A new study published in Nature Neuroscience suggests that the human brain may organize its many cognitive functions by activating specific networks in a repeating, clock-like cycle. This rhythmic pattern of activity could provide a way for the brain to make sure that attention, memory, sensory processing, and other mental operations occur efficiently and without conflict. The findings may offer a new way to understand how the brain structures its activity over time, with potential implications for cognition, aging, and brain disorders.
The human brain is constantly juggling many demands. At any moment, it might need to focus attention, recall a memory, process incoming sights and sounds, or plan a movement. Despite the complexity of these functions, the brain typically handles them with speed and flexibility. Yet researchers have not fully understood how the brain ensures that all of these mental processes take place in a timely and coordinated way.
One possibility is that the brain follows a repeating pattern, cycling through the different mental functions in an organized sequence. If so, this internal rhythm could help structure the mind’s activity even in the absence of external cues. Previous studies have shown that brain networks tend to activate in certain patterns and that transitions between networks are not random. However, it has remained unclear whether these patterns are arranged into a broader, consistent cycle that includes all the major brain networks involved in cognition.
“This research was inspired by observations that transitions between functional brain networks are asymmetric: we have seen that in many cases it is much more likely that network X follows network Y than the other way around. This indicated that there may be a preferential order in which networks activate. Our objectives were to establish and quantify this organization in time,” said study author Mats W.J. van Es, a senior researcher at the University of Oxford and Extraordinary Junior Research Fellow at The Queen’s College.
To investigate this idea, the researchers analyzed data from five large studies that used magnetoencephalography, a technique that records the brain’s magnetic fields with high temporal precision. Participants in these studies were mostly healthy adults who were at rest, either with their eyes open or closed. The research team focused on identifying patterns in how large-scale brain networks became active and transitioned over time.
They applied a method called hidden Markov modeling, which identifies repeating states of brain activity based on their timing and spatial features. Each state corresponded to a functional brain network—a group of brain regions that tend to become active together. These networks have been linked to specific mental functions, such as the default mode network (linked to internal thought), the dorsal attention network (associated with attention to the outside world), and sensorimotor networks.
The researchers then developed a new analytical tool called temporal interval network density analysis. This method examined how likely one brain network was to become active before or after another network. By comparing these patterns across many intervals and participants, the researchers looked for an overall ordering in the transitions.
They found that while individual transitions were somewhat unpredictable, the average pattern revealed a consistent cycle. This cycle appeared to guide the brain through a sequence of network activations, with each network tending to occupy a specific position in the cycle. The cycle repeated approximately every 300 to 1,000 milliseconds and was observed across all five datasets, suggesting it is a robust feature of brain activity.
The results provide evidence that the brain’s large-scale networks do not activate randomly or independently. Instead, they follow a repeating sequence that spans a full set of cognitive functions. Each network was found to have a preferred phase within the cycle, meaning it was more likely to activate at a particular point in the overall sequence.
The cycle grouped networks with similar functions together. For example, networks related to internal thought and memory were located in one part of the cycle, while networks associated with external attention and sensory processing were on the opposite side. This suggests the cycle may help the brain manage different types of processing by organizing them in time.
“We’ve shown first evidence that functional brain networks follow organised temporal rules, which is a level of brain organisation that we didn’t know about before,” van Es told PsyPost. “Brain networks have been studied extensively but mostly in terms of their spatial layout (the brain areas that make up each network), and their engagement for certain behaviors, not their inherent organization in time. We think that this clock-like timing of brain networks might enable the brain to coordinate diverse cognitive functions, and make sure every task is completed within a reasonable time frame.”
The study also found that the strength and speed of the cycle varied across individuals. Some people showed stronger and faster cycles than others. These individual differences were linked to age, cognitive performance, and even genetic factors. Older adults tended to have slower but more pronounced cycles. In a sample of twins, the rate of cycling was found to be heritable, meaning it may have a genetic basis.
