The Brain's Hidden Rhythm: How Neural Networks Cycle Through States in Under a Second

Fred Shaffer
Dec 4, 2025
9 min read

Updated: Dec 8, 2025

This post summarizes findings by van Es and colleagues (2025) on the orderly cycling of large-scale neural networks.

The Brain's Hidden Rhythm

Imagine you're sitting quietly, doing nothing in particular. In that single second of stillness, your brain is tracking the hum of the air conditioner, monitoring the position of your limbs, holding onto the vague thought that you should start dinner soon, and preparing your hand to reach for your phone. All of this happens without any obvious conductor keeping time. How does the brain coordinate such complexity without descending into chaos?

A new study offers a surprising answer: your brain may be running on a hidden clock, cycling through its major functional networks in a consistent order roughly once per second.

The Scheduling Problem

Neuroscientists have long known that the brain organizes itself into large-scale functional networks, collections of regions that tend to fire together and handle specific types of processing. You've probably heard of some of these. The default mode network activates when we turn inward, daydreaming or reflecting on ourselves. The dorsal attention network kicks in when we focus on the external world. Other networks handle sensory processing, motor control, and executive function.

What remained mysterious was how the brain decides when to engage each network. We knew the switching wasn't random. But was there a deeper pattern, something like a master schedule that ensures every network gets its turn?

A research team set out to answer this question using magnetoencephalography, a brain imaging technique that captures the tiny magnetic fields generated by neural activity. Unlike a functional MRI (fMRI), which takes snapshots every second or two, MEG can track brain changes at the millisecond level. This speed turned out to be essential, because the pattern the researchers discovered operates faster than anyone expected.

Finding the Hidden Cycle

The challenge was that you can't see networks directly in raw brain recordings. The researchers needed a way to identify when different networks were active. They turned to a statistical technique called a hidden Markov model, which works something like this: imagine the brain has a limited vocabulary of configurations it can assume, like a small set of recurring poses. The model learns to recognize these poses from the data, then labels each moment in time with whichever pose the brain most resembles.

Using this approach, the team identified 12 distinct brain states, each representing a different network configuration. Now came the key question: Do these states follow a predictable order?

Testing for a cycle is trickier than it sounds. A cycle is a repeatable large-scale ordering of brain network states in which the system progresses through a full sequence of states in a consistent directional flow over 300–1,000 ms. The brain doesn't march through states like a soldier on parade. It meanders, backtracks, and takes detours. Any method that demands perfect sequential ordering would miss the forest for the trees. So the researchers developed a clever approach called temporal interval network density analysis (TINDA).

Here's the intuition. Pick any brain state as your reference point, call it state 1. Now find every moment when the brain entered state 1, and look at what happened between that moment and the next time it returned to state 1. Split that interval in half. For every other state, ask a simple question: does it tend to show up more in the first half of the interval or the second half? If state 5 consistently appears late in these intervals, that suggests it tends to follow state 1. If state 3 appears early, it tends to precede state 1.

By asking this question for every pair of states, you can build a map of which states tend to come before or after which others.

A striking pattern emerged: the states arranged themselves into a circle. The brain wasn't just switching between networks. It was circulating through them in a consistent order.

A Cycle in Under a Second

The most remarkable finding was the speed. The full cycle, moving through all the major network configurations, takes somewhere between 300 and 1,000 milliseconds. In the time it takes you to read this sentence, your brain may have completed the entire loop several times over.

This wasn't a fluke of one dataset. The researchers found the same cyclical pattern across five independent studies, involving different participants, different laboratories, and different MEG scanners. The ordering of states around the cycle was remarkably consistent from one study to the next.

A cycle also made functional sense. States that shared similar properties clustered together along the loop.

Networks associated with high power and strong coordination between brain regions occupied one stretch of the cycle, while networks linked to attention and sensory processing clustered elsewhere.

