Multi-organ imaging-derived polygenic indexes for brain and body health.

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task.

Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks suboptimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives, or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus-building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy, and group cohesion, in 44 groups (n = 204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multidimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with >70% cross-validation accuracy-significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.

Social ageing can protect against infectious disease in a group-living primate.

The benefits of social living are well established, but sociality also comes with costs, including infectious disease risk. This cost-benefit ratio of sociality is expected to change across individuals' lifespans, which may drive changes in social behaviour with age. To explore this idea, we combine data from a group-living primate for which social ageing has been described with epidemiological models to show that having lower social connectedness when older can protect against the costs of a hypothetical, directly transmitted endemic pathogen. Assuming no age differences in epidemiological characteristics (susceptibility to, severity, and duration of infection), older individuals suffered lower infection costs, which was explained largely because they were less connected in their social networks than younger individuals. This benefit of 'social ageing' depended on epidemiological characteristics and was greatest when infection severity increased with age. When infection duration increased with age, social ageing was beneficial only when pathogen transmissibility was low. Older individuals benefited most from having a lower frequency of interactions (strength) and network embeddedness (closeness) and benefited less from having fewer social partners (degree). Our study provides a first examination of the epidemiology of social ageing, demonstrating the potential for pathogens to influence evolutionary dynamics of social ageing in natural populations.

Neural signatures of natural behaviour in socializing macaques.

Our understanding of the neurobiology of primate behaviour largely derives from artificial tasks in highly controlled laboratory settings, overlooking most natural behaviours that primate brains evolved to produce1-3. How primates navigate the multidimensional social relationships that structure daily life4 and shape survival and reproductive success5 remains largely unclear at the single-neuron level. Here we combine ethological analysis, computer vision and wireless recording technologies to identify neural signatures of natural behaviour in unrestrained, socially interacting pairs of rhesus macaques. Single-neuron and population activity in the prefrontal and temporal cortex robustly encoded 24 species-typical behaviours, as well as social context. Male-female partners demonstrated near-perfect reciprocity in grooming, a key behavioural mechanism supporting friendships and alliances6, and neural activity maintained a running account of these social investments. Confronted with an aggressive intruder, behavioural and neural population responses reflected empathy and were buffered by the presence of a partner. Our findings reveal a highly distributed neurophysiological ledger of social dynamics, a potential computational foundation supporting communal life in primate societies, including our own.

Mechanical and morphometric approaches to body mass estimation in rhesus macaques: A test of skeletal variables.

OBJECTIVES: Estimation of body mass from skeletal metrics can reveal important insights into the paleobiology of archeological or fossil remains. The standard approach constructs predictive equations from postcrania, but studies have questioned the reliability of traditional measures. Here, we examine several skeletal features to assess their accuracy in predicting body mass. MATERIALS AND METHODS: Antemortem mass measurements were compared with common skeletal dimensions from the same animals postmortem, using 115 rhesus macaques (male: n = 43; female: n = 72). Individuals were divided into training (n = 58) and test samples (n = 57) to build and assess Ordinary Least Squares or multivariate regressions by residual sum of squares (RSS) and AIC weights. A leave-one-out approach was implemented to formulate the best fit multivariate models, which were compared against a univariate and a previously published catarrhine body-mass estimation model. RESULTS: Femur circumference represented the best univariate model. The best model overall was composed of four variables (femur, tibia and fibula circumference and humerus length). By RSS and AICw, models built from rhesus macaque data (RSS = 26.91, AIC = -20.66) better predicted body mass than did the catarrhine model (RSS = 65.47, AIC = 20.24). CONCLUSION: Body mass in rhesus macaques is best predicted by a 4-variable equation composed of humerus length and hind limb midshaft circumferences. Comparison of models built from the macaque versus the catarrhine data highlight the importance of taxonomic specificity in predicting body mass. This paper provides a valuable dataset of combined somatic and skeletal data in a primate, which can be used to build body mass equations for fragmentary fossil evidence.

The Brain Economy: Advancing Brain Science to Better Understand the Modern Economy.

