Scientists at USC have created the brain's first set of growth charts—a sweeping statistical reference tool that maps how white matter develops, peaks, and declines from childhood through old age. The work, published in Nature Communications, draws from diffusion MRI scans of 54,583 people across 19 international datasets, offering clinicians and researchers an unprecedented window into what constitutes normal brain wiring at any stage of life.
White matter—the vast network of nerve fibers that lets different brain regions talk to each other—is essential for cognition, emotion, and virtually every mental function. Until now, there has been no standardized way to know whether an individual's neural pathways are aging or developing typically or falling into dangerous territory. Pediatricians have used growth charts for decades to track children's height and weight. This research finally brings that same precision to the brain itself.
Researchers at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute used an imaging technique called diffusion MRI, which tracks how water moves through brain tissue. Because water movement is shaped by microscopic structures like nerve fibers and myelin—the protective coating around them—the scan reveals subtle shifts in tissue organization invisible on standard brain imaging. The team analyzed four widely used measures of white matter microstructure across 21 major brain regions, modeling how these change with age and sex to generate percentile ranges showing what is typical at different life stages.
The findings revealed something striking about the aging brain: development and decline follow different timelines depending on which neural pathways you're looking at. Some white matter reaches peak maturity in early adulthood while other regions mature later, in midlife. Even more intriguingly, the data confirmed a longstanding theory in neuroscience called "last in, first out"—the brain regions that develop latest in childhood and adolescence tend to be the first to decline in older age. The researchers found that white matter regions maturing later did indeed decline faster as people aged, offering fresh evidence linking how the brain develops to how it deteriorates.
Julio E. Villalón-Reina, the study's first author and a postdoctoral researcher at the Stevens INI, noted that "brain development and brain aging are not uniform processes." The new tool transforms that insight into clinical practice. To demonstrate its value, researchers applied the model to datasets from patients with mild cognitive impairment, dementia, and 22q11.2 deletion syndrome, a genetic condition that raises schizophrenia risk. In each case, the model identified alterations in brain circuitry that deviated from age-expected norms—but crucially, the deviations differed from person to person, even among those with the same diagnosis.
This seven-year undertaking means clinicians can now evaluate individual neural pathways relative to others of the same age, sex, and demographic background. Rather than relying on crude group-level diagnoses, they can see precisely how a patient's brain diverges from statistical expectation. For people facing cognitive decline, the model offers an early-detection window. For researchers studying schizophrenia, Alzheimer's, and other conditions, it provides a standardized language for describing neurological variation. The work transforms the invisible architecture of the brain into quantifiable, comparable data—a foundation for more precise neurology and psychiatry in the decades ahead.