By the time animals reach middle age, their daily habits can provide clues about how long they can live.
This conclusion comes from a new study supported by the Knight Initiative for Brain Resilience at Stanford University’s Wu Tsai Neuroscience Institute. Researchers observed dozens of short-lived fish continuously throughout their lives to better understand how behavior relates to aging.
Even though these fish shared similar genetics and lived in the same controlled environment, they aged very differently. By early adulthood, those differences were already visible in the way they swam and rested. These patterns were strong enough to predict whether the fish’s lifespans would ultimately become shorter or longer.
Although the study focused on fish, the findings suggest that tracking subtle daily behaviors such as movement and sleep, which are currently commonly recorded by wearable devices, may provide insight into how aging progresses in humans.
This research science March 12, 2026, led by Wu Tsai Neuro Postdoctoral Fellows Claire Bedbrook and Ravi Nath. The study grew out of a Knight Initiative-supported collaboration between the Stanford labs of geneticist Anne Brunet and bioengineer Carl Deiseroth, the study’s senior author.
Track aging in real time
Most aging studies compare young and old animals. Although useful, this approach can miss how aging progresses within individuals over time and how differences between individuals develop.
Bedbrook and Nass wanted to track aging continuously throughout the lifespan. Even animals raised under similar conditions can differ greatly in how they age and how long they live. The researchers aimed to determine whether natural behavior could reveal when that difference begins.
To do this, they used African turquoise killifish, which have a lifespan of only 4 to 8 months. Despite their short lifespans, they share important biological characteristics with humans, including complex brains, making them valuable models for aging research.
The Bluenet Laboratory has played a leading role in establishing the medaka fish as a model organism. This study was the first to track individual vertebrates continuously, day and night, throughout their adult lives.
The researchers designed an automated system in which each fish lives in its own tank under constant camera surveillance. Similar to a real-life version of The Truman Show, this setting recorded every moment of each animal’s life. In total, the team tracked 81 fish and collected billions of video frames.
From this huge data set, they analyzed posture, speed, rest, and movement. They identified 100 different ‘action syllables’ – short, repeated movements that form the basic elements of how fish move and rest.
“Behavior is an amazingly integrated piece of information that reflects what’s going on throughout the brain and body,” says Brunet, the Michelle and Timothy Baracket Professor of Genetics at Stanford School of Medicine. “Molecular markers are essential, but they only capture part of the biology. Behavior allows us to see the whole organism continuously and non-invasively.”
With this detailed record, researchers began to ask new questions. “When do people age differently?” What are the early characteristics that define those paths? And can behavior alone predict lifespan?
Early behavioral signals of longevity
One of the most impressive findings was how early aging pathways begin to diverge. After tracking each fish throughout its life, the researchers grouped the fish by lifespan and looked back to determine when behavioral differences first appeared. The researchers found that by early middle age (70 to 100 days after birth), fish that later become long-lived and those that become short-lived are already behaving differently.
Sleep patterns stand out as an important factor. Eventually, fish whose lifespans were shortened began to tend to sleep not only at night but also during the day. In contrast, long-lived fish slept most of the night.
Activity level also played a role. Long-lived orbital fish swam more actively and reached higher speeds as they moved through the tank. They were also more active during the day. This kind of spontaneous movement is associated with longevity in other species as well.
Importantly, these behavioral differences are predictive, not merely descriptive. Using a machine learning model, the researchers showed that just a few days of behavioral data in middle-aged fish is enough to estimate lifespan. “Changes in behavior very early in life can tell us about future health and future longevity,” Bedbrook says.
Aging occurs in different stages
The study also revealed that aging is not a slow and steady process. Instead, most fish experienced two to six rapid behavioral changes, with each change lasting only a few days. These transitions were followed by several weeks of stable conditions. Fish typically move through these stages in sequence rather than switching back and forth.
“We expected aging to be a slow, gradual process,” Bedbrook says. “Instead, animals remain stable for long periods of time and then quickly transition to new stages. Seeing this graded structure emerge from just continuous behavior was one of the most exciting discoveries.”
