Brain maps reveal first lifetime white matter growth charts from birth to 100

In a new study published recently in the journal Nature, researchers at Vanderbilt University and Vanderbilt Health have created the first growth charts for white matter in the brain over a human lifetime. The work brings together nearly two decades of Vanderbilt research collaborations, the university’s extensive MRI data collections, and an advanced AI-enabled computing platform.

White matter consists of bundled nerve fibers that transmit signals throughout the brain and the rest of the body. Rather than looking at the brain as a single unit, the researchers mapped 72 distinct functional “highways”—distinct white-matter pathways in the brain—and tracked how they grow and age from birth to 100 years old.

Just as height and weight charts help pediatricians monitor the growth of young children, white matter growth charts may one day be used by neurologists to track neural network development and detect abnormalities associated with brain disorders like Alzheimer’s, Parkinson’s, or epilepsy before symptoms become apparent.

“White matter is constantly changing over a human lifespan. This study—for the first time—fully characterizes white matter at each stage of brain development, bringing together research findings from the past 50 years,” said Kurt Schilling, assistant professor of radiology and radiological sciences and a senior author of the Nature study.

“This work will unlock new avenues for researchers to identify and investigate unique patterns in how the brain changes over the course of a human’s life. Applications could be used for areas ranging from autism, ADHD, dyslexia, epilepsy, and multiple sclerosis to Alzheimer’s, Parkinson’s, and more. There’s not a single neurodegenerative disease that doesn’t implicate white matter dysfunction in some way.”

Michael Kim, a Vanderbilt Ph.D. student in computer science who was the study’s lead author, added, “Defining these pathway-specific trajectories and milestones allows researchers to explore interesting neurobiological questions. They also help us investigate how white matter abnormalities present similarly or differently across diseases.”

The study distilled and analyzed MRI data from nearly 42,000 brains, comprised of more than 4 million individual images—an unprecedented data set that has been a central focal point for Bennett Landman, a key contributor to the study whose work has helped standardize and align imaging data across multiple studies.

“What’s exciting is that these data harmonization efforts—really a decade in the making that involved numerous collaborations between Vanderbilt University and Vanderbilt Health—are reaching the point of enabling transformative discovery,” he said. Landman, University Distinguished Professor in Electrical and Computer Engineering and Radiology and Radiological Sciences, also directs the Vanderbilt Lab for Immersive AI Translation (VALIANT).

Charting white matter’s milestones

To create the white matter growth charts, researchers analyzed data from diffusion MRI, or dMRI, samples across 50 population studies of typically developing brains with no known neurological or psychiatric conditions—about 35,000 after screening for abnormalities. Some of the key findings the Vanderbilt researchers uncovered:

Overall volume peaks in the early 30s: Cerebral white matter volume expands rapidly during early development, hitting its peak in a person’s early to mid 30s, before beginning a gradual decline.

Pathway integrity plateaus in the 20s: A key marker of white matter organization and integrity (fractional anisotropy) matures even earlier, rising quickly through childhood and adolescence before reaching a tipping point in the mid 20s, after which it steadily declines.

Circuitry growth and decline happens independently: Rather than following a single brain-wide schedule, the 72 distinct pathways mature and degenerate at their own rates. This means that circuits associated with different brain functions follow completely distinct timelines of growth and vulnerability.

There’s a deep link between maturation and aging: The charts revealed symmetrical patterns in how the brain’s wiring lifecycle unfolds. White matter pathways that take the longest to fully mature also tend to resist aging the longest, delaying the onset of decline. However, when looking at the speed of these changes, pathways that experience the most rapid, explosive growth during adolescence are the ones that degrade the fastest later in life.

The study did not address what happens to specific brain functions like cognition at various points of development, so how these peaks and declines affect behavior is still an open question, the researchers said. These normative measurements do, however, offer anatomical reference points for the development—and subsequent degeneration—of the brain’s white matter over time.

Comparisons of white matter development in a typical brain compared with brains with known neurological or psychiatric conditions did yield striking differences across the entire human lifespan, however.

The researchers analyzed data from dMRI scans of people with neurodevelopmental and psychiatric conditions, including autism, ADHD, schizophrenia, anxiety, and depression, as well as neurodegenerative diseases like Alzheimer’s and mild cognitive impairment. They found disorder-specific deviations in white matter growth and degeneration compared to a healthy baseline depicted in the charts. For instance, while MCI and Alzheimer’s patients showed widespread, pronounced structural degradation, other conditions exhibited unique spatial patterns of deviation.

