

“Or would you prefer to have a really big picture of the whole iceberg that is maybe a little fuzzy but gives you a sense of the complexity and the broad spatial scale of where things happen in the brain?”

For other researchers, this approach is simple to implement, and Noble said he looks forward to seeing how other scientists use it.

This shows a brain made of cogwheels.
In addition, these broader results may help address the reproducibility issue of neuroimaging, in which some findings presented in studies cannot be reproduced by other investigators. The image is in the public domain

She points out that the fields of psychology and neuroscience, including neuroimaging, have faced a problem of reproducibility. And low power in fMRI scans contributes to this: low power studies only uncover small portions of the story, which can be seen as contradictory rather than parts of a whole.

Increasing the power of fMRI, as she and her colleagues did here by scaling up their analyses, could be one way to address reproducibility challenges by exposing how seemingly contradictory results can, in fact, be harmonious.

“Moving up the food chain, so to speak, going from very low level to more complex networks gives you a lot more power,” Scheinost said. “This is one of the tools we can use to help with the reproducibility issue.”

See also

This shows a man playing a banjo.

And scientists shouldn’t throw the baby out with the bathwater, Noble said. There is a lot of good work being done to improve methods and increase stringency, and fMRI continues to be a valuable tool, he said, “I think assessing power, stringency and reproducibility is healthy for any field. Especially one that deals with the complexity of living things and mental processes.”

Noble is now developing a “power calculator” for fMRI, to help others design studies in a way that achieves the desired level of power.

About this neuroimaging research news

Author: Mallory Locklear
Font: yale
Contact: Mallory Locklear-Yale
Image: The image is in the public domain.

original research: Open access.
“Improving power in functional magnetic resonance imaging by going beyond group-level inference” by Stephanie Noble et al. PNAS


Improving power in fMRI by going beyond group-level inference

Neuroimaging inference typically occurs at the level of focal brain areas or circuits. Increasingly, however, well-conducted studies paint a much richer picture of large-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects.

How focal versus large-scale perspectives influence the inferences we make has not yet been thoroughly evaluated using real data.

Here, we compare sensitivity and specificity between procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks). , 3 resampling group sizes, 7 inferential procedures).

Only the large-scale procedures (network and whole brain) achieved the traditional 80% power level to detect an average effect, reflecting >20% more statistical power than the focal procedures (edge ​​and cluster). Power was also substantially increased for the false discovery rate, compared to the family error rate control procedures.

The downsides are pretty limited; the loss in specificity for the large-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the large-scale methods we present are simple, fast, and easy to use, providing an easy starting point for researchers.

This also points to the promise of more sophisticated large-scale methods not only for functional connectivity but also for related fields, including task-based activation.

Taken together, this work demonstrates that changing the inference scale and choosing the FDR control can be accomplished immediately and can help remedy problems with statistical power that plague typical studies in the field.

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