Most strikingly, the default mode network and the dorsal attention network, which are known to have a kind of seesaw relationship (when one is active, the other tends to quiet down), sat on opposite sides of the circle.

Think of it like a preferred route through a city. If you drive around long enough, you'll take plenty of side streets and make unexpected turns. But zoom out and look at your overall movement, and you'll see that you tend to circulate through the same neighborhoods in roughly the same order.

The Cycle Doesn't Stop When You Start Thinking

An obvious question is whether this cycle is just an artifact of the resting brain, something that disappears the moment you start doing something. It doesn't.

In one dataset, participants performed a memory task that involved spontaneous replay of learned material. The cycle persisted, and intriguingly, the states associated with memory replay clustered at a specific phase of the cycle. This suggests that even internally generated mental events may be timed to particular moments in the brain's ongoing rhythm.

In another experiment, participants watched visual stimuli and pressed buttons in response. Again, the cycle continued. And behavior showed subtle but measurable ties to the cycle's dynamics. Certain network states, when active about half a second before a button press, predicted whether the response would be fast or slow. When the cycle was running more strongly in the moments before a response, reaction times tended to be faster.

The effect sizes were modest. This isn't a case where knowing someone's cycle state lets you predict their behavior with high accuracy.

The practical implication is clear: the cycle isn't just neural background music. It has real connections to what we actually do.

Individual Differences and Aging

The cycle also appears to be a stable individual trait, not just a group average phenomenon.

When the same participants were scanned on different days, their cycle characteristics, particularly the speed of the cycle, remained consistent.

Even more interesting were the findings related to age. In a dataset spanning adulthood into older age, a clear pattern emerged: older participants tended to have slower and stronger cycles. The researchers are cautious about interpretation, but the finding opens up new questions. Does cycle slowing reflect some general change in neural dynamics with age? Could it relate to the cognitive changes that often accompany aging?

The researchers also examined whether cycle characteristics run in families. Using data from twins, they applied a statistical model that separates genetic from environmental influences. The results suggested that cycle speed is substantially heritable, with genetics accounting for roughly 73% of the variation between people. Cycle strength, by contrast, didn't show the same genetic signature in this sample.

These findings didn't all replicate across every dataset, which the authors are careful to acknowledge. The cycle itself is on solid ground. The secondary findings about aging, cognition, and heritability need more work before we can draw strong conclusions.

Why a Cycle?

At a conceptual level, this study shifts how we think about brain network dynamics.

The old picture was that networks switch on and off as needed, driven by whatever task demands happen to arise. The new picture is more structured: the brain circulates through its full repertoire of networks in a consistent order, even when nothing in particular is being demanded of it.

Why would the brain organize itself this way? One appealing hypothesis is that a cycle ensures coverage. Just as sleep cycles through different stages that each serve important functions (memory consolidation, physical restoration, dreaming), a waking cycle might ensure that every major cognitive function gets periodic engagement. Attention, self-reflection, sensory processing, motor preparation: each gets its moment, over and over, hundreds of times per minute.

But the cycle isn't rigid. It allows for flexibility. Tasks can shape the timing. Deviations happen constantly. The cycle provides a scaffolding, not a straitjacket.

There's also a deeper theoretical connection here. In physics, systems at equilibrium tend to have balanced flows, with no persistent directionality. Living systems, which consume energy and stay far from equilibrium, can sustain directional processes. The brain's cyclical organization, with its consistent ordering of network states, may be a signature of this nonequilibrium nature, a reflection of the energy the brain continuously expends to maintain organized function.

What We Don't Know Yet

The study has important limitations. The method treats each network as either on or off at any given moment, when in reality networks can overlap and be partially active. The datasets were drawn from existing repositories rather than collected with prospective power calculations, which limits confidence in some of the secondary findings. And the field still lacks consensus on exactly how to define a "network" in electrophysiological data, meaning different analytical choices could carve up the brain differently.

