The study included 4,606 participants with high-quality structural MRI and cognitive data at baseline. They were members of an Icelandic cohort established to investigate risk factors for disease and disability in aging. Their mean age was 76.4 years, and 58% were women.

A subset of 2,561 participants returned for follow-up cognitive testing after a mean interval of 5.2 years. Researchers estimated brain age from T1-weighted MRI using an automated whole-brain analysis pipeline, then subtracted each participant’s chronological age from the estimate. A positive value meant that the brain appeared older than the participant, while a negative value meant that it appeared younger.

The longitudinal analysis therefore asked whether an MRI-based measure of brain aging could provide information about later cognitive change beyond calendar age alone. At baseline, each additional year of advanced brain age was associated with lower standardized memory scores by a model coefficient of −0.047, lower processing-speed scores by −0.044 and lower executive-function scores by −0.037. All three associations were statistically significant, with p values below 0.001.

The models accounted for chronological age, sex and years of education. Researchers also generated residualized cognitive scores, removing the modeled effects of those covariates before examining the relationship between the remaining score variation and BAG. The baseline BAG values ranged from −18.6 to 11.3 years, with a mean of −2.0 years and a standard deviation of 4.3 years.

BAG was negatively correlated with chronological age, with r = −0.598 and p below 0.001, making adjustment for age important in interpreting the results. The mean BAG was slightly higher among participants who returned for follow-up testing than among those who did not, −1.7 years versus −2.0 years, a difference that reached statistical significance. Because the longitudinal sample was therefore not necessarily representative of everyone included at baseline, the follow-up findings should be understood as applying to participants who were assessed twice rather than automatically to the entire cohort.

Among the 2,561 participants assessed twice, average cognition declined in all three domains. The mean change was −0.237 standard deviations for memory, −0.318 for processing speed and −0.232 for executive function, with all changes statistically significant at p below 0.001. Mixed-effects models were then used to test whether baseline BAG predicted change over time.

These models included fixed effects for BAG, assessment time and their interaction, as well as chronological age, sex, education and the number of days between assessments. Subject-specific random effects accounted for correlation between each participant’s baseline and follow-up scores. This design allowed the researchers to distinguish a BAG-related difference in cognitive level from a BAG-related difference in the rate of decline.

For memory, the analysis found both a significant main effect of BAG and a significant interaction between BAG and assessment time. The main effect, with a coefficient of −0.039, indicated poorer memory among participants with older-estimated brains at both time points. The interaction coefficient was −0.0071, with a 95% confidence interval from −0.0142 to −0.0001 and p = 0.046.

This indicated that higher baseline BAG was associated with a steeper decline in memory over follow-up, independently of the measured demographic and timing factors. The result supports the use of BAG as a prospective marker of memory vulnerability in this cohort, although it does not establish that brain aging causes the observed change. Processing speed and executive function showed a different pattern.

A larger BAG remained associated with lower performance in both domains across the study period: the longitudinal main-effect coefficients were −0.044 for processing speed and −0.028 for executive function, with p values below 0.001. However, neither BAG-by-time interaction was statistically significant. The interaction was −0.0026 for processing speed, with a 95% confidence interval of −0.0086 to 0.0031 and p = 0.402, and −0.0051 for executive function, with a 95% confidence interval of −0.0120 to 0.0019 and p = 0.156.

Thus, the primary models supported a BAG-related difference in the level of performance, but did not establish that BAG significantly changed the rate of decline in either domain. Secondary post hoc analyses divided participants into five BAG groups: ≤ −10 years, −10 to ≤ −5 years, −5 to ≤ 0 years, 0 to ≤ 5 years and 5 to ≤ 10 years. The groups with positive BAG values showed steeper declines in memory and executive function than the groups with negative or zero BAG values.

Pairwise comparisons were significant for memory, with adjusted p values from 0.001 to 0.022, and ranged from 0.001 to 0.051 for executive function. The highest group therefore represented brains estimated to be 5 to 10 years older than chronological age, not a category beginning only above five years. Although the higher-BAG groups also showed steeper absolute declines in processing speed, the analysis did not identify additional group differences in that domain at follow-up relative to baseline.

These subgroup findings were exploratory and depended on dividing a continuous brain-age measure into categories. The study offers prospective evidence that MRI-estimated brain age contains information about cognitive vulnerability beyond a person’s calendar age, but several limitations restrict how far the findings can be generalized. BAG was a global measure and could not identify which brain regions were responsible for particular cognitive outcomes.