This gradual pattern is consistent with results from human studies that suggest that molecular changes in aging occur in waves, particularly in midlife and late life. The medaka results provide a behavioral perspective on this phenomenon.
Researchers propose that aging may involve long periods of relative stability punctuated by short periods of rapid change. They liken it to a Jenga tower. In Jenga Tower, many blocks are removed with little effect until one key change causes a sudden change.
To investigate the biology behind these patterns, the researchers examined gene activity in eight organs at stages where behavior can reliably predict lifespan. Rather than focusing on single genes, they focused on coordinated changes across groups of genes involved in shared processes.
The most noticeable difference appeared in the liver. Genes related to protein production and cell maintenance were more active in fish with shorter lifespans. This suggests that as aging progresses, internal biological changes occur along with behavioral differences.
Behavior is the beginning of aging.
“It turns out that behavior is a very sensitive predictor of aging,” says Nass. “If you look at two animals of the same chronological age, you can see that they age very differently just by looking at their behavior.”
This sensitivity is evident in many aspects of daily life, especially sleep. In humans, sleep quality and sleep-wake cycles often decline with age, and these changes are associated with cognitive decline and neurodegenerative diseases. Nass will investigate whether improving sleep can support healthier aging and whether early intervention can change the trajectory of aging.
The researchers also plan to investigate whether the aging pathway can be altered by targeted strategies, such as dietary changes or genetic interventions that can influence the rate of aging.
For Bedbrook, the findings raise broader questions about what facilitates transitions between stages of aging and whether they can be slowed or reversed. She is also interested in moving to more natural environments, where animals can socialize and experience more realistic situations.
“We now have the tools to continuously map aging in vertebrates,” she says. “With the rise of wearable devices and long-term tracking of humans, we are excited to see if the same principles – early predictors, gradual aging, divergent trajectories – apply to humans.”
Another important area of research involves the brain. Deiseroth’s lab is developing tools to continuously monitor neural activity over long periods of time. This could reveal how changes in the brain coincide with, or may influence the pace of, aging in other parts of the body.
Building on the tools and insights developed at Stanford, Bedbrook and Nass will continue this work by opening their own lab at Princeton University in July of this year.
Ultimately, this research aims to explain why aging is so different and discover new ways to support healthier, longer lives.
Publication details research team
The study’s authors were Claire Bedbrook of the Stanford School of Medicine’s Department of Bioengineering and the Stanford School of Engineering. Ravi Nath of Stanford University’s Department of Genetics. Libby Chan, Stanford Engineering, Department of Electrical Engineering, Stanford University; Scott Linderman of the Stanford Humanities Statistics Department, the Knight Initiative for Brain Resilience, and the Wu Tsai Neuroscience Institute. Anne Brunet of Stanford University’s Department of Genetics, Wu Tsai Neuroscience Institute, Knight Initiative for Brain Resilience, and Glenn Center for the Biology of Aging; and Dr. Carl Deiseroth and Professor Chen of the Department of Bioengineering at Stanford School of Medicine and Stanford School of Engineering, the Department of Psychiatry and Behavioral Sciences at Stanford School of Medicine, the Knight Initiative for Brain Resilience, and the Howard Hughes Medical Institute at Stanford University.
Research support
This research was supported by the National Institutes of Health (R01AG063418 and K99AG07687901), the Knight Initiative for the Brain Resilience Catalyst Award and the Brain Resilience Scholar Award, the Keck Foundation, the ARIA Foundation, the Glenn Medical Research Foundation, the Simons Foundation, the Chan Zuckerberg Biohub – San Francisco, and NOMIS. Distinguished Scientist and Scholar Award, Helen Hay Whitney Foundation, Wu Tsai Neuroscience Institute Interdisciplinary Scholar Award, Iqbal Farooq and Asad Jamal Center for Aging, Cognitive Health.
competing interests
Karl Dieseroth is a co-founder and scientific advisory board member of Stellaromics and Maplight Therapeutics, and advises RedTree and Modulight.bio. Ann Brunet is a member of Calico’s Scientific Advisory Board. All other authors declare no conflicts of interest.