Laurie Cutting, Patricia and Rodes Hart Professor of Neuroscience in Peabody College and a co-author on the study, said tools like the white matter growth chart could one day serve as a sensitive tool for tracking a wide array of brain health issues.

“What if you knew early on if white matter patterns deviated from what they should be in areas that we know are associated with reading abilities?” said Cutting, a specialist in dyslexia and language disorders. “You could think about early intervention to address difficulties children might encounter, such as those with dyslexia, well before they struggle with reading.”

Even for those with a typical brain anatomy, the white matter chart offers an important point of reference for overall “brain wellness,” said John Gore, University Distinguished Professor of Physics and Radiology and Radiological Sciences and a co-author of the paper.

“People talk about healthy aging,” said Gore, who in 2002 founded the Vanderbilt University Institute of Imaging Science. “This could encourage people to look at (their) brain health. We take care of our bodies by changing our diet, by exercising and by doing various things to overcome the effects of aging. What should we be doing to protect the brain?”

The study’s authors have made the white matter charts and underlying computer code publicly available, giving researchers a new way to track how the brain develops, ages, and deviates from typical patterns over time.

With more than 30 Vanderbilt-affiliated authors, this latest study published in Nature relies on neuroimaging, MRI methodologies, and computing infrastructure built by Vanderbilt and Vanderbilt Health researchers over the past two decades. Two key research strands pioneered at Vanderbilt that enabled the work on white matter characterization include transforming brain scan images into quantitative, standardized data sets and developing and validating methods for the use of diffusion MRI.

Stretching back to his graduate work at the intersection of engineering and medicine, Landman has contributed to quantitative radiology by deriving numerical markers from large sets of raw imaging data and making them comparable across studies. “At Vanderbilt, we had a tremendous opportunity to scale small studies into very large studies through coordinated multi-PI collaborations,” Landman said. “Because of this, we built systems to support consistent processing and achieved stable results over decade-long timeframes.”

Those efforts brought together expertise from across the university, including VUIIS, which specializes in MRI acquisition and analysis; the Center for Computational Imaging, which develops methods for processing and standardizing large imaging datasets; and the Advanced Computing Center for Research and Education, which provides high-performance computing to analyze them at scale.

It also drew on work by the Alzheimer’s Disease Sequencing Project and the Phenotype Harmonization Consortium, both of which bring together imaging, genomics, and clinical data to enable machine learning and other data analysis tools.

As Landman and others built systems to standardize and analyze imaging data, Schilling became a leading figure in diffusion MRI. Like dropping ink in water, dMRI tracks the direction of that movement through brain tissue, allowing researchers to map its internal wiring—white matter pathways.

Schilling, who earned his Ph.D. in biomedical engineering at Vanderbilt in 2017, began working on diffusion MRI as a postdoctoral research fellow in the VUIIS, and he joined the radiology faculty in 2020. With colleagues including Gore, Landman, and others across Vanderbilt, he pioneered the validation of advanced modeling techniques for the fibers and microstructures of the brain. Over the past decade, Schilling and his colleagues have vastly expanded the use of dMRI and established it as a gold standard for analyzing the brain’s intricate structural networks.

For Schilling and Landman, this study represents a new era of neuroimaging research, one that goes beyond single images and instead builds a trajectory of how the brain’s wiring changes and evolves at every phase of a human’s life.

“It took this huge collaborative effort—yes, within Vanderbilt, but also outside of it—to make it possible to study these changes over an entire lifetime,” Schilling said. “But now this work opens a whole world to anyone wanting to study brain development, aging, or disease and disorders. The scale of this is unlike anything we’ve ever seen.”

That collaborative environment also shaped the experience for Kim, a doctoral candidate in computer science, who said Vanderbilt’s tightly connected network of researchers made the work possible. “When I applied to Vanderbilt, one of the selling points was that all these researchers and institutes are so close to each other,” he said. “It really allows us to form strong collaborations much more easily across diverse research groups.”

One final coda: During the course of the study, Kim’s mother was diagnosed with a terminal illness. She died in December 2025. “Her diagnosis was made in part by the same kind of neuroscience imaging we’re all working on,” he said. “Knowing that I am doing the kind of science that could help someone else—even a little bit—that’s been the most important part of all this for me.”

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Lisa Lock

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Andrew Zinin

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