Summary: A new study shows that the brain’s aging pattern in humans is not unique: mice display strikingly similar changes in their brain networks as they grow older. Using high-resolution fMRI to scan awake mice across their lifespans, researchers found that both species experience a breakdown in modular specialization—the brain’s ability to maintain distinct, efficient networks for specific tasks.
Although human brains are more highly integrated, which likely supports complex cognition, that integration may also make them more vulnerable to faster age-related decline than mouse brains. Because mice age much more quickly, this shared trajectory creates a powerful model for testing interventions—dietary, genetic, or pharmacological—in a fraction of the time required for human studies.
Key Facts
- Network breakdown: Both humans and mice show a reduction in the specialization and proper interaction of brain modules with age, a pattern linked to cognitive decline.
- The imaging challenge: Mouse brains are roughly 3,000 times smaller in volume than human brains, so researchers used MRI scanners with magnetic fields more than three times stronger than typical clinical machines to resolve fine details.
- Integration vs. vulnerability: Human brains exhibit greater between-module integration than mouse brains, which may enhance higher cognition but also appears to correlate with faster degradation of modular structure with age.
- Faster testing: Because mice span a human-equivalent lifespan of roughly 18 to 70 years in about 17 months, researchers can evaluate lifelong effects of interventions on brain aging without waiting decades.
Source: Zuckerman Institute
Study overview: Scientists at Columbia’s Zuckerman Institute and the University of Texas at Dallas scanned the brains of mice repeatedly from youth through old age and found that the way brain networks reorganize with age follows a similar pattern in mice and humans. Their results were published in Proceedings of the National Academy of Sciences.
The brain functions as an organized network of specialized modules responsible for tasks such as visual processing or face recognition. Earlier research in humans found these modules become less distinct with age, a change associated with memory loss and other cognitive impairments. The new work extends those findings by showing the same network-level aging signature appears in mice.
To investigate how aging affects network organization at the whole-brain level, researchers used functional magnetic resonance imaging (fMRI) to measure blood-flow–based activity patterns in 82 mice at multiple ages between 3 and 20 months. Because mouse brains are much smaller, the team employed high-field MRI scanners to improve spatial resolution and capture the subtle network dynamics in awake animals.
One notable technical advance in this work is imaging awake mice. Most mouse fMRI studies record under anesthesia, which alters brain activity; capturing scans from awake animals more closely models human imaging and makes cross-species comparisons more meaningful.
The results showed a clear decline in modular specialization as mice aged, matching the pattern seen in human studies. In other words, the distinct boundaries between specialized brain networks became blurred with age in both species, reflecting reduced efficiency in how brain regions coordinate for particular tasks.
At the same time, differences emerged. Mouse brain modules tended to communicate less with one another compared with human modules. Researchers propose that the higher degree of integration across human brain networks may underlie advanced cognitive abilities, but it might also make those networks more susceptible to faster breakdown over time.
“By looking at mice, we can see if, say, a change in diet in their youth affects them in old age without waiting 80 years for human results,” said study co-senior author Itamar Kahn, PhD, principal investigator at Columbia’s Zuckerman Institute and associate professor of neuroscience at Columbia’s Vagelos College of Physicians and Surgeons.
The authors emphasize that this study focused on a single laboratory mouse strain and that other strains can show different aging trajectories. Future work will explore genetic diversity, environmental influences, and interventions to determine which factors slow, halt, or reverse network-level aging.
Prior mouse studies have often focused on cellular or molecular changes that do not always translate to humans. By combining network-level imaging with cellular investigations, scientists hope to develop therapeutic strategies that are more likely to succeed in clinical trials.
Funding and authorship:
The study’s authors include Ezra Winter-Nelson, Eyal Bergmann, Micaela Y. Chan, Gabriella Vill, Liang Han, Ziwei Zhang, Alexandra Kavushansky, Irit Dolgopyat, Jad Asleh, Jennifer D. Whitesell, Itamar Kahn and Gagan S. Wig. The authors report no conflicts of interest.
Key questions answered
A: It’s about the wiring diagram. Despite differences in size, both species organize the brain into specialized modules that lose their distinctiveness with age. That pattern of reorganization is comparable across species.
Q: Why do humans appear to lose “brain power” faster than mice?
A: Humans have more integrated networks that support complex cognition. That high connectivity may also create greater vulnerability: when network components begin to fail, the system-wide decline proceeds more rapidly.
Q: Will this lead to an “anti-aging” pill soon?
A: These findings accelerate the pace of testing. Researchers can assess lifetime effects of diets, drugs, or genetic changes in mice within months to years rather than decades, providing faster, evidence-based guidance for potential human therapies.
Editorial notes
- This article was edited by a Neuroscience News editor.
- The journal paper was reviewed in full by our staff.
- Additional context was added by the editorial team.
About this research
Author: Charles Choi
Source: Zuckerman Institute
Contact: Charles Choi, Zuckerman Institute
Image credit: Neuroscience News
Original research: Findings appear in Proceedings of the National Academy of Sciences (PNAS).