Although participants were considered healthy, subtle prodromal pathology could not be completely excluded. The models did not assess interactions with other established risk factors for cognitive decline, including diabetes and smoking, and the analysis used structural MRI alone rather than diffusion imaging, functional MRI or other modalities. The relatively short follow-up period also limits interpretation of trajectories, while the relatively preserved cognitive functioning of the cohort may have reduced the ability to detect subtle differences in processing-speed decline.

Moreover, the cohort was relatively homogeneous and geographically localized to Iceland, so validation in more ethnically and geographically diverse populations would be needed before the measure could be treated as a broadly established clinical biomarker. For now, the results identify brain age gap as a possible non-invasive marker of vulnerability to memory decline among older adults, rather than as a diagnostic test or a demonstrated method for predicting dementia. The authors propose that future studies should examine longer follow-up, conversion to mild cognitive impairment or dementia, and whether interventions targeting physical activity, vascular health or cognitive training can alter brain-age trajectories.

Those questions remain open: the present analysis shows associations between older-estimated brains and cognitive outcomes, not that changing brain age would prevent cognitive decline. Its strongest longitudinal finding is that a larger BAG predicted steeper memory loss over roughly five years, while evidence for domain-specific changes in processing speed and executive function was less definitive.

Subject of Research: MRI-estimated brain age and five-year cognitive decline in healthy older adults

Article Title: Advanced brain age and longitudinal cognitive decline in healthy older adults

Article References: Kristinsson, S., Newman-Norlund, R., Bonilha, L., Bakos, L., Rorden, C., Sigurdsson, S., Gudnason, V., & Fridriksson, J. (2026). Advanced brain age and longitudinal cognitive decline in healthy older adults. GeroScience. https://doi.org/10.1007/s11357-026-02598-w

Image Credits: AI Generated

DOI: 10.1007/s11357-026-02598-w

Keywords: brain age gap, MRI, healthy aging, cognitive decline, memory, executive function, processing speed, Reykjavik Study, neuroimaging, longitudinal cohort, Advanced, brain

The concept at the heart of the study is the brain age gap, often abbreviated BAG. Neuroimaging researchers build machine-learning models that learn the typical structural and functional signatures of a brain at any given age, then apply those models to individual scans. If a ten-year-old’s brain looks like the average brain of a twelve-year-old, the difference between predicted and chronological age yields a positive gap, which in this framework reflects deviations from normative brain maturation and reduced potential for brain plasticity. A smaller or negative gap suggests that brain development is tracking along, or ahead of, the expected trajectory—a sign of promoted plasticity during the remarkably dynamic adolescent period.

To capture that gap across multiple dimensions of brain organization, the team, led by Yumeng Yang, Ran Liu, and Liang Luo of Beijing Normal University with colleagues at the University of Toronto and the University of Florida, drew on four complementary MRI modalities from the ABCD Study. Structural anatomy came from T1-weighted scans in 7,330 children, 46.9 percent of them female and 55.2 percent White. White-matter microstructure was assessed with diffusion tensor imaging in 6,963 participants, spontaneous neural activity with resting-state functional MRI in 6,482, and the researchers also computed a combined multimodal estimate using 6,334 children with overlapping data. For each modality, they trained XGBoost regression models—gradient-boosted decision-tree ensembles prized for their performance on tabular neuroimaging features—to predict chronological age from brain measurements, then calculated the difference between predicted and actual age for every child.

With four independent estimates of brain age in hand, the researchers turned to the social determinants of health: the economic, environmental, and social conditions that shape wellbeing long before clinical disease appears. Using Bayesian multilevel models, which quantify evidence on a continuous scale and borrow strength across nested data structures such as children within sites, they examined how these social factors related to brain age gaps. The pattern was strikingly directional. Positive social determinants—lower exposure to industrial pollutants, higher educational quality, and socioeconomic advantage—were associated with smaller brain age gaps, consistent with healthier, more plastic brain maturation. Negative determinants, including the density of early childhood education centers, local unemployment rates, and measured sexism and racism, were associated with larger gaps, signaling deviations from typical developmental trajectories.

The most consequential question was mechanistic: how do circumstances outside the body become biology inside it? The researchers’ answer centered on allostatic load, the cumulative physiological wear and tear that accumulates when the body repeatedly mobilizes stress responses. Often indexed through cardiovascular, metabolic, and inflammatory markers—signals such as blood pressure, lipid profiles, and interleukin-6—allostatic load represents the body’s running tab for chronic adaptation to adversity. In mediation analyses, which test whether a third variable statistically carries the effect of one factor onto another, allostatic load mediated the associations between social determinants of health and brain age gaps. In other words, harsher social environments appeared to enlarge the gap between brain age and chronological age in part by raising the body’s cumulative stress burden, while supportive environments preserved smaller gaps by keeping that burden low.