The coming years are likely to be turbulent due to a myriad of factors or polycrisis, including an escalation in climate extremes, emerging public health threats, weak productivity, increases in global economic instability and further weakening in the integrity of global democracy. These formidable challenges are not exogenous to the economy but are in some cases generated by the system itself. They can be overcome, but only with far-reaching changes to global economics. Our current socio-economic paradigm is insufficient for addressing these complex challenges, let alone sustaining human development, well-being and happiness. To support the flourishing of the global population in the age of polycrisis, we need a novel, person-centred and collective paradigm. The brain economy leverages insights from neuroscience to provide a novel way of centralising the human contribution to the economy, how the economy in turn shapes our lives and positive feedbacks between the two. The brain economy is primarily based on Brain Capital, an economic asset integrating brain health and brain skills, the social, emotional, and the diversity of cognitive brain resources of individuals and communities. People with healthy brains are essential to navigate increasingly complex systems. Policies and investments that improve brain health and hence citizens' cognitive functions and boost brain performance can increase productivity, stimulate greater creativity and economic dynamism, utilise often underdeveloped intellectual resources, afford social cohesion, and create a more resilient, adaptable and sustainability-engaged population.

Quantifying the relationship between bone and soft tissue measures within the rhesus macaques of Cayo Santiago.

OBJECTIVES: Interpretations of the primate and human fossil record often rely on the estimation of somatic dimensions from bony measures. Both somatic and skeletal variation have been used to assess how primates respond to environmental change. However, it is unclear how well skeletal variation matches and predicts soft tissue. Here, we empirically test the relationship between tissues by comparing somatic and skeletal measures using paired measures of pre- and post-mortem rhesus macaques from Cayo Santiago, Puerto Rico. MATERIALS AND METHODS: Somatic measurements were matched with skeletal dimensions from 105 rhesus macaque individuals to investigate paired signals of variation (i.e., coefficients of variation, sexual dimorphism) and bivariate codependence (reduced major axis regression) in measures of: (1) limb length; (2) joint breadth; and (3) limb circumference. Predictive models for the estimation of soft tissue dimensions from skeletons were built from Ordinary Least Squares regressions. RESULTS: Somatic and skeletal measurements showed statistically equivalent coefficients of variation and sexual dimorphism as well as high epiphyses-present ordinary least square (OLS) correlations in limb lengths (R2 >0.78, 0.82), joint breadths (R2 >0.74, 0.83) and, to a lesser extent, limb circumference (R2 >0.53, 0.68). CONCLUSION: Skeletal measurements are good substitutions for somatic values based on population signals of variation. OLS regressions indicate that skeletal correlates are highly predictive of somatic dimensions. The protocols and regression equations established here provide a basis for reliable reconstruction of somatic dimension from catarrhine fossils and validate our ability to compare or combine results of studies based on population data of either hard or soft tissue proxies.

Magnitude shifts spatial attention from left to right in rhesus monkeys as in the human mental number line.

Humans typically represent numbers and quantities along a left-to-right continuum. Early perspectives attributed number-space association to culture; however, recent evidence in newborns and animals challenges this hypothesis. We investigate whether the length of an array of dots influences spatial bias in rhesus macaques. We designed a touch-screen task that required monkeys to remember the location of a target. At test, monkeys maintained high performance with arrays of 2, 4, 6, or 10 dots, regardless of changes in the array's location, spacing, and length. Monkeys remembered better left targets with 2-dot arrays and right targets with 6- or 10-dot arrays. Replacing the 10-dot array with a long bar, yielded more accurate performance with rightward locations, consistent with an underlying left-to-right oriented magnitude code. Our study supports the hypothesis of a spatially oriented mental magnitude line common to humans and animals, countering the idea that this code arises from uniquely human cultural learning.

Attention deficits linked with proclivity to explore while foraging.

All mobile organisms forage for resources, choosing how and when to search for new opportunities by comparing current returns with the average for the environment. In humans, nomadic lifestyles favouring exploration have been associated with genetic mutations implicated in attention deficit hyperactivity disorder (ADHD), inviting the hypothesis that this condition may impact foraging decisions in the general population. Here we tested this pre-registered hypothesis by examining how human participants collected resources in an online foraging task. On every trial, participants chose either to continue to collect rewards from a depleting patch of resources or to replenish the patch. Participants also completed a well-validated ADHD self-report screening assessment at the end of sessions. Participants departed resource patches sooner when travel times between patches were shorter than when they were longer, as predicted by optimal foraging theory. Participants whose scores on the ADHD scale crossed the threshold for a positive screen departed patches significantly sooner than participants who did not meet this criterion. Participants meeting this threshold for ADHD also achieved higher reward rates than individuals who did not. Our findings suggest that ADHD attributes may confer foraging advantages in some environments and invite the possibility that this condition may reflect an adaptation favouring exploration over exploitation.

