Neuroscience and Heisenberg Uncertainty

The current sources of data for neuroscience research are naturalistic data (where there is no intervention and the subjects do not know they are being observed), and experimental data (where there is an intervention, and/or the subjects know they are being observed). Any naturalistic data may suffer from the fact that it was not specifically designed apriori to answer a particular question. Any experimental data may suffer from the Heisenberg uncertainty principle, where the intervention/observer modifies to some extent what is being observed. That holds true from induced pluripotent stem cells to testing of patients in clinical trials. What is the solution to circumvent these limitations and transform them into strengths? We believe that a convergent combination of naturalistic data with experimental data has the best yield, and should be programmatically pursued at all levels of neuroscience. For example, in developing tools to predict psychiatric disease outcomes such as suicide, a combination of naturalistic medical records and other life records mining with experimental neuropsychological and laboratory tests will yield the best outcome. Similar arguments can be made for drug development, and so on.