Alan Anticevic
Alan Anticevic
Johnson & Johnson
Innovation Center
Dr. Alan Anticevic is a clinical neuroscientist whose work focuses on developing quantitative, circuit-informed approaches to advance the understanding and treatment of severe mental illness. He completed his PhD in clinical neuropsychology and computational neuroscience at Washington University in St. Louis, training with Drs. Deanna Barch and David Van Essen, followed by a clinical neuropsychology internship at Yale University School of Medicine.
In 2022 he co-founded Manifest Technologies to translate advances in computational psychiatry and neuroimaging into commercial applications for CNS therapeutic development. His work at Manifest was recognized with the inaugural Yale Ventures Faculty Innovation Award and the Blavatnik Award for technological innovation. And in 2025 he joined Johnson & Johnson as a Senior Director within the Neuroscience therapeutic area, where he leads efforts in computational neuroimaging and translational biomarker development.
Title: From Brain Mechanisms to Personality: A Quantitative Framework for Individual Differences
Human behavior varies along dimensions, not just diagnoses. This talk will describe a quantitative clinical neuroscience framework for linking genetic influences, life experiences, latent brain mechanisms, neural signals, and observable phenotypes into a single probabilistic model. Rather than treating behavior as a set of disconnected traits or symptoms, this framework asks how continuous differences in cognition, emotion, and behavior emerge from dynamic changes in underlying brain states over time. The framework makes a crucial distinction between hidden biological mechanisms, the neural measurements we record, and the phenotypes we observe. That separation makes it possible to think about personality and individual differences in an innovative way: not as behavioral descriptors alone, but as measurable phenotypic dimensions that may reflect underlying variation in brain function. Traits such as emotional reactivity, cognitive control, resilience, and sensitivity to stress can be conceptualized as part of a broader neurobehavioral landscape spanning typical variation, vulnerability, and illness. By modeling these links probabilistically and dynamically, the approach provides a way to ask why different people follow different trajectories across development, adaptation, and disease. It also offers a foundation for testable, mechanistically grounded hypotheses about individual differences — bringing personality science, brain imaging, and clinical neuroscience into closer alignment.