Long-Term Exposure – Environmental Effects in the Range of Years to Months

Studying Associations between Features of the Physical Living Environment and Hippocampus
The Presence of Trees Around the Home Address Is Associated With Brain Structural Markers
Which Types of Nature Exposure Are Most Positively Associated With Brain Structural Markers?
Social Adversities, Isolation, and Exposure to Natural Environments Predict Psychosis
Changes in the Physical Environment and Their Association With Brain Development
The "Common Garden" of a Student Housing Facility
Associations Between Density of Urban Fabric and Cognitive Performance in Elderly People
Investigating the Relationship Between Urbanicity, Multi-tasking, and Perceptual Style
Conclusion

The physical living environment humans are exposed to for extended periods of time can have a major impact on people's lives. But to date the "active ingredients" in the environment—the pathways and the neural structures involved in this relationship—remain unclear. Many studies have focused on small sets of environmental variables. CEN aims to advance a more holistic understanding of the physical environment by treating it as the multi-layered complex phenomenon it actually is. By employing existing large-scale datasets such as the Berlin Aging Study II (BASE-II), Hamburg City Health Study (HCHS), the LIFE study from the Leipzig Research Center for Civilization Diseases, NAKO (German National Cohort study), or the UK Biobank, CEN strives to identify which environmental factors are most strongly associated with mental and brain health outcomes on an observational/correlational basis.

Studying Associations between Features of the Physical Living Environment and Hippocampus

To investigate how the multitude of factors constituting the living environment relate to brain structure, Drewelies et al. (2025) used data of 21,094 adults from the UK Biobank to examine the association between immediate characteristics of the physical environment around the individuals' home address (e.g., greenspace, levels of air pollution in the neighborhood) and the volume of the hippocampus, a brain region known to be highly prone to plasticity. A machine-learning approach based on feature importance estimation and recursive feature elimination within a random forest approach as well as linear regression analysis was utilized. Common demographic factors known to be associated with brain structure, such as sex, income, education, and age, were accounted for. The results identified a subset of environmental features—in particular air pollutants nitrogen monoxide (NO), nitrogen dioxide (NO₂), particulate matter (PM2.5), presence of greenspace, and noise exposure—to be most strongly associated with hippocampal volume across adulthood. The fact that presence of greenspace emerged as an important positively associated factor above and beyond the air pollution measures is interesting, because it highlights that these factors do not cancel each other out but rather appear to independently contribute to the overall effect on hippocampal volume.

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The Presence of Trees Around the Home Address Is Associated With Brain Structural Markers

Previous neuroimaging studies have frequently focused on environmental exposure during childhood, particularly in the first 15 years in life. In those, urban exposure during upbringing has consistently been associated with reduced prefrontal grey matter. This study, utilizing the IMAGEN dataset, investigated the current living environments of 677 European young adults. It found that higher tree cover density within 500 m of a participant's current home address correlates with increased grey matter volume in the right orbitofrontal cortex. This specific brain region was in turn positively associated with better verbal and spatial cognitive performance and negatively associated with impulsivity and alcohol-related problems. These findings suggest that current greenspace, specifically trees, may impact brain structure and mental health risk factors independently of upbringing, highlighting the potential for evidence-based urban planning to improve public mental health.

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Which Types of Nature Exposure Are Most Positively Associated With Brain Structural Markers?

To determine which specific types of nature exposure are most beneficial for brain health, Kühn et al. (2023) analyzed longitudinal data from 95 children from the prospective BIBO cohort in the Netherlands. By tracking residential exposure from birth to age 12.5 years, they observed that open greenspaces and high sky visibility (sky view factor) within 200 m of the home address during childhood were positively linked to grey matter volume in the prefrontal cortex. Conversely, and surprisingly, a measure of tree cover density, as an indicator for the number of trees present in the same areas, showed a negative association with prefrontal cortex volume. By using all environmental predictors in the same statistical model, the study concluded that sky visibility was the most critical predictor for medial prefrontal cortex volume. The results of this study suggest that enabling free views onto the sky could be relevant for designing health-focused urban environments for children. Future research is needed to determine whether sky visibility is indeed potentially responsible for many of the green- and bluespace effects reported in the literature.