The study also probed whether lifestyle factors could modify these pathways, and one interaction stood out. Adequate nutritional intake amplified the beneficial effect of regional educational attainment on brain plasticity, acting through reduced allostatic load. That finding carries a practical edge: the neural payoff of living in an educated, resource-rich region was stronger for adolescents who were well nourished, suggesting that nutrition is not merely one more desirable factor but a potential lever that magnifies the value of other social investments. For policymakers, the implication is that combining educational enrichment with food security may yield more than the sum of its parts.

Because the ABCD Study follows children longitudinally, the team could ask whether brain age gaps forecast future mental health rather than merely co-occurring with it. Examining psychiatric outcomes measured two years after the imaging assessments, they found a selective prediction: only the brain age gap estimated from resting-state functional MRI—that is, from the intrinsic patterns of spontaneous brain activity—predicted increased depression two years later. Gaps computed from structure alone, from white-matter diffusion, or from the multimodal combination did not carry the same predictive power. The result hints that the functional organization of the developing brain, the way regions talk to each other at rest, may be the modality most sensitive to social embedding and most informative about vulnerability to adolescent depression.

Methodologically, the paper leans on tools that have become standard in modern biobank-scale neuroscience. XGBoost models were used for age prediction because they handle the high-dimensional, non-linear relationships typical of neuroimaging features while remaining robust to large samples. Shapley Additive Explanations, or SHAP values, offer a way to trace which input features drove each prediction, injecting interpretability into otherwise opaque machine-learning pipelines. On the statistics side, confirmatory and exploratory factor analyses helped distill many individual social and biological variables into coherent constructs, while Bayes Factors allowed the authors to weigh evidence for and against associations rather than relying solely on binary significance thresholds, and average causal mediation effects with confidence intervals quantified the pathway from social conditions through allostatic load to the brain.

The scale and demographics of the analytic samples matter for the credibility of the conclusions. Roughly 7,000 children across four MRI modalities, recruited across the United States and tracked by the ABCD consortium—a decade-long effort supported by the National Institutes of Health—provide statistical power that smaller single-site studies cannot match, and the Bayesian multilevel framework accounts for the clustering of participants across recruitment sites. All procedures were approved by the central Institutional Review Board at the University of California, San Diego, and by local boards at each site, with informed consent and assent obtained from participants and guardians. The research was funded by national science programs in China, the University of Toronto Connaught Fund, and Canadian social sciences funding, alongside NIH grants supporting the underlying data infrastructure.

The authors’ conclusions point toward intervention as much as description. By identifying allostatic load as the conduit through which social adversity reshapes adolescent brain maturation, the study reframes a biological marker long studied in adult medicine as a modifiable target in childhood. Social conditions such as pollution, unemployment, discrimination, and educational quality are not fixed facts of life; they are policy variables. If chronic stress is the transmission channel, then reducing that stress—through environmental cleanup, economic support, anti-discrimination efforts, and nutrition programs—may slow or prevent the premature neural aging that otherwise precedes depression and other mental health problems. For a field searching for early warning signs of adolescent psychiatric illness, a measure computable from a single MRI scan, sensitive to the social environment, and predictive of depression two years on is a genuinely promising lead. What remains to be established is whether interventions that lower allostatic load in childhood actually narrow the brain age gap over time—a question the continuing ABCD cohort, now following its participants into early adulthood, is well positioned to answer.

Subject of Research: Associations of social determinants of health and allostatic load with brain age gaps in adolescent neurodevelopment

Article Title: Associations of social determinants of health, allostatic load, and brain age gaps in adolescence

Article References: Yang, Y., Liu, J., Yu, L., Li, H., Kong, T., Ji, F., Zhang, X., Gong, G., Liu, R., & Luo, L. (2026). Associations of social determinants of health, allostatic load, and brain age gaps in adolescence. BMC Medicine. https://doi.org/10.1186/s12916-026-05193-0

Image Credits: AI Generated

DOI: 10.1186/s12916-026-05193-0

Keywords: social determinants of health, brain age gap, allostatic load, adolescence, ABCD Study, MRI, machine learning, XGBoost, depression, brain plasticity, nutritional intake, Bayesian multilevel models

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