Michael L. Platt

The paper shows that trait anxiety leads to premature disengagement from tasks despite normal sensitivity to opportunity costs, identified through drift diffusion modeling and controlled for physiological markers.

The paper shows that age and early life adversity affect DNA methylation across tissues in macaques, with distinct but overlapping patterns.

The paper shows that anxiety and depression reduce value sensitivity during decision evidence accumulation, leading to biased decision making.

This study shows that mid-superior temporal sulcus (mSTS) neurons in macaques encode spatial context and behavioral state during natural, unconstrained exploration.

This study profiles 5.3 million cells from 55 rhesus macaques across their lifespan to map age-related changes in brain cell types and identify molecular signatures linked to aging and Alzheimer's disease.

260,000+ primate images from 60+ genera with face boxes and 48/68-point landmarks; detectors and landmark models trained on them work across primates and approach human-trained baselines on human faces.

The paper optimizes a reduced representation DNA methylation sequencing protocol for cost-effective, population-scale studies across species.

The paper examines how social relationships in monkeys influence social attention, showing individual differences in attention relate to social tendencies and dyadic interactions, with oxytocin affecting these dynamics.

The paper estimates realized relatedness in free-ranging macaques using IBD analysis from low coverage whole genome data, revealing genetic variation within kin classes and cryptic relatives.

This study shows that a hurricane increased pathogen transmission in macaques by altering social structure, doubling infection rates and reducing status-based risk differences.

This study shows that targeting cortical areas with high resting state functional connectivity to the sgACC with TMS leads to stronger sgACC responses in depressed patients.

The paper examines how reduced social connectedness in older primates can mitigate infection risks from a hypothetical pathogen, showing age-related changes in social behavior affect disease dynamics.

This study shows that rhesus macaques can predict U.S. election outcomes based on facial features, suggesting humans may similarly be influenced by evolutionary conserved visual cues in political candidates.

The paper analyzes sex-biased gene expression in rhesus macaque brains to understand its evolutionary and biomedical implications, finding similarities with human patterns and associations with neurodevelopmental disorders.

This study shows that a natural disaster altered selection on sociality in group-living primates, triggering persistent changes in their social structure.

This study shows that heart rate synchrony in group discussions predicts better decision-making outcomes in a naturalistic task.

This study models human and monkey foraging behavior to show that overharvesting is driven by information gathering, with residence times decreasing over sessions.

The paper creates a single-cell multi-omic atlas of the rhesus macaque brain, identifying cell-type-specific regulatory elements and providing insights into brain evolution and disease.

This study identifies neural signatures of natural social behaviors in rhesus macaques using wireless recordings and ethological analysis, revealing how social interactions are encoded in brain activity.

The paper uses genetic data to predict imaging-derived phenotypes from the UK Biobank, creating polygenic scores that identify disease risk in external cohorts.

The paper shows that PCC neurons track environmental information and predict choice variability during routine foraging in monkeys.

This study shows monkeys use recursive reasoning during strategic gameplay, with mSTS neurons signaling social information and cooperatively obtained rewards.

This paper reviews rhesus macaques as a model for human ageing, showing shared gene regulatory signatures in immune ageing between macaques and humans.

This study decomposes loss aversion into valuation and response biases using computational modeling and physiological biomarkers like gaze and pupil dilation.

This study uses machine learning to identify objective biomarkers of anxiety and stress using physiological and self-report data.