Summary: Averaging brain scan data across groups can fundamentally mislead scientists about how the human brain actually works. By analyzing functional MRI data from over 4,000 children individually, researchers discovered that children, particularly those struggling with inhibitory control, display unique brain dynamics that are often the exact opposite of “group average” patterns.
This study challenges decades of conventional neuroscience and provides a new theoretical foundation for personalized psychiatry and the treatment of conditions like ADHD.
Key Facts
- The Speed-Accuracy Paradox: Just as a group’s fastest arithmetic solvers are often the most accurate, but an individual slows down to gain accuracy, brain patterns behave differently in a group context than they do within a single person’s temporal dynamics.
- Default Mode Network (DMN) Flip: At the group level, slow reaction times are linked to increased activity in the DMN (the “mind-wandering” center). However, in individuals, a slow reaction time actually triggers a decrease in DMN activity.
- Proactive vs. Reactive Control: The study identifies that cognitive control isn’t a single “muscle” but a symphony of sub-processes, including proactive control (preparing to stop) and reactive control (the act of stopping).
- Multiple Pathways to Success: Children with weaker cognitive control often use alternate neural pathways to compensate, suggesting that inhibitory control is a dynamic skill that can be regulated or learned in multiple ways rather than a fixed capacity.
Source: Stanford
Studying cognition by averaging data from many people’s brain scans hides how individuals use their brains, new Stanford Medicine research has shown.
In particular, children who struggle with goal-oriented tasks show distinct patterns of brain activity when their data is analyzed individually, rather than as part of a group of kids with mixed abilities.
The findings, which have implications for understanding how the brain works in such conditions as attention-deficit/hyperactivity disorder, will be published April 27 in Nature Communications.
“Investigating how dynamics unfold within individual brains can provide significant insights into the neuroscience of individual differences and help us tackle questions that cannot be answered using conventional approaches,” said Percy Mistry, PhD, a research scholar in psychiatry and behavioral sciences, and a lead author of the study.
Mistry shares lead authorship with Nicholas Branigan, MS, a research data analyst in psychiatry and behavioral sciences. The senior author is Vinod Menon, PhD, a professor of psychiatry and behavioral sciences and the Rachael L. and Walter F. Nichols, MD, Professor.
The research evaluated inhibitory cognitive control — the process by which the brain suppresses distracting or irrelevant information while someone completes a task — in more than 4,000 children.
The researchers compared results obtained by averaging brain-scan data of children against results obtained by analyzing the temporal dynamics in each child as they performed repetitions of the same task.
“Our study provides theoretical support for a growing movement toward personalization in human neuroscience, psychology and psychiatry,” Branigan said.
This approach was also able to identify subgroups of children with different levels of cognitive control and performance monitoring, or the ability to modify one’s strategy after making an error.
For example, children with good cognitive control and performance monitoring and those with poor cognitive control and performance monitoring showed quite different — and often opposite — brain dynamics.
Noting that studies connecting behavior to brain activity typically draw their conclusions from averaging groups’ data, Menon said, “Our study clearly shows that group averages can fundamentally mislead us about how the brain dynamically regulates behavior.”
A clue from the speed-accuracy trade-off
Psychologists have long known that behaviors that seem linked in a certain way when you study them in groups may not be related in the same way in individuals. The best example is the speed-accuracy trade-off: If you ask a group of people to do a task such as quickly solving arithmetic problems, the faster people tend to be more accurate.
However, if you ask one individual to go faster, their accuracy will likely decline — or, if you ask one person to be more accurate, they’ll probably slow down.
Experts have wondered if a similar phenomenon plays out in hidden ways inside the brain.
The Stanford Medicine team looked at brain scan data from kids doing a task that measures their inhibitory control. To focus on a job or goal, a person must suppress the urge to pay attention to distractions and things that aren’t relevant while inhibiting actions or impulsive behaviors that are contradictory to reaching their goal.
Inhibitory control gets the job done. Poor inhibitory control is a hallmark of several psychiatric diagnoses, including ADHD, bipolar disorder and addiction. Understanding how inhibitory control normally works — and how it goes awry — could help guide the development of better behavioral therapies for these conditions.
Individual results going a different way
The research team analyzed data from more than 4,000 children, all 9 or 10 years old, that was collected as part of the baseline visit for the Adolescent Brain and Cognitive Development study, a long-term study tracking brain maturation into early adulthood.
The children’s brains were scanned via functional magnetic resonance imaging while they completed an activity designed to assess inhibitory control. Called the stop-signal task, the activity consists of pressing a button in response to prompts on a screen.
Every time the child saw “Go” on the screen, they were asked to press the button as quickly as possible. Occasionally, the “Go” sign was immediately followed by an additional “Stop” sign. The children were supposed to avoid pressing the button when they saw this infrequent, unpredictable Stop cue.
The research team examined aspects of what the brain was doing during the task on every trial — both when comparing the children with one another and when analyzing several repetitions of the task by the same person.
The researchers found several brain-behavior links that were different within individuals than in the group as a whole.
For instance, when analyzing average trends in groups of children, slower reaction times to the “Go” signal were linked to increased activity in many brain regions, including the default mode network, which is involved in daydreaming, thoughts about oneself and mind-wandering.
