Proponents of personalized medicine have promoted neuroimaging evaluation and treatment of major depressive disorder in three areas of clinical application: clinical prediction, outcome evaluation, and neurofeedback. Whereas psychometric considerations such as test-retest reliability are basic precursors to clinical adoption for most clinical instruments, they are often not considered for neuroimaging assessments. As an example, we consider functional magnetic resonance imaging (fMRI) of depression, a common and particularly well-validated mechanistic technology for understanding disorder and guiding treatment. In this article, we review work on test-retest reliability for depression fMRI studies. We find that basic psychometrics have not been regularly attended to in this domain. For instance, no fMRI neurofeedback study has included measures of test-retest reliability despite the implicit assumption that brain signals are stable enough to train. We consider several factors that could be useful to aid clinical translation including 1) attending to how the BOLD response is parameterized, 2) identifying and promoting regions or voxels with stronger psychometric properties 3) accounting for within-individual changes (e.g., in symptomatology) across time and 4) focusing on tasks and clinical populations that are relevant for the intended clinical application. We apply these principles to published prognostic and neurofeedback data sets. The broad implication of this work is that attention to psychometrics is important for clinical adoption of mechanistic assessment, is feasible, and may improve the underlying science.
bioRxiv Subject Collection: Neuroscience