Disease Detection Gets Boost from Keck’s New Brain Reference Map

Investigators at the USC Mark and Mary Stevens Neuroimaging and Informatics Institute (Stevens INI) at the Keck School of Medicine say they have created one of the largest reference models ever developed for the human brain, using diffusion MRI scans from more than 54,000 people to chart how the brain’s communication pathways develop, mature, and decline across the lifespan.

Published in Nature Communications, the study “Lifespan normative modeling of brain microstructure” provides the equivalent of growth charts for the brain’s white matter, the vast network of neural wiring that allows brain regions to communicate, according to the Keck team, which adds that the novel tool offers researchers a new way to detect subtle patterns linked to aging, Alzheimer’s disease, schizophrenia risk, and other neurological and psychiatric conditions.

“Just as pediatric growth charts help clinicians determine whether a child’s height or weight is developing as expected, these brain charts provide a reference for how the brain’s neural pathways typically change over the lifespan,” said Julio E. Villalón-Reina, MD, PhD, a postdoctoral researcher at the Stevens INI and the study’s first author. “That gives us a powerful new way to identify when an individual’s brain wiring falls outside the expected range.”

To study white matter, the team used diffusion MRI, an imaging method that tracks how water moves through brain tissue. Because water movement is shaped by microscopic features such as nerve fibers and myelin, diffusion MRI can reveal subtle changes in tissue organization not visible on standard brain scans.

After compiling diffusion MRI data from 54,583 individuals across 19 international datasets, the researchers built statistical growth and decline charts for the brain’s neural pathways.

The researchers focused on four widely used measures of white matter microstructure across 21 major brain regions. By modeling how these measures vary by age and sex, they generated lifespan curves and percentile ranges that show what is typical at different stages of life.

“Brain development and brain aging are not uniform processes,” continued Villalón-Reina. “The brain’s neural pathways mature on distinct timelines, and some are more vulnerable to decline than others. Our model reveals this structure by merging data on a truly global scale.”

The scientists also discovered evidence for a longstanding theory of brain aging, sometimes described as last in, first out. According to this theory, brain pathways that develop last in childhood and adolescence tend to be more susceptible to decline in older age. The researchers observed that white matter regions that mature later did indeed decline faster in old age, offering new insight linking brain development and aging.

To demonstrate the model’s practical value, the researchers applied it to clinical datasets from people with mild cognitive impairment, dementia, and 22q11.2 deletion syndrome, a genetic condition that increases risk of schizophrenia.

In each case, the model identified alterations in the brain’s circuitry that deviated from age-expected norms. Importantly, these deviations were not identical across individuals with the same diagnosis, highlighting the value of a person-specific approach.

“This monumental study took seven years to complete,” explained Paul M. Thompson, PhD, associate director of the Stevens INI and senior author of the study. “The vast scale of the data and the fine scale of the brain features assessed means we can now evaluate your neural pathways relative to other people of the same age, sex, and demographics. We can see how your brain differs from what we would expect for a person of your age and sex, giving us a tool to use in clinical trials of treatments for dozens of brain diseases.”

When applied to people with dementia and mild cognitive impairment, the model detected atypical white matter patterns in brain regions involved in memory and interregional communication. In people with 22q11.2 deletion syndrome, it identified deviations in multiple key neural pathways, helping researchers discover which brain systems develop differently.

The reference charts may also help researchers evaluate treatments by tracking whether a person’s white matter measures move closer to the expected range, or whether a treatment slows the shift away from healthy patterns over time. The charts will now be used to compare more than 30 brain diseases and conditions, offering a common framework for studying how different disorders emerge, progress, and respond to intervention.

The models are also a publicly available resource that can be extended as additional brain imaging data become available. The methods are now being used to study neurological, psychiatric, and neurodevelopmental disorders by providing a common reference standard for white matter microstructure across the lifespan.

“This study demonstrates the power of large-scale, international data sharing to create tools the entire research community can use,” pointed out Arthur W. Toga, PhD, director of the Stevens INI and provost professor at USC. “By establishing a lifespan framework for the brain’s communication pathways, this work opens new opportunities to detect subtle disease-related changes, compare conditions more rigorously, and move toward a more individualized understanding of brain health.”

The post Disease Detection Gets Boost from Keck’s New Brain Reference Map appeared first on GEN - Genetic Engineering and Biotechnology News.