However, when an individual had a slower reaction time to the “Go” signal, activity decreased in the default mode network — the opposite of the group-level pattern.
“Group-level associations substantially mischaracterize the neural dynamics governing processing speed at the individual level,” the research team wrote.
The researchers also developed a mathematical model that enabled them to study how the children adapted their reactions during several repetitions of the stop-signal task.
Children with adaptive (good) regulation showed faster stopping reactions as they got further past the first “Stop” signal, correctly anticipating that each subsequent trial was more likely to be another “Stop.”
Children with maladaptive reactions showed the opposite pattern, indicating decreasing expectancy of a second “Stop” signal. This difference shows up in the scans, manifesting as opposite reactions in activity in specific brain areas among the children with adaptive and maladaptive regulation.
The analysis also showed that some effects seen in the entire group of children were driven solely by children in one of the groups; in other words, the average results obscured what happened in many children’s brains.
Various pathways
The researchers also found that cognitive control has multiple components, which are orchestrated by different parts of the brain, including proactive control (preparing to stop) and reactive control (actually stopping). The brain regions used in these sub-processes are not always talking to each other in the same way in kids with stronger versus kids with weaker cognitive control.
“When children are weaker at cognitive control, they may be able to compensate for that with a more proactive approach or an alternate cognitive pathway,” Mistry said. “That’s interesting because it moves the dialogue away from cognitive control being a static capacity that children have, to something that can perhaps be regulated or driven in multiple ways.”
The findings may prove useful in designing new approaches to help children with ADHD improve in the types of behavior regulation that are hard for them, Mistry added. “If you’re looking at strategies in the classroom, this data points to the fact that inhibitory cognitive control is not a single capacity. There are multiple pathways involved, and perhaps students can learn to engage specific pathways to be more proactive about their inhibitory control approach,” he said.
Not only does the study open new possibilities for understanding human variability in brain function, but it should encourage neuroscientists to examine how each person reacts to specific situations, Menon added.
“We really have to pay attention to each person’s unique brain responses, because we’re trying to understand, and if necessary modify, behavior as it unfolds in real time for specific situations,” he said.
“There is no such thing as an average brain. The real question is, ‘How is this child or this adult responding to the particular situations and changing contexts that demand attention and adaptive regulation of behavior?’ That’s what cognitive control is — knowing what response to take, at what time and under what circumstances.”
The data for the Adolescent Brain and Cognitive Development study is held in the National Institute of Mental Health Data Archive.
Funding: The ABCD study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html.
At Stanford Medicine, this work was supported by the National Institutes of Health (grants MH121069 and MH124816), the National Science Foundation (grant 2024856), and the Stanford Maternal and Child Health Research Institute. In addition, Stanford University and Stanford Research Computing provided computational resources and support that contributed to the research results.
Key Questions Answered:
A: Averaging creates a “composite” brain that doesn’t actually exist in nature. It’s like taking the average clothes size of 100 people and finding it’s a “Medium,” then being surprised when that size doesn’t perfectly fit the “Small” or “Extra-Large” individuals in the group. In neuroscience, this means we’ve been missing the specific ways children with ADHD or bipolar disorder actually process information.
A: Instead of viewing a child as simply having “poor focus,” clinicians can identify which specific pathway is struggling. For example, if a child is bad at reactive stopping but good at proactive preparation, classroom strategies can be tailored to lean on their strengths rather than trying to fix a “broken” average capacity.
A: Not wrong, but incomplete. Group studies are great for finding general trends, but they are poor at predicting how a specific person will react to a specific situation. This study acts as a “corrective lens,” allowing scientists to see the high-resolution reality of individual differences.
Editorial Notes:
- This article was edited by a Neuroscience News editor.
- Journal paper reviewed in full.
- Additional context added by our staff.
About this neuroscience and ADHD research news
Author: Erin Digitale
Source: Stanford
Contact: Erin Digitale – Stanford
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Nonergodicity and Simpson’s paradox in neurocognitive dynamics of cognitive control” by Percy K. Mistry, Nicholas K. Branigan, Zhiyao Gao, Weidong Cai & Vinod Menon. Nature Communications
DOI:10.1038/s41467-026-71404-0
Abstract
Nonergodicity and Simpson’s paradox in neurocognitive dynamics of cognitive control
Nonergodicity and Simpson’s paradox present significant, yet underappreciated challenges in cognitive neuroscience.
Leveraging brain imaging and behavioral data from over 4000 individuals and a Bayesian computational model of cognitive dynamics, we investigated brain-behavior relationships underlying cognitive control at both between-subjects and within-subjects levels.
Strikingly, brain-behavior associations reversed across levels of analysis, revealing pervasive nonergodicity. Within-subjects analysis uncovered dissociated neural representations of reactive and proactive control and revealed that individuals who adaptively versus maladaptively regulated cognitive control exhibited distinct brain-behavior associations.
Our findings demonstrate that between-subjects analyses can fundamentally mischaracterize within-individuals mechanisms, as group-level patterns not only disagreed with individual-level patterns but often reversed them.
This work highlights the necessity of distinguishing between-subjects and within-subjects inferences in neuroscience, with implications for understanding cognitive mechanisms and designing personalized interventions.
