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The Lake Titicaca basin was one of the major centers for cultural development in the ancient world. This lacustrine environment is unique in the high, dry Andean altiplano , and its aquatic and terrestrial resources are thought to have contributed to the florescence of complex societies in this region. Nevertheless, it remains unclear to what extent local aquatic resources, particularly fish, and the introduced crop, maize, which can be grown in regions along the lakeshores, contributed to facilitating sustained food production and population growth, which underpinned increasing social political complexity starting in the Formative Period (1400 BCE to 500 CE) and culminating with the Tiwanaku state (500 to 1100 CE). Here, we present direct dietary evidence from stable isotope analysis of human skeletal remains spanning over two millennia, together with faunal and floral reference materials, to reconstruct foodways and ecological interactions in southern Lake Titicaca over time. Bulk stable isotope analysis, coupled with compound-specific amino acid stable isotope analysis, allows better discrimination between resources consumed across aquatic and terrestrial environments. Together, this evidence demonstrates that human diets predominantly relied on C 3 plants, particularly quinoa and tubers, along with terrestrial animals, notably domestic camelids. Surprisingly, fish were not a significant source of animal protein, but a slight increase in C 4 plant consumption verifies the increasing importance of maize in the Middle Horizon. These results underscore the primary role of local terrestrial food resources in securing a nutritious diet that allowed for sustained population growth, even in the face of documented climate and political change across these periods. 

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P, however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.