The study was retrospective in design, meaning the researchers looked back at imaging data that had already been collected rather than recruiting participants prospectively. The team analyzed brain MRIs from 2,826 patients, 1,094 men and 1,732 women, ranging in age from 18 to 96 years. The neuroimaging data came from consecutive inpatient and outpatient clinical MRI examinations performed at the University of Wisconsin Hospitals and Clinics and at community partner sites between January 2024 and June 2024. Importantly, patients with imaging evidence of disease were excluded from the analysis, which helps ensure that the brain differences observed were not simply the result of an existing neurological diagnosis.

To quantify neighborhood disadvantage, the researchers calculated an Area Deprivation Index, or ADI, score for each patient based on the resident’s zip code. The ADI is a census block group-level composite measure that incorporates 17 indicators spanning income, education, employment, and housing characteristics, derived from 2023 American Community Survey data. Because the index aggregates information about the socioeconomic character of an entire residential area rather than any single individual, it captures something broader than personal circumstances alone. Researchers have increasingly turned to indices like the ADI in health disparities research because they reflect accumulated structural conditions, including poverty concentration, housing instability, and limited local resources, that extend beyond individual behaviors.

The study focused on two key brain health metrics. The first was the brain age gap, the difference between a person’s chronological age and how much younger or older their brain appears based on estimates derived from MRI scans. Machine-learning approaches to brain age estimation have become a widely used tool in neuroimaging research, because a brain that appears older than its owner’s actual age is thought to reflect accumulated wear on brain tissue. The second metric was total brain volume, normalized to intracranial volume so that head size would not confound the comparison. Statistical models were controlled for age and sex, two of the strongest determinants of brain structure, to isolate the contribution of neighborhood disadvantage.

The results were striking. Individuals living in the most disadvantaged neighborhoods exhibited a higher brain age gap, meaning their brains appeared older than their chronological years, and lower total brain volume than residents of less deprived areas. We’ve long suspected that your living environment affects your brain, but until now it’s not been proven at scale in clinical populations, said John-Paul J. Yu, M.D., Ph.D., associate professor of radiology, psychiatry, biomedical engineering and biostatistics and medical informatics at the University of Wisconsin School of Medicine and Public Health, and an associate director at the Center for Health Disparities Research at the University of Wisconsin–Madison. We’ve shown very robust, measurable metrics that our living environment has an outsized impact on the brain at the molecular and cellular level.

Yu emphasized the everyday significance of the finding. We found that residents in the most disadvantaged neighborhoods had an accelerated brain age compared with people living in less deprived areas, he said. In other words, where you live leaves a measurable imprint on your brain. Elevated brain age is not merely a statistical curiosity. Previous research has linked a higher brain age gap to cardiovascular risk factors, psychiatric illness, and cognitive impairment, making it a potential early warning sign of declining neurological health long before symptoms become clinically obvious.

The MRI analysis also revealed a second, equally consequential difference: residents of the most disadvantaged neighborhoods had higher volumes of white matter hyperintensities. These are bright spots that appear on certain MRI sequences and are widely regarded as markers of small-vessel damage in the brain. White matter hyperintensities are essentially the footprints of cardiovascular damage in the brain, Yu explained. When you live with chronic stressors like high blood pressure, poor sleep or constant stress, blood vessels constrict, restricting blood flow to that part of the brain. Over time, these areas show up as these bright spots on MRI. Larger white matter hyperintensity volumes are connected to a higher risk of memory loss, cognitive problems, and dementia, which places this imaging finding squarely within the pathway linking environmental hardship to long-term neurological disease.

The biological logic behind these associations is consistent with a growing body of research on the health effects of chronic stress and deprivation. Prolonged exposure to environmental stressors is known to elevate blood pressure, disrupt sleep, and sustain inflammatory processes, all of which can damage the small blood vessels that nourish deep brain tissue. When those vessels narrow or fail, the surrounding white matter is starved of adequate perfusion and gradually shows the damage that radiologists recognize as hyperintensities. At the same time, sustained adversity may accelerate the loss of brain tissue volume that normally accompanies aging. The new study suggests that these processes are not evenly distributed across the population but instead track with the socioeconomic character of the places where people live.

Yu said the findings suggest that data derived from MRI may serve as intermediate imaging biomarkers of structural inequity, with relevance for both population neuroscience and clinical risk stratification. In practical terms, that means radiologists and clinicians could one day use routinely acquired brain scans, combined with neighborhood-level indices such as the ADI, to identify individuals and communities at elevated risk of cognitive decline before disease manifests. We have shown for the first time in a real-world clinical population that an individual’s environment has clear, quantifiable implications for brain age, and, by proxy, brain health, he said. The conditions in which individuals are born, live, work and age, profoundly shape long-term neurological health outcomes.

The implications extend well beyond the radiology suite. Neighborhood disadvantage, quantified by indices such as the ADI, is an important determinant because it reflects accumulated structural inequities extending beyond individual behaviors that contribute to disparities in clinical outcomes, Yu noted. It’s important to factor in this data when planning health intervention programs, crafting public policy, and identifying geographically defined areas that need improved access to care. As an emerging area of research with critical implications for basic science, public health, and clinical practice, the work points toward a future in which brain imaging not only diagnoses disease but also illuminates the social conditions that quietly shape it. The study, titled Evaluating the Effect of Neighborhood-level Disadvantage on Brain Health: An Imaging Epidemiology Study, was published on September 15, 2026, with collaborators including Ethan H. Willbrand, Elizabeth M. Stoeckl, Daryn Belden, Sheena Y. Chu, Eleanna M. Melcher, Daniil Zhitnitskii, Elena Bonke, Jussi Mattila, Usman Iftikhar, Juha Koikkalainen, Antti Tolonen, Jyrki Lötjönen, and Richard Bruce.

Subject of Research: The association between neighborhood-level socioeconomic disadvantage and brain health measured by MRI

Article Title: Where you live may influence your brain health

Article References: Where you live may influence your brain health. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: brain health, neighborhood disadvantage, Area Deprivation Index, brain age gap, MRI, white matter hyperintensities, total brain volume, health disparities, Radiology, dementia risk, public health, neuroimaging