Month: January 2024

Statistical learning of pain signals in the brain Pain often fluctuates over time. We don’t know why, but we know that pain fluctuations strongly affect how well pain can be managed in everyday life. Recent work in our group suggests that the human brain can learn and control the temporal dynamics of pain. Our approach combines computational…

Neural dynamics of homeostatic control Homeostasis refers to the ability of an organism to adjust its internal environment to maintain a stable equilibrium. We use control theory and simulations with artificial neural networks to understand how neural populations in the midbrain contribute to homeostatic control and endogenous pain regulation. This work is relevant to chronic…

Computational methods for Neuroimaging We develop computational methods for the analyses of human neuroimaging data. For example, we have developed Fourier-based methods for the study of cortical topographic representations in humans, imaged with brain fMRI (Mancini et al. 2012; Mancini et al. 2019). Recently, we are contributing tools for the analyses of high-density diffuse optical tomography…