Taking identity-by-descent analysis into the wild: Estimating realized relatedness in free-ranging macaques.

Biological relatedness is a key consideration in studies of behavior, population structure, and trait evolution. Except for parent-offspring dyads, pedigrees capture relatedness imperfectly. The number and length of DNA segments that are identical-by-descent (IBD) yield the most precise estimates of relatedness. Here, we leverage novel methods for estimating locus-specific IBD from low coverage whole genome resequencing data to demonstrate the feasibility and value of resolving fine-scaled gradients of relatedness in free-living animals. Using primarily 4-6× coverage data from a rhesus macaque (Macaca mulatta) population with available long-term pedigree data, we show that we can call the number and length of IBD segments across the genome with high accuracy even at 0.5× coverage. The resulting estimates demonstrate substantial variation in genetic relatedness within kin classes, leading to overlapping distributions between kin classes. They identify cryptic genetic relatives that are not represented in the pedigree and reveal elevated recombination rates in females relative to males, which allows us to discriminate maternal and paternal kin using genotype data alone. Our findings represent a breakthrough in the ability to understand the predictors and consequences of genetic relatedness in natural populations, contributing to our understanding of a fundamental component of population structure in the wild.

Immune cell composition varies by age, sex and exposure to social adversity in free-ranging Rhesus Macaques.

Increasing age is associated with dysregulated immune function and increased inflammation-patterns that are also observed in individuals exposed to chronic social adversity. Yet we still know little about how social adversity impacts the immune system and how it might promote age-related diseases. Here, we investigated how immune cell diversity varied with age, sex and social adversity (operationalized as low social status) in free-ranging rhesus macaques. We found age-related signatures of immunosenescence, including lower proportions of CD20 + B cells, CD20 + /CD3 + ratio, and CD4 + /CD8 + T cell ratio - all signs of diminished antibody production. Age was associated with higher proportions of CD3 + /CD8 + Cytotoxic T cells, CD16 + /CD3- Natural Killer cells, CD3 + /CD4 + /CD25 + and CD3 + /CD8 + /CD25 + T cells, and CD14 + /CD16 + /HLA-DR + intermediate monocytes, and lower levels of CD14 + /CD16-/HLA-DR + classical monocytes, indicating greater amounts of inflammation and immune dysregulation. We also found a sex-dependent effect of exposure to social adversity (i.e., low social status). High-status males, relative to females, had higher CD20 + /CD3 + ratios and CD16 + /CD3 Natural Killer cell proportions, and lower proportions of CD8 + Cytotoxic T cells. Further, low-status females had higher proportions of cytotoxic T cells than high-status females, while the opposite was observed in males. High-status males had higher CD20 + /CD3 + ratios than low-status males. Together, our study identifies the strong age and sex-dependent effects of social adversity on immune cell proportions in a human-relevant primate model. Thus, these results provide novel insights into the combined effects of demography and social adversity on immunity and their potential contribution to age-related diseases in humans and other animals.

Interpersonal heart rate synchrony predicts effective information processing in a naturalistic group decision-making task.

Groups often outperform individuals in problem-solving. Nevertheless, failure to critically evaluate ideas risks sub-optimal outcomes through so-called groupthink. Prior studies have shown that people who hold shared goals, perspectives or understanding of the environment show similar patterns of brain activity, which itself can be enhanced by consensus building discussions. Whether shared arousal alone can predict collective decision-making outcomes, however, remains unknown. To address this gap, we computed interpersonal heart rate synchrony, a peripheral index of shared arousal associated with joint attention, empathic accuracy and group cohesion, in 44 groups (n=204) performing a collective decision-making task. The task required critical examination of all available information to override inferior, default options and make the right choice. Using multi-dimensional recurrence quantification analysis (MdRQA) and machine learning, we found that heart rate synchrony predicted the probability of groups reaching the correct consensus decision with greater than 70% cross-validation accuracy-significantly higher than that predicted by the duration of discussions, subjective assessment of team function or baseline heart rates alone. We propose that heart rate synchrony during group discussion provides a biomarker of interpersonal engagement that facilitates adaptive learning and effective information sharing during collective decision-making.

A single-cell multi-omic atlas spanning the adult rhesus macaque brain.

