Model-based selection of most informative diagnostic tests and test parameters

Given the complexity of most brain and body processes, it is often not possible to relate experimental data from an individual to the underlying subject-specific physiology or pathology. Computer simulations of these processes have been suggested to assist in establishing such a relation. However, the aforementioned complexity and required simulation accuracy impose considerable challenges. To date, the best-case scenario is varying the model parameters to fit previously recorded experimental data. Confidence intervals can be given in the units of the data, but usually not for the model parameters that are the ultimate interest of the diagnosis. We propose a likelihood-based fitting procedure, operating in the model-parameter space and providing confidence intervals for the parameters under diagnosis. The procedure is capable of running parallel to the measurement, and can adaptively set test parameters to the values that are expected to provide the most diagnostic information. Using the pre-defined acceptable confidence interval, the experiment continues until the goal is reached. As an example, the approach was tested with a simplistic three-parameter auditory model and a psychoacoustic binaural tone in a noise-detection experiment. For a given number of trials, the model-based measurement steering provided 80% more information