The tick-tock of your brain is made of exposome

The allostatic hands of your brain times
The passage of time can be conceived as a universal, mechanical, exact clock. Parmenides envisioned time as static, repeated, and unchanged, akin to a universal view of time. In contrast, Heraclitus argued that change and flux are the fundamental nature of reality. The brain clock, as conceptualized in recent research by our team, does not tick in a universal, linear fashion but instead captures the entropic, transient nature of time—how the brain ages differently depending on the environment in which it is embedded. 
The concept of the brain as an entropic clock resonates with the idea of allostasis, which refers to the process by which the body responds to environmental demands through physiological and behavioral changes. As captured by the brain clocks in a new study, the brain’s aging process reflects this allostatic nature, showing how it adapts, sometimes maladaptively, to the pressures of its environment.
Exposome and the entropic passage of time
The exposome encompasses all environmental exposures an individual experiences throughout their lifetime, including physical factors like pollution and social factors such as socioeconomic status. Our Nature Medicine study highlights how these exposomes significantly influence aging and dementia, emphasizing the importance of integrating these factors into ecological models of brain health.
We employed multimodal data from functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) to develop a brain-age gap model that accounts for the diverse exposures and conditions experienced by different populations (Figure 1). We use convolutional deep neural networks and high-order brain interactions combined with data augmentation to make the AI more robust and capture the inherent heterogeneity of diverse populations. Individuals in countries with more significant socioeconomic inequalities, particularly in Latin America and the Caribbean (LAC), tend to have older brains than their chronological age. This was most pronounced in brain regions associated with the frontoposterior networks, which are crucial for high-level cognitive functions. Structural inequalities, such as disparities in country-level income, access to healthcare, and exposure to environmental pollutants, were significant predictors of accelerated brain aging. The research also revealed sex differences in brain aging, particularly in LAC countries, where females exhibited more significant brain age gaps, especially those diagnosed with Alzheimer’s disease. This disparity was linked not only to biological sex factors but also to gender inequalities prevalent in these regions, further highlighting the relationship between the biological clocks and the social environment.

Fig. 1: Dataset characterization and analysis pipeline. Datasets included LAC and non-LAC healthy controls (HC, total n = 3,509) and participants with Alzheimer’s disease (AD, total n = 828), bvFTD (total n = 463), and MCI (total n = 517). The fMRI dataset included 2,953 participants from LAC (Argentina, Chile, Colombia, Mexico, and Peru) as well as non-LAC (the USA, China, and Japan). The EEG dataset involved 2,353 participants from Argentina, Brazil, Chile, Colombia, and Cuba (LAC), as well as Greece, Ireland, Italy, Turkey, and the UK (non-LAC). The raw fMRI and EEG signals were preprocessed by filtering and artifact removal, and the EEG signals were normalized to be projected into the source space. A parcellation using the automated anatomical labeling (AAL) atlas for the fMRI and EEG signals was performed to build the nodes from which we calculated the high-order interactions using the Ω-information metric. A connectivity matrix was obtained for both modalities, later represented by graphs. Data augmentation was performed only in the testing dataset. The graphs were used as input for a graph convolutional deep learning network (architecture shown in the last row), with separate models for EEG and fMRI. Finally, age prediction was obtained, and the performance was measured by comparing the predicted versus the chronological ages. 
Multimodal diversity and brain research
Our study pioneered the inclusion of diverse populations and the multimodal burden of exposomes. Most neuroscience studies have focused on populations from the Global North, often neglecting both the extracerebral and internal exposome conditions. This study utilized data from 5,306 participants across 15 countries, including seven LAC countries. It assessed macrosocial factors, physical exposomes, measures of structural socioeconomic inequity, and brain data to develop a more context-sensitive and inclusive model of brain aging.
The findings suggest that our brain operates like an entropic clock—one sensitive to the tear and wear inherent in its environment—rather than a predictable, mechanical clock.  This study is just one step in enhancing our understanding of how various factors influence brain aging. A framework for personalized interventions tailored to individual exposomes should be developed in the future. In such a framework, identifying those at greater risk of accelerated brain aging may help tailor interventions to their specific needs. As our understanding of the brain continues to grow, integrating the entropic nature of time and the dynamic interplay between the brain and environment will be essential for creating a better science of brain health worldwide.
For more information: Moguilner, S., Baez, S.,  et al. Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations. Nat Med (2024). https://doi.org/10.1038/s41591-024-03209-x
The poster Image was built with GPT4 under the supervision of the authors.