Still, the core finding, that canonical brain networks circulate in a consistent subsecond cycle, replicated across five independent datasets. That kind of robustness is rare and suggests the phenomenon is real.

This research offers a new way to think about the brain's background activity. When your client is sitting quietly in the waiting room, or when you're between sessions and your mind wanders, the brain isn't idling. It's running through a complex, organized sequence of network activations, a hidden rhythm that may underlie our capacity to stay ready for whatever comes next.

Key Takeaways

The brain's major networks follow a hidden rhythm. Even though moment-to-moment transitions look random, the overall pattern forms a reliable cycle that repeats roughly every 300 to 1,000 milliseconds.

This finding is solid. The same cyclical pattern appeared across five separate brain imaging studies and held up whether people were resting quietly or actively performing tasks.

The cycle has meaningful structure. Networks that do similar things cluster together at the same phase of the cycle. For instance, the default mode network (linked to inward-focused thought) and attention networks (linked to outward focus) sit on opposite sides of the loop, reflecting their known push-pull relationship.

Cycle characteristics look like stable personal traits. How fast and how strongly someone's brain cycles stays consistent over time, varies predictably with age (older adults show slower and stronger cycles), and appears to be substantially influenced by genetics.

The cycle connects to real-world performance. Which phase of the cycle the brain occupies before a response, and how strongly the cycle is running at that moment, both predict how quickly someone will react. Cycle metrics also show relationships with cognitive test scores.

Glossary

cycle: a repeatable large-scale ordering of brain network states in which the system progresses through a full sequence of states in a consistent directional flow over 300–1,000 ms. cycle duration: the time required to complete one defined traversal through the major phases of the network cycle.

cycle phase: a position along the cycle that indicates when a given network state tends to occur relative to the others.

cycle rate: the inverse of cycle duration, used as a measure of how quickly the system traverses the cycle.

cycle strength: a summary measure of how strongly directional transition asymmetries support a global cyclical ordering.

default mode network (DMN): a large scale network often linked to internally oriented cognition.

dorsal attention network (DAN): a large scale network often linked to externally oriented attention and task engagement.

functional MRI (fMRI): a neuroimaging method that measures brain activity indirectly by tracking changes in blood oxygenation, which serve as a proxy for local neural activity.

hidden Markov model (HMM): a probabilistic model that infers a sequence of latent states assumed to generate the observed data.

magnetoencephalography (MEG): a noninvasive neuroimaging method that measures magnetic fields generated by neural activity, with millisecond level temporal resolution.

network: a distinct, recurring pattern of cortical activity—defined by its spatial power and coherence profile—that the hidden Markov model identifies as a functional brain state.

resting state network: a reproducible pattern of large-scale functional organization observed during quiet rest.

time delay embedded HMM (TDE-HMM): a hidden Markov model that uses time delay embedding so that inferred states are distinguished by spectral and cross spectral patterns.

TINDA: a method that assesses directional relationships between states by comparing which states occur in the early versus late halves of variable length intervals between repeated visits to a reference state.

Open-Access Reference

van Es, M. W. J., Higgins, C., Gohil, C., Quinn, A. J., Vidaurre, D., & Woolrich, M. W. (2025). Large-scale cortical functional networks are organized in structured cycles. Nature Neuroscience, 28, 2118–2128. https://doi.org/10.1038/s41593-025-02052-8

About the Author

Fred Shaffer earned his PhD in Psychology from Oklahoma State University. He earned BCIA certifications in Biofeedback and HRV Biofeedback. Fred is an Allen Fellow and Professor of Psychology at Truman State University, where has has taught for 50 years. He is a Biological Psychologist who consults and lectures in heart rate variability biofeedback, Physiological Psychology, and Psychopharmacology. Fred helped to edit Evidence-Based Practice in Biofeedback and Neurofeedback (3rd and 4th eds.) and helped to maintain BCIA's certification programs.