Cataloging the diverse cellular architecture of the primate brain is crucial for understanding cognition, behavior, and disease in humans. Here, we generated a brain-wide single-cell multimodal molecular atlas of the rhesus macaque brain. Together, we profiled 2.58 M transcriptomes and 1.59 M epigenomes from single nuclei sampled from 30 regions across the adult brain. Cell composition differed extensively across the brain, revealing cellular signatures of region-specific functions. We also identified 1.19 M candidate regulatory elements, many previously unidentified, allowing us to explore the landscape of cis-regulatory grammar and neurological disease risk in a cell type-specific manner. Altogether, this multi-omic atlas provides an open resource for investigating the evolution of the human brain and identifying novel targets for disease interventions.

The biology of aging in a social world: Insights from free-ranging rhesus macaques.

Social adversity can increase the age-associated risk of disease and death, yet the biological mechanisms that link social adversities to aging remain poorly understood. Long-term naturalistic studies of nonhuman animals are crucial for integrating observations of social behavior throughout an individual's life with detailed anatomical, physiological, and molecular measurements. Here, we synthesize the body of research from one such naturalistic study system, Cayo Santiago, which is home to the world's longest continuously monitored free-ranging population of rhesus macaques (Macaca mulatta). We review recent studies of age-related variation in morphology, gene regulation, microbiome composition, and immune function. We also discuss ecological and social modifiers of age-markers in this population. In particular, we summarize how a major natural disaster, Hurricane Maria, affected rhesus macaque physiology and social structure and highlight the context-dependent and domain-specific nature of aging modifiers. Finally, we conclude by providing directions for future study, on Cayo Santiago and elsewhere, that will further our understanding of aging across different domains and how social adversity modifies aging processes.

Information gathering explains decision dynamics during human and monkey reward foraging.

Foraging in humans and other animals requires a delicate balance between exploitation of current resources and exploration for new ones. The tendency to overharvest-lingering too long in depleting patches-is a routine behavioral deviation from predictions of optimal foraging theories. To characterize the computational mechanisms driving these deviations, we modeled foraging behavior using a virtual patch-leaving task with human participants and validated our findings in an analogous foraging task in two monkeys. Both humans and monkeys overharvested and stayed longer in patches with longer travel times compared to shorter ones. Critically, patch residence times in both species declined over the course of sessions, enhancing reward rates in humans. These decisions were best explained by a logistic transformation that integrated both current rewards and information about declining rewards. This parsimonious model demystifies both the occurrence and dynamics of overharvesting, highlighting the role of information gathering in foraging. Our findings provide insight into computational mechanisms shaped by ubiquitous foraging dilemmas, underscoring how behavioral modeling can reveal underlying motivations of seemingly irrational decisions.

Human OPRM1 and murine Oprm1 promoter driven viral constructs for genetic access to µ-opioidergic cell types.

With concurrent global epidemics of chronic pain and opioid use disorders, there is a critical need to identify, target and manipulate specific cell populations expressing the mu-opioid receptor (MOR). However, available tools and transgenic models for gaining long-term genetic access to MOR+ neural cell types and circuits involved in modulating pain, analgesia and addiction across species are limited. To address this, we developed a catalog of MOR promoter (MORp) based constructs packaged into adeno-associated viral vectors that drive transgene expression in MOR+ cells. MORp constructs designed from promoter regions upstream of the mouse Oprm1 gene (mMORp) were validated for transduction efficiency and selectivity in endogenous MOR+ neurons in the brain, spinal cord, and periphery of mice, with additional studies revealing robust expression in rats, shrews, and human induced pluripotent stem cell (iPSC)-derived nociceptors. The use of mMORp for in vivo fiber photometry, behavioral chemogenetics, and intersectional genetic strategies is also demonstrated. Lastly, a human designed MORp (hMORp) efficiently transduced macaque cortical OPRM1+ cells. Together, our MORp toolkit provides researchers cell type specific genetic access to target and functionally manipulate mu-opioidergic neurons across a range of vertebrate species and translational models for pain, addiction, and neuropsychiatric disorders.

Yearly variation coupled with social interactions shape the skin microbiome in free-ranging rhesus macaques.