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Social Adversities, Isolation, and Exposure to Natural Environments Predict Psychosis

In the field of psychiatry, it has long been discussed why psychotic experiences are more common in cities than in rural areas and which specific features of the urban environment contribute to this association. Beyer et al. (2024) addressed this question using UK Biobank data from approximately 23,000 adults, relating self-reported psychotic experiences to 26 indicators of the built environment, neighborhood context, and individual social adversity. To deal with substantial collinearity among urban exposures, the researchers employed a combination of machine learning methods, namely random forests and elastic-net logistic regression. This allowed them to derive a stable set of predictors while controlling for demographic covariates.

Across different modeling approaches, the researchers found that social adversities and isolation are the most consistent correlates of psychotic experiences, but childhood trauma, recent stressful life events, social isolation, and younger age also show robust associations. Surprisingly, conventional "urbanicity" indicators such as population density did not emerge as key predictors once these other factors were taken into account. However, a protective effect of exposure to natural environments was observed, operationalized via residential greenspace and everyday nature exposure. Lower levels of nature exposure were associated with elevated psychosis risk over and above social adversity (Figure 1).

Together, these findings suggest that urban psychosis risk is driven less by density per se and more by socially structured adversity and limited access to restorative natural environments, highlighting social integration and urban greening as promising targets for mental health prevention and intervention.

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Changes in the Physical Environment and Their Association With Brain Development

Since the goal of CEN is ultimately to unravel causal relationships, it is indispensable to investigate changes over time. However, the scarcity of longitudinal data and the difficulty of capturing the environment comprehensively means research of this type is limited. In this data analysis, Quinones et al. (2026) used structural equation modeling to quantify changes in brain structure and neighborhood-level features across adolescence within the IMAGEN data set and examined their associations using regularization-based feature selection. The team observed substantial developmental change in brain volumes, cortical thickness, and structural connectivity strength, as well as average changes in several neighborhood characteristics. Multivariate path models with Lasso regularization identified changes in a subset of neighborhood features—including tree cover density, presence of water bodies, and air pollution—as predictors of adolescents' brain structural change. These findings provide novel evidence for brain–environment coupling from a change–change perspective, suggesting that environmental dynamics play a meaningful role in shaping adolescent brain development. This work deepens the current understandings of environmental influences and underscores the value of monitoring environmental trajectories to better evaluate their relevance for neural development and ultimately, mental health.

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The "Common Garden" of a Student Housing Facility

Traditional research linking the physical environment to brain and mental health faces a fundamental confound: people typically choose where they live. Differences in outcomes can therefore be due to the environment itself or to the socioeconomic, demographic, or personal factors that led individuals there. To address this selection bias, researchers, in particular in biology, adopted the so-called "common garden" experimental design, in which they place species from different environments (e.g., rural or urban) into the same setting (usually a lab environment). In a recently complete study, the common-garden design was transferred to humans living in a student housing facility. Researchers collected intensive multimodal data of 95 students from 40 different countries living in the same residential environment in which they were randomly assigned rooms.

The core innovation of this project lies in the integration of multimodal, intensive data collection within a controlled setting. The research team combined structural and functional brain imaging with a week-long Geographic Ecological Momentary Assessment (GEMA). Participants' smartphones continuously tracked their location while prompting them for real-time self-reports on mood, stress, and activity, and a brief cognitive task assessing working memory. This created a dynamic map enabling immediate environmental exposure to be linked to psychological and cognitive states.

This research design isolates the effect of the shared current environment while accounting for individual differences in mobility and perception shaped by past life experiences. The resulting rich open dataset serves as a foundational resource for the emerging field of environmental neuroscience. It carries the promise to show how controlled, multi-method approaches can move the field beyond simple correlations, enabling the investigation of precise neural mechanisms through which specific environmental features measured in real time and real space affect human well-being. The data are currently being curated for publication.

Associations Between Density of Urban Fabric and Cognitive Performance in Elderly People

The potential impact of the physical environment on cognition has been a subject of study in environmental psychology, but findings to date have been inconclusive. While some studies have indicated a negative correlation between urbanization and cognitive ability, others have found no significant effects. Here researchers investigated associations of episodic memory, working memory, and fluid intelligence with different densities of urban fabric obtained from the European Urban Atlas in a structural equation model framework. Data of 1,053 healthy older individuals drawn from BASE-II (see overview below) were used. All participants were living within the city of Berlin. Home addresses were used to calculate the share of different land use categories, including different degrees of density of urban fabric within a 1-km radius around each household. These different urban density types were found to be significantly positively related to working memory performance and fluid intelligence. This is surprising, since the opposite, namely short-term exposures to greenspaces, have been shown to improve working memory performance directly after exposure. No significant association emerged for episodic memory. The results were robust against the inclusion of a set of covariates known to be related to cognitive performance. The findings might underline the epidemiological observation that dementia is more frequently observed in rural than in urban populations.