The researchers also explored whether these cycles played a role in behavior. In one dataset, participants engaged in a memory task. The results indicated that certain phases of the cycle were more likely to coincide with moments of spontaneous memory replay. In another dataset involving a visual task, the phase of the cycle just before a response predicted how quickly the participant would react. This suggests that the cycle is not just a passive background rhythm but may have real-time consequences for how the brain functions.
“We were surprised by how robust the cycles are,” van Es said. “The presence of the cycle and the order in which networks preferably activate was the same across datasets, and even in a task where people were bombarded with visual input. We had thought that the visual task would completely disrupt the cycle, but it did not! It was still present in the background and affected behaviour. We are now curious to find out how stable these cycles are in the same individuals doing different tasks.”
The findings suggest a new level of temporal organization in brain activity. While much research has focused on where in the brain certain functions happen, this study highlights when they occur in relation to one another. The idea that the brain moves through a regular cycle of network activity could help explain how it manages to carry out many functions smoothly and in parallel.
This cyclical pattern may offer a mechanism for balancing different mental demands. For instance, if attention and memory compete for resources, organizing them into different phases of a cycle could help prevent interference. The rhythm might also act as an internal scaffold, structuring thought even in the absence of external structure.
The study’s results also raise the possibility that disruptions in this cycle could be linked to cognitive problems. Slower or less coordinated cycles might contribute to the difficulties with memory or attention seen in aging or certain psychiatric conditions. Because the cycle rate appears to be influenced by genetics, it may also serve as a biomarker for individual differences in brain function. But more research is needed to examine this.
“In this study, we primarily set out to establish this new type of brain organization, and it remains to be seen how strongly it affects our behavior,” van Es noted. “Our initial findings do suggest that there may be links to cognitive flexibility and performance, and we are currently investigating the role of these cycles in brain disorders such as Alzheimer’s, but the size of the effects is still unknown. In the very least, we have uncovered a limit to how the brain operates.”
Another limitation is that the method used to identify the cycle assumes that only one brain network is active at any given moment. While this simplification helps with analysis, real brain activity is likely more overlapping and nuanced. Future studies could use more advanced models that allow for multiple networks to be active at once.
“The order in which functional brain networks activate is robust, but it does somewhat vary from time to time,” van Es explained. “Additionally, we here used a model that assumes only one network is active at each moment, which is a useful simplification. In reality, there is likely a complicated mix of activity in these networks. Thus, while the order of the cycle is robust, it does somewhat differ between individual cycle. This combination of order and flexibility represents a trade-off between the need for the brain to perform all its essential functions in a timely manner, while maintaining its flexibility and adaptability.”
The researchers hope to explore whether these cycles can be influenced by training, disease, or interventions. They also aim to understand whether the cycle shapes the sequence in which mental operations are carried out. For example, does the brain have a preferred order for switching from memory to perception to action? If so, this might place limits on how thoughts and behaviors unfold in time.
Over the long term, this research may lead to new ways to track or even improve cognitive function by measuring or adjusting the brain’s internal timing. Much like the sleep cycle organizes different stages of rest, this newly identified brain cycle may provide a foundational rhythm for the mind at work.
“I want to better understand the mechanisms behind the cyclical pattern and understand where this preferred activation pattern comes from,” van Es said. “At the same time, I want to investigate how these cycles may shape or even constrain behaviour: Does the preferred order of brain networks activations constrain the order of cognitive operations we can complete (like memory, perception, movement)? As in sleep, disruption of the cycles could also be an important marker of brain disorders, and cycle-related measures could inform future clinical applications.”
“One of the lead authors on this study (Dr. Cameron Higgins) has founded an Australian-based company (Resonait) that seeks to improve an existing neurostimulation treatments for depression by coordinating the stimulation timing with these cyclical patterns.”
The study, “Large-scale cortical functional networks are organized in structured cycles,” was authored by Mats W. J. van Es, Cameron Higgins, Chetan Gohil, Andrew J. Quinn, Diego Vidaurre, and Mark W. Woolrich.