While skin microbes are known to mediate human health and disease, there has been minimal research on the interactions between skin microbiota, social behavior, and year-to-year effects in non-human primates-important animal models for translational biomedical research. To examine these relationships, we analyzed skin microbes from 78 rhesus macaques living on Cayo Santiago Island, Puerto Rico. We considered age, sex, and social group membership, and characterized social behavior by assessing dominance rank and patterns of grooming as compared to nonsocial behaviors. To measure the effects of a shifting environment, we sampled skin microbiota (based on sequence analysis of the 16S rRNA V4 region) and assessed weather across sampling periods between 2013 and 2015. We hypothesized that, first, monkeys with similar social behavior and/or in the same social group would possess similar skin microbial composition due, in part, to physical contact, and, second, microbial diversity would differ across sampling periods. We found significant phylum-level differences between social groups in the core microbiome as well as an association between total grooming rates and alpha diversity in the complete microbiome, but no association between microbial diversity and measures of rank or other nonsocial behaviors. We also identified alpha and beta diversity differences in microbiota and differential taxa abundance across two sampling periods. Our findings indicate that social dynamics interact with yearly environmental changes to shape the skin microbiota in rhesus macaques, with potential implications for understanding the factors affecting the microbiome in humans, which share many biological and social characteristics with these animals. IMPORTANCE Primate studies are valuable for translational and evolutionary insights into the human microbiome. The majority of primate microbiome studies focus on the gut, so less is known about the factors impacting the microbes on skin and how their links affect health and behavior. Here, we probe the impact of social interactions and the yearly environmental changes on food-provisioned, free-ranging monkeys living on a small island. We expected animals that lived together and groomed each other would have more similar microbes on their skin, but surprisingly found that the external environment was a stronger influence on skin microbiome composition. These findings have implications for our understanding of the human skin microbiome, including potential manipulations to improve health and treat disease.

Best practices for genotype imputation from low-coverage sequencing data in natural populations.

Monitoring genetic diversity in wild populations is a central goal of ecological and evolutionary genetics and is critical for conservation biology. However, genetic studies of nonmodel organisms generally lack access to species-specific genotyping methods (e.g. array-based genotyping) and must instead use sequencing-based approaches. Although costs are decreasing, high-coverage whole-genome sequencing (WGS), which produces the highest confidence genotypes, remains expensive. More economical reduced representation sequencing approaches fail to capture much of the genome, which can hinder downstream inference. Low-coverage WGS combined with imputation using a high-confidence reference panel is a cost-effective alternative, but the accuracy of genotyping using low-coverage WGS and imputation in nonmodel populations is still largely uncharacterized. Here, we empirically tested the accuracy of low-coverage sequencing (0.1-10×) and imputation in two natural populations, one with a large (n = 741) reference panel, rhesus macaques (Macaca mulatta), and one with a smaller (n = 68) reference panel, gelada monkeys (Theropithecus gelada). Using samples sequenced to coverage as low as 0.5×, we could impute genotypes at >95% of the sites in the reference panel with high accuracy (median r2 ≥ 0.92). We show that low-coverage imputed genotypes can reliably calculate genetic relatedness and population structure. Based on these data, we also provide best practices and recommendations for researchers who wish to deploy this approach in other populations, with all code available on GitHub (https://github.com/mwatowich/LoCSI-for-non-model-species). Our results endorse accurate and effective genotype imputation from low-coverage sequencing, enabling the cost-effective generation of population-scale genetic datasets necessary for tackling many pressing challenges of wildlife conservation.

The art of the deal: Deciphering the endowment effect from traders' eyes.

People are often reluctant to trade, a reticence attributed to the endowment effect. The prevailing account attributes the endowment effect to valuation-related bias, manifesting as sellers valuing goods more than buyers, whereas an alternative account attributes it to response-related bias, manifesting as both buyers and sellers tending to stick to the status quo. Here, by tracking and modeling eye activity of buyers and sellers during trading, we accommodate both views within an evidence-accumulation framework. We find that valuation-related bias is indexed by asymmetric attentional allocation between buyers and sellers, whereas response-related bias is indexed by arousal-linked pupillary reactivity. A deal emerges when both buyers and sellers attend to their potential gains and dilate their pupils. Our study provides preliminary evidence for our computational framework of the dynamic processes mediating the endowment effect and identifies physiological biomarkers of deal-making.

Social responses to a hurricane drive greater epidemic risk.

The increasing frequency and intensity of extreme weather events due to climate change has the potential to alter ecosystem dynamics and wildlife health. Here we show that increasing social connections in response to a hurricane enhanced disease transmission risk for years after the event in a population of rhesus macaques. Our findings reveal that behavioural responses to natural disasters can elevate epidemic risk, thereby threatening wildlife health, population viability, and spillover to humans.