[Cancelled] Translational neuromodeling
from a variety of underlying mechanisms. So far, we lack diagnostic tests for non-invasive identification of subject-specific pathophysiological pathways. As a consequence, we are presently neither able to obtain mechanistically interpretable diagnoses for individual patients nor to make principled predictions about individualized treatment. In this talk, I discuss how neurocomputational models could help to address this critical problem for psychiatry and neurology. In this translational neuromodeling framework physiologically interpretable dynamic system models are combined with computational (Bayesian) models that are fitted to neuroimaging and behavioral data in order to provide estimates of pathophysiological mechanisms at the synaptic and circuit level. Subsequently, such model-based quantitative characterizations of “hidden” neuronal disease mechanisms can be exploited by machine learning techniques (e.g., Bayesian model selection and generative embedding) to generate probabilistic predictions about clinical outcome and treatment responses in individual patients. This presentation outlines the theoretical foundations of this framework and illustrates its potential by preliminary examples from pharmacological and clinical studies.