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For many brain diseases, particularly in psychiatry, we lack clinical tests for differential diagnosis and cannot predict optimal treatment for individual patients. This presentation outlines a translational neuromodeling framework which aims at establishing “computational assays” for inferring subject-specific mechanisms of brain disease from non-invasive measures of behaviour and neuronal activity. I will show how clinical theories of maladaptive cognition and aberrant brain-body interactions can guide the construction of computational assays that are based on generative models. The clinical utility of these assays is presently evaluated in ongoing prospective patient studies that address concrete clinical problems, such as treatment response prediction, as benchmarks for model validation. If successful, computational assays may help provide a formal basis for differential diagnosis and treatment predictions in individual patients and, ultimately, facilitate the construction of mechanistically grounded disease classifications.