Overview of the Berlin Aging Study II (BASE-II) – www.base2.mpg.de
BASE-II follows a longitudinal design. At the first wave of measurements (T1), the BASE-II sample consisted of 1,600 participants aged 60 to 80 years and 600 individuals aged 20 to 35 years. Data collection of the first wave was completed in 2014. In close collaboration with Simone Kühn, then Lise Meitner Group for Environmental Neuroscience, eligible BASE-II participants (n = 445) were additionally invited for a structural magnetic resonance imaging (MRI) assessment of the brain, comprising T₁-weighted imaging, resting state data, diffusion tensor imaging, and high-resolution imaging of the hippocampus. In 2015, this MR subsample was re-invited for another wave of cognitive and psychosocial assessments and a second MRI session (n = 327). In 2017, 1,100 members of the older group from the original BASE-II sample took part in the project Sex- and Gender-Sensitive Prevention of Cardiovascular and Metabolic Disease in Older Adults in Germany (GendAge, funded by the Federal Ministry of Education and Research) and underwent most of the medical and biological assessments of T1, along with a third wave of cognitive and psychosocial assessments. In addition, accelerometers were used to track participants' physical activity and sleep for a week.
In 2021, a subset of participants (n = 104) took part in the VW Stiftung funded side study "Dynamics of Daily-Life Adaption in the Corona Crisis Among Older Adults (CorAge)" to investigate the effects of COVID-19 and of COVID-19 restrictions and provided daily data on psychosocial, cognitive, and medical variables. MR-eligible older participants were reinvited to a third wave to re-assess structural MR parameters in 2021. From 2022 to 2023, another comprehensive cognitive and medical assessment of the remaining younger (n = 218) and older BASE-II participants (n = 846) was completed. Since 2024, the Center for Environmental Neuroscience has re-invited the remaining MR-eligible older participants (n = 76) for MR scanning. Participants also completed a short behavioral session with two cognitive tasks and answered some environmental questionnaires.

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Investigating the Relationship Between Urbanicity, Multi-tasking, and Perceptual Style

Where we live may not only impact access to healthcare or the likelihood of mental illness, but may even have an impact on how we visually make sense of the world around us. Previous research, in particular on remote populations (such as the pastoralist Himba tribe in Namibia), has suggested that rural residents show a local perceptual bias and higher attentional control. CEN investigated the relationship between urbanicity, multitasking abilities, and perceptual style in an online study. Participants (N = 205) were recruited from distinct postcode areas in the UK classified as high or low in urbanicity based on the percentage of urban features present. The study observed no association between urbanicity and multitasking abilities. However, urbanicity was related to perceptual style, assessed using a global-local visual processing task. In line with previous research comparing Himba with Western urbanized populations, those UK participants living in highly urban areas showed a global bias compared to those living in less urban areas. Overall, the results suggest that the physical environment in which people live is not only associated with higher-order cognitive functions, as reported previously, but also with the way they process visual information. Importantly, this study extends evidence from cross-cultural studies into a study context in which individuals living in high versus low urbanicity regions from the same Western country were researched, thereby controlling for many potential confounds in previous cross-cultural studies.

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Conclusion

Across large, population‑based datasets, the living environment shows robust observational links with brain structure, mental health, and cognition—most prominently with hippocampal and orbitofrontal markers, psychosis risk, and cognitive performance. Key potential "active ingredients" identified by CEN from observational/correlational studies so far include specific forms of greenspaces (e.g., tree cover versus open green), sky visibility, bluespaces, as well as air and noise pollution. Their associations appear to be partly independent rather than mutually cancelling. Social adversity and isolation explain more variance in urban psychosis risk than density per se, while access to everyday nature shows protective associations. Change–change analyses in adolescence underscore that environmental dynamics—not just static exposure—track brain development. What is passed down the funnel are the prioritized potential "active ingredients" of the environment (e.g., specific greenspaces such as tree cover versus open greens and sky visibility, bluespaces, and air‑ and noise‑pollution). These elements are further probed with short‑term exposure and randomized controlled field and lab intervention studies to establish relations, identify moderators (e.g., social adversity, individual sensitivity), and define the pathways still to be unpacked at the mechanistic level.